Details of expert systems for process sequence and planning design in sheet forming
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n\n\n\n\n'}],latestNews:[{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"6910",leadTitle:null,fullTitle:"Bacteriophages - Perspectives and Future",title:"Bacteriophages",subtitle:"Perspectives and Future",reviewType:"peer-reviewed",abstract:"Bacteriophages are viruses that utilise bacterial cells as factories for their own propagation and as safe havens for their genomic material. They are capable of equipping bacteria with properties that bestow environmental advantages. They are also capable of specifically and efficiently killing bacteria.Bacteriophages are resilient in a wide diversity of environments, presumed to be as ancient as life itself, and are estimated to be the most numerous biological entities on the planet. Their overarching capacity to survive via molecular adaptation is supported by an arsenal of encoded enzymatic tools, which also enabled biotechnology. This volume includes contributions that describe bacteriophages as nanomachines, genetic engineers, and also as medicines and technologies of the future, including relevant production and process issues.",isbn:"978-1-83880-446-6",printIsbn:"978-1-83880-438-1",pdfIsbn:"978-1-83880-447-3",doi:"10.5772/intechopen.73439",price:119,priceEur:129,priceUsd:155,slug:"bacteriophages-perspectives-and-future",numberOfPages:142,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"7f28b4e1886882252219cac01e75b69c",bookSignature:"Renos Savva",publishedDate:"February 19th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/6910.jpg",numberOfDownloads:8045,numberOfWosCitations:1,numberOfCrossrefCitations:5,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:18,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:24,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 10th 2018",dateEndSecondStepPublish:"May 1st 2018",dateEndThirdStepPublish:"June 30th 2018",dateEndFourthStepPublish:"September 18th 2018",dateEndFifthStepPublish:"November 17th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"252160",title:"Dr.",name:"Renos",middleName:null,surname:"Savva",slug:"renos-savva",fullName:"Renos Savva",profilePictureURL:"https://mts.intechopen.com/storage/users/252160/images/system/252160.png",biography:"Renos Savva is a Senior Lecturer at Birkbeck, University of London. His research interests are in the interactions of viruses with their cellular host environments, particularly those of viral proteins with nucleic acids, and with host-encoded proteins. Dr. Savva’s research concerns the nature of viral survival mechanisms, such as viral replicative switches, evasion of host restriction factors, and the adaptation and sequence plasticity of virus-encoded proteins. Dr. Savva’s published research includes insights from the structural biology of phage-encoded inhibitors of the ubiquitous family-1 uracil-DNA glycosylase, Ung. His research also extends to questions of protein sequence plasticity, which crosses over into synthetic biology collaborations: On the one hand, how adaptable are essential phage proteins, and on the other, how interchangeable are structural components of phage from closely related genomic families.",institutionString:"Birkbeck, University of London",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Birkbeck, University of London",institutionURL:null,country:{name:"United Kingdom"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"427",title:"Virology",slug:"biochemistry-genetics-and-molecular-biology-microbiology-virology"}],chapters:[{id:"70386",title:"Introductory Chapter: Nature’s Ancient Nanomachines and Their Synthetic Future",doi:"10.5772/intechopen.90384",slug:"introductory-chapter-nature-s-ancient-nanomachines-and-their-synthetic-future",totalDownloads:649,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Renos Savva",downloadPdfUrl:"/chapter/pdf-download/70386",previewPdfUrl:"/chapter/pdf-preview/70386",authors:[{id:"252160",title:"Dr.",name:"Renos",surname:"Savva",slug:"renos-savva",fullName:"Renos Savva"}],corrections:null},{id:"66740",title:"Bacteriophages: Their Structural Organisation and Function",doi:"10.5772/intechopen.85484",slug:"bacteriophages-their-structural-organisation-and-function",totalDownloads:1948,totalCrossrefCites:4,totalDimensionsCites:10,hasAltmetrics:0,abstract:"Viruses are infectious particles that exist in a huge variety of forms and infect practically all living systems: animals, plants, insects and bacteria. Viruses that infect and use bacterial resources are classified as bacteriophages (or phages) and represent the most abundant life form on Earth. A phage can be described as a specific type of nano-machine that is able to recognise its environment, find a host cell, start infection, self-assemble and safeguard its genome until the next cycle of replication is initiated. Remarkable results have been obtained by combining cryo-EM, X-ray analysis and bioinformatics in structural studies of these nano-machines. In this review we will describe results of structural studies of phages that uncover their organisation in different conformations, thus facilitating our understanding of the functional mechanisms in supramolecular assemblies and helping us understand the usage of phages in medical treatments. Currently, antibiotic resistance is an enormous challenge that we face. The tailed phages could be used in place of antibiotics due to their high specificity to host cells, but more knowledge of their organisation and function is required.",signatures:"Helen E. White and Elena V. Orlova",downloadPdfUrl:"/chapter/pdf-download/66740",previewPdfUrl:"/chapter/pdf-preview/66740",authors:[{id:"101052",title:"Prof.",name:"Elena",surname:"Orlova",slug:"elena-orlova",fullName:"Elena Orlova"},{id:"262804",title:"Dr.",name:"Helen",surname:"White",slug:"helen-white",fullName:"Helen White"}],corrections:null},{id:"70784",title:"Biotechnology Tools Derived from the Bacteriophage/Bacteria Arms Race",doi:"10.5772/intechopen.90367",slug:"biotechnology-tools-derived-from-the-bacteriophage-bacteria-arms-race",totalDownloads:776,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The long association and intense competition between bacteria and their viruses have created a fertile ground for evolution to develop numerous tools for DNA modification, assembly and degradation. Many of these tools underpin the past 50 years of molecular biology, and others show great potential in shaping the next 50 years of the field. Here, I present some of the tools that have come out of the bacteria-bacteriophage arms race and discuss some of the concepts that may shape their future use. Molecular biology remains a fast-growing area increasingly limited solely by researcher ingenuity.",signatures:"Vitor B. Pinheiro",downloadPdfUrl:"/chapter/pdf-download/70784",previewPdfUrl:"/chapter/pdf-preview/70784",authors:[{id:"264693",title:"Dr.",name:"Vitor",surname:"Pinheiro",slug:"vitor-pinheiro",fullName:"Vitor Pinheiro"}],corrections:null},{id:"67876",title:"The Unusual Linear Plasmid Generating Systems of Prokaryotes",doi:"10.5772/intechopen.86882",slug:"the-unusual-linear-plasmid-generating-systems-of-prokaryotes",totalDownloads:1176,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Linear DNA is vulnerable to exonuclease degradation and suffers from genetic loss due to the end replication problem. Eukaryotes overcome these problems by locating repetitive telomere sequences at the end of each chromosome. In humans and other vertebrates this noncoding terminal sequence is repeated between hundreds and thousands of times, ensuring important genetic information is protected. In most prokaryotes, the end-replication problem is solved by utilizing circular DNA molecules as chromosomes. However, some phage and bacteria do store genetic information in linear constructs, and the ends of these structures form either invertrons or hairpin telomeres. Hairpin telomere formation is catalyzed by a protelomerase, a unique protein that modifies DNA by a two-step transesterification reaction, proceeding via a covalent protein bound intermediate. The specifics of this mechanism are largely unknown and conflicting data suggests variations occur between different systems. These proteins, and the DNA constructs they produce, have valuable applications in the biotechnology industry. They are also an essential component of some human pathogens, an increased understanding of how they operate is therefore of fundamental importance. Although this review will focus on phage encoded protelomerase, protelomerases found from Agrobacterium and Borellia will be discussed in terms of mechanism of action.",signatures:"Sophie E. Knott, Sarah A. Milsom and Paul J. Rothwell",downloadPdfUrl:"/chapter/pdf-download/67876",previewPdfUrl:"/chapter/pdf-preview/67876",authors:[{id:"298694",title:"Dr.",name:"Paul",surname:"Rothwell",slug:"paul-rothwell",fullName:"Paul Rothwell"},{id:"298695",title:"Ph.D. Student",name:"Sophie",surname:"Knott",slug:"sophie-knott",fullName:"Sophie Knott"},{id:"302001",title:"BSc.",name:"Sarah A",surname:"Milsom",slug:"sarah-a-milsom",fullName:"Sarah A Milsom"}],corrections:null},{id:"68334",title:"Scale-Up and Bioprocessing of Phages",doi:"10.5772/intechopen.88275",slug:"scale-up-and-bioprocessing-of-phages",totalDownloads:916,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"A profusion of new applications for phage technologies has been developed within the last few years, stimulating investigations into the large-scale production of different phages. Applications such as antibiotic replacement, phages as gene therapy vectors, phages as vaccines, diagnostics using filamentous phages and novel optical applications such as the phage laser may need grams to kilogrammes of phage in the future. However, many of the techniques that are used for the growth and purification of bacteriophage at small scale are not transferable to large-scale production facilities of phage in industrial processes. In this chapter, the stages of production that need to be carried out at scale are examined for the efficient large-scale fermentation of the filamentous phage M13 and the Siphoviridae phage lambda (λ). A number of parameters are discussed: the multiplicity of infection (MOI) of phage to host cells, the impact of agitation on the initial infection stages, the co-growth with phage rather than static attachment, the use of engineered host cells expressing nuclease, the optimisation of both the quantity and the physiology of the E. coli inoculum and phage precipitation methods.",signatures:"John Maxim Ward, Steven Branston, Emma Stanley and Eli Keshavarz-Moore",downloadPdfUrl:"/chapter/pdf-download/68334",previewPdfUrl:"/chapter/pdf-preview/68334",authors:[{id:"263913",title:"Prof.",name:"John",surname:"Ward",slug:"john-ward",fullName:"John Ward"}],corrections:null},{id:"64001",title:"Surveillance and Elimination of Bacteriophage Contamination in an Industrial Fermentation Process",doi:"10.5772/intechopen.81151",slug:"surveillance-and-elimination-of-bacteriophage-contamination-in-an-industrial-fermentation-process",totalDownloads:1442,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Commercial fermentation processes are often vulnerable to bacteriophage due to the lack of genetic diversity and use of high cell density cultures. Bacteriophage infections in these fermentations can have adverse impacts on operability of the production facility and product quality and prevent recovery of valuable bioproducts in the downstream process. Prevention strategies have been developed and optimized through feedback from bacteriophage diagnostic tests, which inform improvements to process design for elimination of entry points, as well as modification of the biocatalyst to reduce or eliminate bacteriophage virulence. In this chapter, we provide case studies for successful elimination of bacteriophage virulence via host modifications, including bacteriophage binding-site modifications on the outer membrane of an Escherichia coli production host, used for commercial manufacture of 1,3-propanediol, as well as application of CRISPR-associated protein 9 (Cas9) for bacteriophage immunity. Finally, we report application of bacteriophage diagnostic methods to fully characterize and eliminate bacteriophage entry points in a commercial fermentation process.",signatures:"James A. Zahn and Mathew C. Halter",downloadPdfUrl:"/chapter/pdf-download/64001",previewPdfUrl:"/chapter/pdf-preview/64001",authors:[{id:"257066",title:"Ph.D.",name:"James",surname:"Zahn",slug:"james-zahn",fullName:"James Zahn"},{id:"257072",title:"Mr.",name:"Mathew",surname:"Halter",slug:"mathew-halter",fullName:"Mathew Halter"}],corrections:null},{id:"66194",title:"Targeting Peptides Derived from Phage Display for Clinical Imaging",doi:"10.5772/intechopen.84281",slug:"targeting-peptides-derived-from-phage-display-for-clinical-imaging",totalDownloads:1139,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Phage display is a high-throughput technology used to identify peptides or proteins with high and specific binding affinities to a target, which is usually a protein biomarker or therapeutic receptor. In general, this technique allows peptides with a particular sequence to be presented on a phage particle. Peptides derived from phage display play an important role in drug discovery, drug delivery, cancer imaging, and treatment. Phage peptides themselves can act as sole therapeutics, for example, drugs, gene therapeutic, and immunotherapeutic agents that are comprehensively described elsewhere. In this chapter, we discuss phage selection and screening procedures in detail including some modifications to reduce nonspecific binding. In addition, the rationale for discovery and utilization of phage peptides as molecular imaging probes is focused upon. Molecular imaging is a new paradigm that uses advanced imaging instruments integrated with specific molecular imaging probes. Applications include monitoring of metabolic and molecular functions, therapeutic response, and drug efficacy, as well as early cancer detection, personalized medicine, and image-guided therapy.",signatures:"Supang Khondee and Wibool Piyawattanametha",downloadPdfUrl:"/chapter/pdf-download/66194",previewPdfUrl:"/chapter/pdf-preview/66194",authors:[{id:"70846",title:"Dr.",name:"Wibool",surname:"Piyawattanametha",slug:"wibool-piyawattanametha",fullName:"Wibool Piyawattanametha"},{id:"257847",title:"Dr.",name:"Supang",surname:"Khondee",slug:"supang-khondee",fullName:"Supang Khondee"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"3505",title:"Current Issues in Molecular Virology",subtitle:"Viral Genetics and Biotechnological Applications",isOpenForSubmission:!1,hash:"039c53aa204f5131f1f67d2c24e160d0",slug:"current-issues-in-molecular-virology-viral-genetics-and-biotechnological-applications",bookSignature:"Victor Romanowski",coverURL:"https://cdn.intechopen.com/books/images_new/3505.jpg",editedByType:"Edited by",editors:[{id:"90590",title:"Prof.",name:"Victor",surname:"Romanowski",slug:"victor-romanowski",fullName:"Victor Romanowski"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1775",title:"Molecular Virology",subtitle:null,isOpenForSubmission:!1,hash:"2e80abf77926d0ba82ba2bfd729031b0",slug:"molecular-virology",bookSignature:"Moses P. 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Expert system can be defined as an intelligent computer program, a repository of knowledge, a set of rules, like a human consultant, all aiming at delivering accurate solutions/suggestions to a problem at any level, say during plan, design, manufacture, and quality control. Some of the important definitions are quoted here.
“An expert system is a computer system used to distribute the expertise of a human or group of humans throughout a group of users” (Wang et al., 1991)
“Expert systems (ES) are a branch of applied artificial intelligence involving that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer. This knowledge is then stored in the computer and users call upon the computer for specific advice as needed” (Liao, 2005)
“An expert system is one that has expert rules and avoids blind search, reasons by manipulating symbols, grasps fundamental domain principles, and has complete weaker reasoning methods to fall back on when expert rules fail and to use in producing explanations. It deals with difficult problems in a complex domain, can take a problem description in lay terms and convert it to an internal representation appropriate for processing with its expert rules, and it can reason about its own knowledge (or lack thereof), especially to reconstruct inference paths rationally for explanation and self-justification” (Dym, 1985)
The general structure of an expert system is shown in Fig. 1 (Dym, 1985; Tisza, 1995). The components of the expert system include -
Basic structure of an expert system (
Expert system incorporates three types of knowledge: factual or data oriented knowledge, rule based knowledge, and procedural knowledge (Wang et al., 1991) embodied within a model base. The trend at present is to exploit the convergence of all the three kinds of knowledge representation in a single system. The knowledge base is contained in a set of rules or conditions and a secondary data base. Each production rule represents knowledge about a field, expressed in antecedent-consequent form and a knowledge base may contain hundreds of rules. For example, typical knowledge base can have some 700 rules on maximum load required for forming, press requirements and material properties. There are other ways of knowledge base representation like semantic networks, frame system etc. (Dym, 1985). In semantic network, there are set of
Inference engine basically work on inference rules that tries to derive answers from a knowledge base. Backward chaining and forward chaining are the two main methods of reasoning when using inference rules. Forward chaining is data driven, and backward chaining is goal driven. An inference engine using forward chaining searches the inference rules until it finds one in which the ‘IF’ clause is ‘true’. It then concludes the ‘THEN’ clause and adds this information to its data. It would continue to perform this operation until a goal is attained. An inference engine using backward chaining would search the inference rules until it finds one which has a ‘THEN’ clause that closely matches a prescribed goal. If the ‘IF’ clause of that inference rule is not true, then it is added to the list of goals. The selection of inference engine is important and is coupled to the nature of task the system is intended to perform. The selection of inference engine depends mainly on memory allocation for solving the problem, solution space required, tentative reasoning about the domain, and whether or not the data are noisy and varying with time. Initially LISP, the list processing language; Prolog, a logic oriented language are used, but an important trend in the expert system market is the evolution of systems towards more performance oriented programming languages like C, Pascal, Fortran etc. The reason for such a shift is two fold. Not only the inference engines run faster (100 times faster than older LISP based), but also promote ease of integration with other software applications. Nowadays various methodologies are available to construct expert systems in any chosen field. Expert systems can be developed based on rules, knowledge systems, neural networks, fuzzy system, object oriented methodology, case based reasoning, system modeling, intelligent agents, database methodology etc. (Liao, 2005).
Sheet metal forming is one of the important manufacturing processes used predominantly in automotive, aerospace, electronics, electrical, house-hold appliances, and building sectors. This process involves plastic deformation of metallic sheet materials to make sheet metal components for any application. Typical applications can be seen in cars, washing machines, air plane wings, house-hold appliances like gas cylinders, beverage cans, and building roofs. The sheet metal forming processes include deep drawing, stamping, bending, rolling, spinning, cutting, and blanking. Generally the sheet components are made by any one of the mentioned process or combination of processes. Most of the sheet forming processes requires a sheet material to be formed; tools like die, punch, sheet holder, draw bead, mandrel and machines to perform the operation. The material properties like chemical composition, microstructure, texture; mechanical properties viz., yield strength, ultimate tensile strength, elongation; formability factors like strain hardening exponent, anisotropy index, strain path; process parameters like friction conditions between sheet and tools, working temperature, strain rate, blank holding force, draw bead restraining force; and finally tool (die, punch) geometry influence the sheet forming behavior in a compounding fashion.
In industrial practice, the sheet forming engineer should be aware of the process sequence, tool design aspect, process parameter design, sheet material behavior, blank design, and machine usage for successful fabrication of sheet parts. For example, the process sequence required for single stage and multi-stage components are different. Similarly the tool design requirements for making a sheet product made of un-welded blank and tailor welded blanks (two or more sheets of different thickness, grades etc. welded before forming) are not same because of the presence of different thickness, grade sheets, and weld line movement. The properties and behavior of the sheet material play a vital role in deciding its applicability in making a sheet part. The material behavior requirement will differ from product to product and hence knowledge on materials used is essential.
It is known that the parameters mentioned above determine the sheet formability in a synergistic manner. Designing sheet product for a typical application will be successful only by knowing the appropriate material behavior, process parameter design, process sequence, tool and machine design, and specific issues pertaining to advances in material and forming technology. Predicting these sheet stamping parameters in advance will be helpful in determining the formability of any sheet part. In order to fulfill this requirement, one has to perform lot of simulation trials and experiments separately for each of the cases which is time consuming and resource intensive. Automotive sheet forming designers will greatly benefit if ‘expert systems’ are available for sheet stamping that can deliver its forming behavior for varied material, process and tool conditions. Developing an expert system or knowledge based system, especially in fields like material forming and deformation behavior, die design, casting design, machining processes, metallurgy etc. is of interest to manufacturing and design engineers. In this chapter, the expert system and its application to predict the sheet metal formability in terms of tool, process and material design will be discussed through published literature. The expert system based analyses in sheet process design, process planning, strip layout design, tool design, material forming will be focused. The various techniques used to develop expert systems will be presented. The available expert systems and their applicability in sheet forming field will be highlighted. Finally an expert system research scheme that is being developed to predict the formability of Tailor Welded Blanks (TWB) will be discussed with case study results.
The sheet forming process includes operations like cup drawing, stamping, stretching, bending, ironing, spinning etc. that can be performed by many forming techniques using die, punch, blank holder and other tools. The purpose of process design is to predict and design the sheet forming process at the ‘design stage’ of a component manufacturing. For instance, the number of stages required and amount of cup drawing that is possible in each stage of a multi stage deep drawing process can be predicted at the design stage itself for successful completion of the sheet product. Similarly an intelligent process design would include closed loop control of blank holding force for changing friction conditions during forming. There are many expert systems (ES)/knowledge based systems (KBS) available for sheet process design. The sheet forming processes and techniques by which process design is performed is described here.
Mostly bending, deep drawing, and general stamping (say industrial sheet parts) are concerned for ES development. The applicability of expert system in enhancing the bending process design was demonstrated by Lin and Peing (Lin & Peing, 1994). In this, a prototype of sheet metal bending expert system is developed. This ES is capable of predicting the pressure, width of die shoulder, minimum flange length and product inner radius for a given set of bending and material input. With this data, the punch and die numbers are inferred and their drawings are given as result using the graphic mechanism in the ES. This is presented with an example of V-bending (Lin & Peing, 1994), where the bend angle and material of the sheet were given as input. The predictions are based on ‘IF-THEN’ rules within the ES that are constructed with the help of lot of qualitative data base and experience based knowledge base. Here PC/AT was chosen as the computer hardware and artificial intelligence language LISP (LISt Processing), which has a powerful symbol processing ability, was used as system construction language. Like wise, an Intelligent Design Environment (IDE) which is a knowledge based system, was developed (Palani et al., 1994) that provides a method of integration, automation and optimization of sheet metal forming. IDE integrates knowledge base, finite element analysis and geometric modelers in AI framework. For the inputs like geometry of the sheet and material, process constraints given to the system, the outputs like optimized die geometry, material and process parameters for successful forming operation will be delivered. This was demonstrated in their work (Palani et al., 1994) by analyzing automotive inner panel. In this, ‘IF-THEN’ rules like "IF strain distributions should be uniform, THEN increase the critical radius, AND change the lubrication; OR change the blank size and shape" were used and their IDE can diagnose ‘splitting’ failure. It can effect changes in material properties including thickness, friction, BHF, drawbead geometry, blank size for evaluating the optimum forming operation. In the case of automotive inner panel, the final decision on modifying sharp corners improved the strain distribution and die geometry. A knowledge based process layout system (CAPP) based on decision tables (Sing & Rao, 1997) was constructed for axi-symmetrical deep drawn cup which can deliver a set of highest feasible process parameters for die design purpose for which the final deep drawn product is required as input. In this CAPP system, the knowledge base is represented by ‘production rules’ and ‘frames’. The production rules are based on popular ‘IF-THEN’ rules like “IF First Draw of mild steels THEN the maximum drawing ratio is 2.2”. These rules are represented as decision tables, which makes it very different from other KBS, where in the user can suggest ‘Yes’, ‘No’ or ‘Don’t care’ for many rules corresponding to ‘condition stubs’ entries. The condition stub mainly includes the final cup description like Horizontal elements included, Vertical elements more than one, Concave elements included and many more. Similarly the ‘action stub’ contains different types of drawn cups like flanged cup, hemispherical cup etc. for which once again ‘Yes’, ‘No’ or ‘Don’t care’ entries are suggested for various rules. This system has fuzzy inference like “R1: IF thickness is Ti THEN drawability is Mi, for i = 1…n” which relates drawability and thickness range. Here ‘R1’ is a fuzzy relation mapping thickness (Ti) and drawability (Mi). ‘Ti’ and ‘Mi’ are subsets of sets A (thickness range) and B (drawability) on U and V universe, respectively. These fuzzy inferences are also represented as decision tables containing ‘condition stubs’ like thickness range, cup without flange and ‘action stubs’ like drawing rate. These decision tables are made from ‘frames’, having drawing rates for different types of cups. This system is tested with a ‘flanged cup’ that is given as input. The system basically evaluates, through analytical models and rules, the blank development, blank layout design, number of draws, clearance, air vent hole, tool radii, and BHF and delivers as output (Sing & Rao, 1997). A component check KBS was made (Shailendra Kumar et al., 2006) which is once again based on ‘IF-THEN’ rules made from details available with designers, manufacturers, and hand books that can suggest the feature based outputs based on inputs registered about the sheet material and product. This system makes use of Auto LISP and Auto CAD for data representation. An intelligent design system for multi stage deep drawing is also available (Choi et al., 2002). The capability of the system was illustrated with the help of single stage deep drawing, three step deep drawing, and deep drawing with embossing. A user friendly, menu driven decision support system (DSS) was also developed by Faura et al. for sheet metal blanking operation (Faura et al., 2001). The DSS consists of relational database having technical-economic information and collection of processing algorithms created to apply the know-how to the model. The technical knowledge is based on many experiments relating empirically the blanking clearance with edge draw-in, the penetration depth of cracks, and burr height. By providing dialog boxes to facilitate data input and display of output results, it is possible to predict the impact of blanking parameters on the cost and quality (Faura et al., 2001).
Other than the ES described above, there are expert systems that are helpful for web based manufacturing. These ES are generated and used through World Wide Web (WWW) for part and process design. The user can virtually sit in a remote place performing simulations and analyses and finally design the process and part as per their requirement. A virtual design system (VDS) for sheet forming was developed by Hindman and Ousterhout (Hindman & Ousterhout, 1998). This system has web based interface in ‘fill out forms’ where the user can input the details of forming like material properties, bend allowance, bend radius etc. The forms are framed by html programming and submitted by the user for further processing. The user can also transfer a part to remote shop through file transfer protocol (FTP) for fabrication with worry-free transfer of sensitive data indicating standard security. The VDS is not only demonstrated for ‘bending’ where final die design is performed, but also for spring back and deep drawing. The system works not only on equations, but also has space for real time simulations of the manufacturing processes. An interesting and efficient Distributed Multi Agent expert System (DMAS) was developed for sheet forming by Karayel and Ozkan (Karayel & Ozkan, 2006). DMAS is a system that operates in an integrated manner with all agents to realize a common objective. Each agent is an expert system containing rules and knowledge base that are at distant places connected through internet. The model proposed consists of knowledge management agent (KMA), product description agent (PDA), calculating and dimensioning agent (CDA), artificial intelligence agent (AIA), die design agent (DDA), assembly and disassembly agent (ADA), operation planning and cost agent (OPCA), validation and verification agent (VVA) and design mediator (DM), all are connected and have specific tasks. For example, the job of PDA is to describe sheet metal component geometry when the design is realized using multi agent system. The sheet metal parts are represented and stored as complete geometric and topological solid in 3D. Such a representation is suitable for display and any engineering analyses and simulation can be performed. The PDA also takes care of feature representation like holes, curvatures, bends, flanges, notches etc. Similarly the purpose of VVA is to observe any faults and discrepancies in the manufacturing lot and notifies them to KMA. This agent prepares a global report on all faults and their degrees as well. Similarly all other agents are meant for specific purpose. This system will be implemented fully for efficient running of the sheet metal expert system (Karayel & Ozkan, 2006).
There are expert systems/knowledge based systems based on neural network that control the sheet forming processes. One best example would be the adaptive control system developed for controlling the deep drawing process (Manabe et al., 1998) where in neural network is used for initial process parameter identification. In this, chosen input parameters are trained with optimized network architecture and output material properties like strain hardening exponent (n), plastic coefficient (F), anisotropy coefficient (R) are identified. The predicted n, F, R values is used to evaluate the friction coefficient, fracture/wrinkle limit Blank Holder Forces (BHFs) using available equations.. The BHF is optimized for varying friction conditions during the drawing process by in-process sensing information. Corresponding to the current frictional condition, the fracture and wrinkle limit BHFs are modified and the BHF path scheme can be optimized accordingly. This adaptive system, where the BHF is updated continuously with changing friction conditions, was demonstrated by monitoring thinning distribution of wall region of the cup and comparing it with the constant BHF case. The thinning behavior is found to be improved in the case of adaptive deep drawing system (Manabe et al., 1998). Summing up one can say that, deep drawing, bending and stamping of industrial sheet parts are generally modeled using expert system for planning and controlling the sheet forming process. Also the expert systems used for process design are mainly based on production rules (IF-THEN rules), web based process control strategies, and neural network based data modeling and process control or a combination of different techniques. In the case of ES based on rules, predominantly AutoLISP is used for data handling, in conjunction with AutoCAD for graphics representation. One can also refer the state of the art report by Duflou et al. (Duflou et al., 2005) on computer aided process planning for sheet bending.
Table 1 shows the details of process planning and sequence design in sheet forming. Most of the literature suggests that ‘IF-THEN’ variety is used for knowledge base implementation, while few of them are based on specified algorithms, design rules, fuzzy set theory etc. The sheet operations considered are axi-symmetric forming (like drawing), blanking, bending, general stamping sequence, and specific sheet parts like rotor and stator. The KBS developed are validated with industrial shop floor trials having simple to complex geometries.
The strip layout design exercise is like a tailor’s job. Here the tailor maps the different parts of the shirt and makes a layout as per the customer’s choice, efficient cloth utilization and fashion. Later the layout is used for stitching shirts. Similarly strip layout involves laying out the material strip that is passed through the press in order to produce stamping, exactly as it will appear after all operations have been performed on its parts (Ann & Kai, 1994). The strip layout design is an art by itself, wherein the experience and practice in the light of reality decides the quality of the stamped sheet product. In early days, the strip layout was done manually. The trial and error method followed resulted in maximum material
Illustrations (if any) | A complex part is used here and the system suggests two different bending sequences with six stages in each sequence | Flanged shell having two diameters, tapering concave shell, Reverse geometry; System analyzes the process based on product geometry (1st pass) and product formability (2nd pass) details | A simple sheet part with total of eight features; four holes with two holes of radius 2 units, two holes of radius 1 unit, & four bends | Separate illustrations are given for each of the modules for different operations; Presently sheet with 23 bends can be addressed for its process planning & other sequences. | Stator and Rotor parts | Sheet parts used in Indian telephone industries, India (blanking, plating) & Indo-Asian Fuse Gear Ltd., India (piercing, blanking, & plating) | An industrial part is simulated for validation |
System deliverables | Bending process sequence | Process sequence with intermediate product geometry | Bending & hole making sequence with die number in which the process occurs | Operation sequence, tools & robot grippers needed, tool layout grasp positions, gage, robot motion plans for product making | Generating automatically NC data to match tooling requirements by checking dimensions according to the drawings of the die layout module | Optimal process plan for sheet metal operations | Drawing process steps, dimensions of steps, route selection, lay out die definition |
Knowledge generation modes | -- | Literature, industrial brochure, industrial visits, experts opinion etc. | Discussion with die designers & process planners, die design & forming handbooks | -- | Plasticity theories, experimental results & knowledge of field experts | Knowledge of process planners, die designers, Handbooks, monographs, journals, & industrial brochures | Discussion wit process planners, scientific community, enterprises |
Technique used for developing expert system | Fuzzy set theory | IF-THEN rules variety like “If the input object geometry is axi-symmetric, then the blank is circular”; Knowledge base & inference engine in Prolog, Analysis module in Fortran | Rule based KBS, IF-THEN variety like “If there is a hole on a bent edge, then do the hole before the bend”; implemented using Turbo prolog | State-space search formulation based on A* algorithm; sequence decision is based on cheapest total time (total time = execution time + repositioning time + set up time); specific modules are based on rules & constraints; Implemented using C++ | Design rules for blanking or piercing like ‘If holes to be pierced are close to each other or are not related to function, the holes are distributed for many processes’ and formulae like Pface = Fd / LShear t BLR | IF-THEN rules like ‘If, 0.01 < minimum accuracy needed on part in mm< 2.0; and feature required on part = Line cut then, required operation=slitting’ | Based on design rules implemented in visual basic with GUI, & application development environment |
Purpose of expert system | CAPP system for process sequence design | Knowledge based system for process sequence design | Process planning for sheet forming operations – binding & hole making | Automated process planning for robotic sheet metal bending press brakes; system contains central operation planner, tooling & set up planner, grasping planner, motion planner | CAD/CAM system having nine modules – input and shape treatment, flat pattern layout, production feasibility check, blank layout, strip layout, die layout, data conversion, modeling, post processing | Process planning for small and medium scale sheet metal industries based on rules (150 nos.) implemented in AutoLISP on AutoCAD | Computer aided process planning in sheet forming based on activity model |
Process details in expert system | Brake forming; tools having punch, flat sheet, bend sheet, die, finger stops | Axi-symmetric sheet forming | General sheet parts giving importance to bending & hole punching | Bending operations | Making rotor and stator parts with blanking operation | Sheet metal operations like piercing, blanking, notching, forming, cropping, shearing, trimming, clipping, parting, slitting, perforating, bending & drawing | Meant for drawing operations in sheet forming |
Publication details | Ong et al., 1997 | Sitaraman et al., 1991 | Uzsoy et al., 1991 | Gupta et al., 1998 | Choi & Kim, 2001 | Rajender Singh & Sekhon, 2005 | Ciurana et al., 2006 |
S. No. | 1 | 2 | 3 | 4 | 6 | 7 8 | 8 |
Details of expert systems for process sequence and planning design in sheet forming
utilization and is still followed in many small and medium scale industries. Nowadays there are many computer aided systems that takes care of the strip layout design even in complex parts. A knowledge based system for strip layout design was presented by Ann and Kai (Ann & Kai, 1994), includes two important modules viz., part orientation module and sequence of operation module. The part orientation module is meant for providing recommendations about the appropriate ‘part orientation’ in the parent strip, while the sequence of operation module is mainly to suggest the proper design in terms of strip layout in each of the four operations of the progressive die – piercing, blanking, bending and trimming. The ‘part orientation module’ has few sub-modules like
This section focuses on the development of expert system or knowledge based system for designing the press tools (like punch, die etc.) used for sheet stamping operation. Emphasis is given on the input-output details of the system, structure and design methodology used in the system and case study illustrations. It is observed from the selected references that progressive die design involving varied forming operations, tool design for bending, drawing die design and specific applications like industrial parts, roll forming etc. are dealt with extensively. An attempt has been made to automate the progressive metal stamping die design through knowledge base approach (Cheok et al., 1994). The main input to the system is the 3D representation of a product and the system delivers the die to transform the strip into the stamped (or final) product. Some of the crucial issues to be addressed while automating the die design are work shape representation, die components representation, punch shape recognition and decomposition, staging and assembly of die components, operation simulation of various die operations. In the work shape representation, using existing CAD/CAM system, a 2D drawing of the strip layout can be obtained. In order to convert this data into useful information like detailing the topological relations of the various features of the work shape, semantic network based Constructive Solid Geometry (CSG) representation of the workpiece is considered in the KBES. In CSG, the external profile and internal features of the workpiece are represented as ‘nodes’ where the geometry details about the work shape is stored. There are other decisions pertaining to die design like holes that can be punched in same or different stations are registered as ‘slots’ like Box515, Cyl516 etc. It can be understood that the CSG tree of a workpiece provides a wealth of topological information that can be processed by declarative knowledge statements in a KBES to guide the synthesis of a die. In the case of punch profile recognition, the KBES contains twelve different geometric features that are used to select the usable punch profiles for internal profiles. If a suitable match cannot be achieved, the system will decompose the profile of the internal feature into a combination of the pre-defined shapes using a ‘divide and match’ approach. This is achieved by dividing the original punch profiles into a combination of smaller profiles of standard dimensions that can be included in the twelve punch shapes available in the KBES. The ‘divide and match’ approach is guided by number of condition statements in the KBES. Similarly the selection of punches for external profiles is also governed by few rules and procedures. In order to represent the die components, model based reasoning (MBR) approach is followed. MBR requires die components to be represented in such a way that their geometrical modeling information, their parametric dimensions – stored in database, and the algorithmic mathematical functions for design can interact with declarative knowledge statements governing the synthesis of these components. The standard die components can be represented by part identification and parametric dimensions. The data on its location and orientation is also provided when inserting into the die assembly. The non-standard die components are complex and hence need more information to describe them that depends on other components in the sub-assembly too. As an example, the topology of a punch plate depends on the size and location of the punches it carries, their mounting arrangements, and the size and location of the fasteners and dowels. In this, rules and functions are defined that require resolution of conflict and that do not require resolution of conflict (Cheok et al., 1994). The staging of punch operations and selection of pilot holes are based on IF-THEN rules. Finally all the different components of the KBES are implemented on a PC that uses AutoCAD extension modeling for solid modeling and Kappa PC to provide the object oriented programming capabilities. The routines that convert geometries into features are written in C++ for efficient working. Similarly ‘logic programming’ has been used to plan the process in sheet forming with progressive dies (George-Christoper et al., 2005). The product category targeted is U-shaped with bending and cutting operation predominantly. This expert system consists of three modules like
Bending tool design is another important issue to be discussed. There are expert systems based on ‘rules’ for bending tool design. There are few based on neural network too. Neural network based bending tool design (Lin & Chang, 1996) based on back propagation network requiring machine inputs like pressure capacity of the bending machine, the bending length of the product, the open height of the die, and the punch stroke, is efficient in designing the final tooling for bending using pattern classification capability of the BP neural network. The ANN modeling is performed under digital and conditional attributes mode. For the inputs specified, the output of the system is the bending machine code containing information like pressure capacity, maximum stroke, bending speed, motor power, number of cylinders. Esche (Esche et al., 1996) developed a process and die design methodology for multi step forming (say deep drawing) of round cups. ASFEX – Axisymmetric Sequence Forming Expert System was developed initially in MPROLOG. Later this was transferred to C/C++ environment on UNIX platform. The main aim of the system is to develop the process sequence, especially for multi step forming operation, and tool configuration for the whole forming process. In process sequence design, both geometry and formability based decisions will be taken. The process is then analyzed by finite element simulation to check the feasibility of the production. Later tooling for each stage of forming operation will be decided. There are standard steps and rules to evaluate the forming sequence in the system. The system is demonstrated through a sample simulation of round cup made of Aluminium alloy material and compared with experiments. The radial strain and thickness are also compared with experiments and the results are found to be satisfactory (Esche et al., 1996). Similar expert system for drawing dies is seen in (Lin et al., 2008) also, except that the design is based on parametric design system. The efficiency of the system to improve the design quality is demonstrated through inner wheel housing part.
Roll forming is a continuous bending operation, in which the ductile sheets are passed through consecutive set of rolls, or stands, each performing only incremental, prescribed bending operation, till the actual cross-section profile is obtained. This process is particularly suitable for long strips of large quantities, with minimum material handling. Expert systems are available for roll forming pass design that are based on neural network and shape element idea. In the case of neural network based ES (Downes & Hartley, 2006), Radial Basis Function (RBF) is used for training the input data. The example taken for study has four bends and three parallel surfaces connected by slanting sides. The four bends are quantified by four angles φ1, φ2, φ3, and φ4 that forms input for ANN. The ANN system predicts the output which is the location of the design data. There are almost 63 different locations for the design data that are present with the industrial collaborator. Classification of the data depends on a number of section parameters, such as the total number of bends, sheet thickness and sheet width prior to forming. The system developed in this project searches the 63 storage locations to find integral shapes. If a similar shape is identified it will have the same number of bends, and each corresponding bend angle φ1, φ2, φ3, and φ4 will have a similar value (Downes & Hartley, 2006). In the case of shape element idea (Shen et al., 2003), the roll formed part is visualized as combination of forming of each part. The relationship of space geometry is relatively fixed and the forming steps of these parts are almost same. These parts are called as ‘shape element’. The validity of the system is presented with few examples like roll forming with symmetrical section, non-symmetrical section, welded pipe, and complex shape of steel window section (Shen et al., 2003). There are expert systems for press selection (Singh & Sekhon, 1999) also. The press selection expert system belongs to IF-THEN variety following forward chaining method. The rules are based on information obtained from experienced designers, shop floor engineers, handbooks, journals, monographs and industrial brochures. This system suggests the press tonnage, recommended presses, optimum press and unit manufacturing cost for the posed inputs or queries (Singh & Sekhon, 1999). There are expert systems for specific applications like die design for automotive parts (Lin & Kuo, 2008), progressive die design for electron gun grid parts (Park, 1999) etc. A fully integrated CAD/CAM/CAE system was developed (Lin & Kuo, 2008) for stamping dies of automotive sheet parts that functions with the help of high end softwares like CATIA for layout diagram design and die structure analysis, STRIM software for die face design, DYNAFORM for formability analysis and CADCEUS for tooling path generation and simulation. Finally the stamping die development is illustrated for the ‘trunk lid outer panel’ (Lin & Kuo, 2008).
Table 2 details the expert/knowledge base system (or models) to predict the material forming behavior like flow stress, shear strength, material failure, mechanical properties, residual stress etc. The materials considered are steel, Al alloys, Zircaloy, welds, and processes like rolling practice, shot peening are modeled. Most of the techniques used are ANN based and few others are based on design rules, specific theories and algorithms. ANN is found to reproduce the results with maximum accuracy showing its efficiency over rule based systems. The material behavior thus predicted is of academic importance and industrial practice as well. TENSALUM, an expert system used to predict the stress-strain data of Al alloys, implemented in industries, shortened the testing time by approximately 300-400% in comparison with other programs in market (Emri & Kovacic, 1997). Similarly the KBS for materials management developed by Trethewey et al. (Trethewey et al., 1998) which is demonstrated for the selection of coating for marine applications is of practical importance. More details on the KBS for material forming are given in table 2.
Tailor Welded Blanks (TWB) are blanks of similar or dissimilar thicknesses, materials, coatings etc. welded in a single plane before forming. This welded blank is then formed like un-welded blanks to manufacture automotive components, with appropriate tooling and forming conditions. Applications of TWB include car door inner panel, deck lids, bumper, side frame rails etc. in the automotive sector.
Some of the advantages of using TWBs in the automotive sector are: (1) weight reduction and hence savings in fuel consumption, (2) distribution of material thickness and properties resulting in part consolidation which results in cost reduction and better quality, stiffness and tolerances, (3) greater flexibility in component design, (4) re-usage of scrap materials to have new stamped products and, (5) improved corrosion resistance and product quality. (Ganesh & Narasimhan, 2008). The forming behavior of TWBs is critically influenced by thickness and material combinations of the blanks welded; weld conditions like weld orientation, weld location, and weld properties in a synergistic fashion. Designing TWB for a typical application will be successful only by knowing the appropriate thickness, strength combinations, weld line location and profile, number of welds, weld orientation and weld zone properties. Predicting these TWB parameters in advance will be helpful in determining the formability of TWB part in comparison to that of un-welded base materials. In order to fulfill this requirement, one has to perform lot of simulation, experimental trials separately for each of the cases which are time consuming and resource intensive. This can be avoided if an ‘expert system’ is available for TWBs that can deliver its forming behavior for varied weld and blank conditions.
System deliverables | Stress-strain data is measured from the testing machine for the inputs provided. The tedious procedure of comparing the values of mechanical properties that the tested material should meet with the standards during manual experiments is completely avoided when the ES is used. | Phase transformation physical properties, heat physical properties, mechanics physical properties, mechanical properties, retrieve element and standard of the material, and display the characteristic chart of each material. | The system identifies failure modes & material, geometric properties. The failure modes like corrosion, creep, deformation, fatigue, fracture, surface damage and wear erosion will be identified, after diagnoses. Properties include melting point, yield strength, bond strength etc. | UTS of the sample obtained through tensile test. This is compared with the reference value for checking the adequacy. | Flow stress of the material as function of T, , & ε | σ = f (ε, , T, %C) | Residual stress as a function of depth from surface & shot peening | Shear strength of stainless steel & Zr-2 welds |
Testing method & data base details | Tensile testing is performed based on the data base including test conditions, material, product form, elongation, temper condition, mechanical property limits made from various resources | Database is developed through technical references & experiments | Data base contains material performance knowledge & corrosion details. The data retrieval is done through case based reasoning. The data generated is obtained from domain expert, knowledge engineer and transferred to computer through expertise elicitation shell. | Many test samples were made using varied chemical composition and casting parameters and tensile tested for UTS evaluation. This forms the data for validating the ANN model | Experimental data was used to train the network; Strain, strain-rate & temperature were input parameters for predicting the flow stress (σ) | Experimental runs varied in the range of 1000-20000; Strain, strain-rate & temperature were input parameters for predicting the flow stress (σ) | Experimental results were used as training data set; Shot peening and depth of surface as inputs & residual stress as output | AE signals generated during the spacer pad welding were used as input for ANN |
Technique used | -- | Data mining technology is used to analyze the data from different aspects and finding correlations or patterns among dozens of fields in large relational database. | Certainty theory is used to judge exact inferences. Certainty factor is defined as a measure of belief and disbelief based on evidence is used as decision making index. Visual Basic is used to create inference engine and microsoft Access for the database. | Neural network is used to build the model. The NN with different hidden layers and neurons were evaluated for investigation. | ANN based on back propagation network with two hidden layers and 40 hidden neurons | ANN based on recurrent self-organizing neuro fuzzy networks | ANN based on back propagation learning algorithm with one hidden layer having four neurons; | Multilayer preceptron based ANN is adopted to model the system; 6-20-2 architecture with 20 neurons in hidden layer |
Material details | Mainly for Al and Al alloy sheets; Can be updated for materials like polymers, wood, textile fabrics & leather | Meant for metallic materials suitable for heat treating conditions | -- | Spheroidal cast iron was made for different chemical composition and other casting parameters that has become input for the model | Medium carbon steels tested under different strain, strain-rate and temperatures | Different carbon steels with varying carbon equivalent at different strain, strain-rate and temperature | Shot peened steel sample of C-1020 material | Austenitic stainless steel welds and Zr-4 welds |
Purpose of expert/knowledge base system | To test the mechanical properties of Al and Al alloys. The ES is called as TENSALUM | ES named as IndBASEweb-HT is an intelligent database web tool system of simulation for heat treatment process. This system is capable of performing Metallo-thermo-mechanical coupled analyses. | Generic model of the knowledge structure of materials performance, viz., materials selection and failure analysis, has been developed | Prediction of mechanical properties of spheroidal cast iron | Flow stress prediction during hot deformation | Flow stress prediction during hot deformation | Study the impact of shot peening on residual stresses and predicting the same | Predicting the shear strength of stainless steel & Zr-2 welds |
Publication details | Emri & Kovacic, 1997 | Qiang et al, 2006 | Trethewey et al., 1998 | Calcaterra et al., 2000 | Rao & Prasad, 1995 | Shashi Kumar et al., 2007 | Karatas et al., 2009 | Vasudevan et al., 2005 |
S. No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
Details of expert systems in material forming
The main objective of the present research work is to develop an ‘expert system’ for welded blanks that can predict their tensile, deep drawing, forming behavior under varied base material and weld conditions using different formability tests, material models, and formability criteria. It is decided to develop the expert system in conjunction with Artificial Neural Network (ANN). The data required for the expert system development is obtained through simulations only. PAM STAMP 2G a finite element code is used to generate data for varied base material and TWB conditions. The proposed expert system design for TWB forming is shown in Fig. 2 (Veera Babu et al., 2009). This expert system is expected to involve three different phases. All the three phases have a design mode of operation where an initial expert system is created and put in place. The created expert system is then operated in use and update mode.
In Phase 1, while the expert system is designed, a range of material properties and TWB conditions are defined within which ANN models are developed to predict the results as discussed in the earlier sections. The same phase while operated in the usage mode, the user selects base material properties and TWB conditions within the chosen range for application and prediction of formability. In this phase, user can select different material models viz., strain hardening laws and yield theories to predict the forming behavior. There is no single strain hardening law and yield theory that can predict the forming behavior of TWBs made of varied sheet materials accurately. Hence in the design mode, ANN models will be developed to predict the forming behavior using different material models. As a result, in the usage mode of the expert system, the user can opt for desired material models to predict the forming characteristics.
Phase 2 involves selecting the forming behavior to be predicted for chosen base material and weld conditions. In the design mode, tensile behavior, formability characteristics, deep drawability of welded blanks will be simulated by standard formability tests. Different category of industrial sheet parts will be simulated and expert system will be developed to predict their forming behavior. The global tensile behavior of TWB viz., stress-strain curve, yield strength, ultimate tensile strength, elongation, strain hardening exponent and strength co-efficient will be monitored. Formability properties like forming limit curve, percentage thinning, dome height at failure, failure location will be predicted by Limit Dome Height (LDH) test and in-plane stretching tests using different limit strain criteria (say M-K analysis, thickness gradient based necking criterion, effective strain rate criterion, semi empirical approach etc.). Cup deep drawability response like draw depth, weld line movement, punch force, failure location, earing and draw-in profile can be predicted. Also it is planned to develop ANN model and expert system for predicting the formability of application (or industry) specific sheet parts made of welded blanks. In the usage mode, the user selects the type of test results that is required to be predicted.
In phase 3 the training, testing, usage and updating the ANN predictions with simulation results will be performed. In the design mode operation, various ANNs are created and validated for predicting the forming behavior (enumerated in Phase 2) for various combination of material properties and TWB conditions and constitutive behavior (enumerated in Phase 1). In the usage mode, the user predicts the required forming behavior for an initially chosen material, TWB condition and constitutive behavior. If the forming behavior predicted is not indicative of a good stamped product, the user changes the above said conditions till he gets satisfactory results. In the absence of this expert system, the user will have to run time consuming and resource intensive simulation for this iterative stage. In
Expert system proposed for TWB forming (Veera Babu et al.,
the usage mode, if the results are not with in the expected error limit, the user will have the choice of selecting different material models for predicting the required forming behavior as described earlier and/or the expert system is updated with the specific case by updating the ANN models to predict the case within acceptable error limits. In this way, the expert system also learns form the application cases, enhancing the range and success rate of predictions.
In this chapter, some representative expert system prediction like the stress-strain behavior, draw-in profile during cup deep drawing, and forming limit curve are presented. The tools required for tensile test, deep drawing test, Limit dome height test simulation and modeling details can be obtained from (Veera Babu et al., 2009; Siva Krishna, 2009). The six different input parameters are varied at three different levels (decided from literature) and simulation trials were conducted as per L27 Taguchi’s orthogonal array. The various ANN parameters like number of hidden layers, neurons, and transfer functions are optimized based on many trials to predict the outputs within the normalized error limit of 10-4. Various network structures with one and two hidden layers with varying number of neurons in each layer are examined. Finally the architecture which yielded better performance is used for modeling. In all the cases, a feed forward back propagation algorithm is selected to train the network in Matlab programming environment. Here the scaled conjugate gradient algorithm is used to minimize the error. From the available simulation data sets, 27 data sets are used to train and two intermediate data sets are utilized for testing the ANN model/expert system. The comparison between ANN predicted true stress-strain behavior and simulation results are shown in Fig. 3. The strain hardening exponent (
It should be noted that even though Hollomon’s strain hardening law is not accurate to predict the tensile behavior of aluminium alloy base material, ANN predictions are quite accurate in predicting the same. Similarly, the comparison between ANN/expert system and simulation results of draw-in profile during square cup deep drawing is presented in Fig. 4. At different TWB conditions, the draw-in profile predicted by ANN model/expert system is well matched with the simulation results for both steel and Al alloy TWBs. In the case of LDH test, the FLC is predicted by thickness gradient based necking criterion
Validating the true stress - strain behavior predicted by ANN/expert system with FE simulation; (a) Steel TWB, (b) Al alloy TWB (
Comparison of cup draw-in profile between ANN prediction and FE simulation; (a) Steel TWB, (b) Al alloy TWB (Veera Babu et al.,
(TGNC). The ANN/expert system prediction is found to show excellent correlation with FLC from the criterion (Fig. 5 a-c) for steel TWB. It is also found (Siva Krishna, 2009) that the FLCs predicted from other failure criteria – effective strain rate, major strain rate based necking criteria, both the original and modified ones (Fig. 5 a-c), are comparing satisfactorily with the expert system results. A slight deviation in the plane strain and stretching modes of deformation is seen in both the intermediate TWB conditions.
The proposed ANN based expert system for TWB is in rudimentary stage of development. The suitability of the system is problem specific. A sheet forming engineer who wants to develop an expert system for some industrial TWB sheet part can just make it as part of
Comparison of ANN prediction with TGNC and other failure prediction (continued). Comparison of ANN prediction with TGNC and other failure prediction - (a) intermediate level 1, ESRC, MSRC, TSRC comparison; (b) intermediate level 2, ESRC; (c) intermediate level 2, MSRC (
existing system framework in the same line of thought, with out introducing new rules and conditions. This way the expert system is also expanded, becomes more efficient in solving realistic TWB forming conditions. The relations between TWB inputs and outputs are non-linear in nature and hence it is complex to explicitly state rules for making expert system. But these complex relationships can be easily handled by ANN. In fact, it is not mandatory that the user should know about the input-output relations in TWB. Since this expert system is ANN based, it can potentially become a learning system as the problem solved by the system can also become a part of training examples for customers. Though the expert system can not reason out the decisions/results unlike rule based systems, one can interpret the results by comparing the outputs of two different input conditions quantitatively with minimum knowledge in TWB forming behavior. The ANN learning and fixing optimum architecture takes time and are problem specific, which can be sorted out by practice. The expert system developed in this work is applicable within the range of input and base material properties specified by Veera Babu et al. (Veera Babu et al., 2009). Though this is true, the range specified is large enough to include usable TWB conditions. It is worth to study the applicability of the present expert system outside the range and for many new sheet materials including high strength steels.
In this chapter, the expert system applications in designing, planning, and manufacturing of sheet parts is discussed. Emphasis is given for process design, process sequence and planning, strip layout plan, and tool design. The use of expert system in material forming is also highlighted. Finally an expert system that is being developed to predict the TWB forming behavior is presented. The expert systems play a vital role in designing the sheet forming processes and acts like a ‘brain’ in taking decisions and suggesting the optimum conditions for better sheet formability. In TWB, the expert system can predict the weld line movement for the given input properties, by which the blank holding force can be varied suitably to minimize the weld line movement. Most of the expert/knowledge based systems belong to IF-THEN variety which has interpretation power and updating ability. Some of the systems are neural network based that are capable of handling non-linear relationships in a better fashion and are independent of existing design rules. The only disadvantage is that they can not interpret the results, unlike IF-THEN rule based systems. The strength of ANN based system is that any new material, forming process, process parameter, and industrial parts can be included into the model without formalizing new rules, except that one needs to train and test the network whenever it is updated for new prediction work. The IF-THEN variety systems are based on data obtained from experts, industries, handbooks, journals etc. while ANN are based on data from experiments and simulations. Also it looks like most of the systems are developed as per industrial requirements, rather than for academic research. There are ES for deep drawability, bending, and blanking that are quantified by ‘geometric parameters’ like cup height, bending angles etc. and hardly any expert system is found to design the sheet forming based on ‘forming limit diagram (FLD)’ which is quantified by strain or stress developed during the forming operation. The ES based on FLDs will give more insight into the sheet design aspects. In this case, one has to follow some forming limit criteria to predict the limit strains under varied TWB conditions, as depicted earlier in TWB expert system.
In future, expert systems to design and predict, (a) the sheet formability of new materials like high strength steels, advanced high strength steels; new processes like hydro forming, micro forming etc.; (b) the ability of allied processes like friction stir welding, laser welding to manufacture sheet parts are expected. For instance, expert system can be developed for tailor welded blanks made of dual phase steel, friction stir welded blanks made of Al alloy sheets, hydro forming of dual phase steel, spring back and bending of high strength steels that are of practical importance and can be used efficiently in industries. Efficient expert systems that can predict the microstructural, fatigue, and high temperature behavior of many automotive and constructional materials should be developed in future. ANN model developed by Hosseini et al. (Hosseini et al., 2004) to predict the tensile strength and elongation of TRIP steels is an example of this kind. One can also develop hybrid expert systems that integrate different methods of expert system development like ANN and Genetic Algorithm (GA) to predict the sheet forming behavior. The best example for this is the spring back prediction work done by Liu et al. (Liu et al., 2007) using integrated ANN and GA, in which GA is used to optimize the weights during ANN modeling.
Taiwan is on the path of western Pacific typhoon path and on the circum-Pacific earthquake belt, indicating that Taiwan suffered from two or more natural disasters, which was the highest in the world [1]. Besides, most of the land in Taiwan, about 70% of total area, is hillside. Given the conditions of increasing impacts of climate change and extreme weathers, the rainfall-induced landslide has become a serious issue in Taiwan.
Most landslide researches used the landslide susceptibility analysis (LSA) to develop landslide evaluation model [2]. The LSA models basically use factors and observed data to construct the description of landslides. The factors include rainfall intensity, accumulated rainfall, slope degree, vegetation, etc. The common models developed for landslide hazard or landslide evaluation are usually deterministic analysis, including the traditional slope stability analysis [2]. Recently, a novel concept of applying probability to landslide evaluation had been proposed. The fragility curves, which are commonly used in the earthquake-induced structure analysis, had been adopted to represent the probability of landslide [3, 4, 5]. The process of applying fragility curve to landslide evaluation is to consider and estimate the recurrence and the probability of exceedance of a damage level for a landslide [3, 4].
In this chapter, the preparation of landslide fragility curves was introduced. The procedure of developing the landslide fragility curve (LFC) model was the researches of rainfall-induced shallow landslide in the past years [2, 3, 4, 5]. The proposed LFC model considered the impacts of rainfall and the vulnerability of environment. Instead of using one-variable triggering factor (rainfall intensity or accumulation) in the previous research [2], the newly improved LFC model used bivariate approach in the model [3, 4]. The improved LFC model introduced the landslide fragility surface (LFS) by considering the influence of both rainfall intensity and accumulation at the same time [4, 5].
The spatial statistics and geographic information system (GIS) were used for data processing. The data of each factor used in the model was further divided into groups. Classification of factors represented the environmental characteristics of a specific area. The analysis basis was conducted spatially on the slope units, which are topographically defined as the parts of a watershed [5]. With the LFS model, the risk assessment of landslide then was analyzed in association with the rainfall hazard potential [4, 5]. The Shenmu area of Chen-Yu-Lan watershed was selected as the study area, and historical cases were used to illustrate the application of LFS model.
When considering the factors to be used in the landslide problem, these factors are generally classified as triggering and environmental factors [6, 7, 8]. Among these factors, the rainfall is usually the major concern, and for environmental vulnerability, many factors can be chosen from. Not every chosen environmental factor can be used in developing a landslide model because of (1) few data in the database, (2) lack of data, and (3) low influence in the model. In this chapter, the cumulative rainfall and maximum hourly rainfall (rainfall intensity) were used for triggering factors, whereas slopes, slope aspects, landslide area, incremental landslide area, ratio of incremental landslide area, normalized difference vegetation index, distance to the nearest river, and geology were used for environmental factors of hillside slope in the study. A GIS database to describe landslide areas was created and was later applied in developing the proposed fragility curve model. These indexes, factors, and symbol definitions are explained in the following:
Maximum rainfall intensity (
Effective accumulated rainfall (
Hillside slope (S): the dynamic behavior of the landslide has close relationship with the slope. Hence, the degree of slope may be a prominent factor of triggering landslides. In this study, the slope was classified based on the Soil and Water Conservation Bureau manual [9]. There are seven slope levels of 5% or less, 5–15%, 15–30%, 30–40%, 40–55%, 55–100%, and slope exceeding 100%. The slopes <15% are recognized as flat ground or very gentle slopes and not included in this study. Slopes of levels 3–7 were studied in the landslide model.
Slope aspect (A): the slope aspect represents the vulnerable directions of occurring landslide when given a known topography. This factor may represent the “weak” aspect of a slope in terms of landslide.
Landslide area (LA): observing the landslide distribution through image classification results can obtain the information about the land cover change. The change from events of Typhoon Sinlaku (in 2008) and Typhoon Morakot (in 2009) was identified using GIS software.
Incremental landslide area (IA): to understand the landslide increment, the images before and after a landslide were considered. The landslides are classified into five categories (shown in Figure 2): (1) the original landslide area (number 1 + 2), (2) the original landslide area extension (number 2), (3) new landslide area on single period (number 3), (4) new landslide area on pre-/post periods (number of 2 + 3), and (5) vegetation restoration area (number of 1). In this study, the new landslide area on pre-/post periods (number of 2 + 3) was considered.
Ratio of incremental landslide area (RIL): to obtain the ratio of incremental landslide area, this study used the incremental landslide area from image of two periods to determine this factor.
Vegetation index (N): to determine the density of vegetation on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants [10]. Nearly almost satellite vegetation indices employ the difference formula,
Distance to the nearest river (R): the landslide may be triggered due to the erosion by the river at the toe section. The distance to the river reflects the potential of landslide contributed from the river system.
Geology (G): the geological time scale of the area and the rock types of the site were combined into consideration as the geology factor. In the past studies, the geology-related information (like the rock types and rock strength) was not usually available. Therefore, to simplify the classification, the geological time scale was chosen to represent the possible influence of geology.
The definition of rainfall indices: Imax and Rte (modified after [
Concept of mapping landslide area change: differences between two periods of SPOT image [
To explain the landslide fragility model, the Shenmu area in Taiwan was used as a case to demonstrate the development of LFC of a given site. The Shenmu area locates in the watershed of Chen-Yu-Lan River. Chen-Yu-Lan watershed is at the central part of Taiwan (Figure 3). The Chen-Yu-Lan River originates from the north peak of Yu Mountain and is one of the upper rivers of the Zhuoshui River system, which is the largest river system in Taiwan. Chen-Yu-Lan River has a length of 42.4 km with an average declination slope of 5%, and its watershed area is about 450 km2. This area was fragile after the Chi-Chi Earthquake (occurred on September 21, 1999).
Chen-Yu-Lan watershed [
The Shenmu area is a location where debris flows frequently occurred [5]. The local village is adjacent to the confluence of three streams: Aiyuzi Stream (DF226), Huosa Stream (DF227), and Chushuei Stream (DF199). In Shenmu, the debris flows usually occurred at the Aiyuzi Stream due to its shorter length and large landslide area (Table 1) in its upstream [5]. Figure 4 shows the terrain of three streams.
Debris flow no. | Stream | Length (km) | Catchment area (km2) | Landslide area (km2) |
---|---|---|---|---|
DF199 | Chushuei stream | 7.16 | 8.62 | 0.33 |
DF227 | Huosa stream | 17.66 | 26.20 | 1.49 |
DF226 | Aiyuzi stream | 3.30 | 4.00 | 1.00 |
The landslide area in Shenmu after 2009 [5].
The terrain and landslide areas of Shenmu area.
In addition to the basic terrain data of Shenmu area, the hydrologic and geographic factors are needed in modeling. To obtain these factors, an environment database of Chen-Yu-Lan watershed was prepared. Among the data collection, the landslide increment (i.e., new landslides) after a rainfall event was also obtained by image processing method in this study.
To develop the LFC model, the local environmental data was collected for the study area, and GIS was used to process the data. The environment database of Chen-Yu-Lan watershed includes data of geology, geological layers, rock property, slope and slope aspects, and DEM, as shown in Figures 5–8.
Chen-Yu-Lan watershed: (a) geological time scale and (b) rock types.
Five-meter DEM of Chen-Yu-Lan watershed (after [
The slope of Chen-Yu-Lan watershed.
The slope aspects of Chen-Yu-Lan watershed.
The new landslide areas (Figures 9 and 10) were identified by using pre- and post-event satellite images of Typhoon Sinlaku in 2008 and Typhoon Morakot in 2009 (Table 2). These landslide areas were used for later LFC model analysis. Another important factor in the LFC model is the vegetation conditions. The information of vegetation status was also obtained by image processing the same as the determination of new landslides.
Satellite images of pre- (a) and post-event (b) Typhoon Sinlaku and the new landslide areas (c) in Chen-Yu-Lan watershed.
Satellite images of pre- (a) and post-event (b) Typhoon Morakot and the new landslide areas (c) in Chen-Yu-Lan watershed.
Watershed | Event | Image time | Satellite | Incremental area (km2) |
---|---|---|---|---|
Chen-Yu-Lan 448.14 km2 | Pre-Sinlaku | February 21, 2008 | SPOT5 | 9.52 (2.12%) |
Post-Sinlaku | November 28, 2008 | SPOT5 | ||
Pre-Morakot | November 28, 2008 | SPOT5 | 10.21 (2.28%) | |
Post-Morakot | October 14, 2009 | SPOT5 |
Satellite images of events at Chen-Yu-Lan watershed.
In addition to the hydrologic and geographic data, the landslide triggering factors were also considered in data preparation. Table 3 defines the rainfall indices. It should be noted that the effective accumulated rainfall was calculated by including the antecedent 7-day accumulated rainfall. The antecedent 7-day accumulated rainfall is the total weighted rainfall counted from the 7-day duration before the starting of current rainfall event. Take Typhoon Sinlaku (September 11–16, 2008) for example. The starting date of Typhoon Sinlaku was September 11, 2008, and the antecedent 7-day accumulation rainfall was the total weighted rainfall during September 3 to September 10, as described as
Index | Symbol | Definition |
---|---|---|
Max. hourly rainfall | The maximum hourly rainfall in a rainfall event | |
Effective accumulated rainfall | The antecedent 7-day accumulated rainfall (with reduction factor of 0.7*) before the starting of current event and the accumulated rainfall before the max. hourly rainfall in current event |
The rainfall indices.
Antecedent 7-day accumulated rainfall (Ra) can be calculated by
Figures 11 and 12 show the rainfall interpolation of the events of Typhoon Sinlaku (September 11–16, 2008) and Typhoon Morakot (August 5–10, 2009). The red spots in the figure are the locations of rainfall stations. It was noted that the rainfall intensity and the cumulative rainfall of event of Typhoon Morakot were much higher than those of Typhoon Sinlaku. Both events had caused serious landslides in the central Taiwan.
Rainfall indices of Typhoon Sinlaku: (a) Imax and (b) Rte.
Rainfall indices of Typhoon Morakot: (a) Imax and (b) Rte.
Finally, the database was used to analyze the study area on the basis of slope units. The slope unit was defined as in Figure 13. A slope unit is defined as one slope part or the left/right part of a watershed. Slope units can be topologically divided by the watershed divide and drainage line, with the help of GIS tool [12]. The application of slope unit in the development of LFC was based on the physical interpretation of slopes in the mountain area. The environmental database was applied in accordance with the slope units at the site of interest. Figure 14 shows the slope unit distribution (total 5872 units) of Chen-Yu-Lan watershed.
Slope unit delineation, the left and right slope units of a watershed [
The slope units of Chen-Yu-Lan watershed.
To develop the empirical landslide fragility model, a probability distribution was chosen to describe the potential of landslide fragility. When the probability distribution was determined, the parameters of probability, the median and standard deviation, were obtained by fitting the data from the environmental database and the landslide areas. The use of slope unit was adopted here, and the classification of environmental factors was applied to represent the conditions of landslide given rainfall intensity and accumulated rainfall. The procedure of developing the empirical landslide fragility curve was described in the following.
The fragility analysis is usually used to describe the potential of hazard in terms of potential levels or probability of exceedance of a level. To describe the probability about a hazard fragility, a feasible probability distribution can be assumed and applied in the model. The fragility curve of landslide, therefore, was assumed to be a lognormal distribution [12, 13]. The lognormal distribution can be constructed simply by the values of median and lognormal standard deviation and are called bivariate parameters (Eq. (1)):
where
Eq. (2) represents the
Since both the rainfall intensity and rainfall accumulation contribute to the probability of triggering a landslide, the bivariate lognormal distribution was applied in the developing LFC model [4, 14], as in Eq. (3):
where
Eq. (4) represents the j-th fragility curve of landslide, including four fragility parameters. The cumulative density function of Eq. (4) is a fragility surface of probability.
The parameters in Eq. (4) can be obtained by using the least square estimate. When the landslide locations and areas are available, meaning the classification of landslide based on the factors (see next section), the fragility curve of landslide (a surface) of a specific classification can be determined.
The environmental factors, geology, slope, distance to river, slope aspect, and vegetation index, were classified into levels in order to group similar slope units. The triggering factors of rainfall intensity and effective accumulated rainfall were also redistributed onto slope unit scale. These factors were classified into groups, i.e., two groups of G, three of S, two of R, two of A, and two of N (Tables 4–8), based on the available data and appropriate judgment to simplify the process. There were total of 48 combinations of classification, as described below.
Classification | Geology time scale | Rock type |
---|---|---|
G1 | Eocene | Dark gray slate and phyllite slate, interbedded with quartz sandstone |
Eocene | Slate and phyllite quartzite sandstone | |
Oligocene | Hard shale sandwiched to thick sandstone | |
Oligocene | Thick or massive white medium to very coarse quartzite and hard shale | |
G2 | Miocene | Hard shale, slate, phyllite sandstone |
Mid-Miocene | Sandstone and shale interbed, coal seam | |
Late Miocene | Sandstone and shale interbed, coal seam | |
Miocene to Pliocene | Sandstone and shale interbed, coal seam | |
Pliocene | Shale, sandy shale, mudstone | |
Pliocene | Sandstone, mudstone, shale interbed | |
Pliocene to Pleistocene | Gravel | |
Pleistocene | Gravel, sand, and clay |
The geology classification.
Classification | SWCB slope level | Technical regulations for soil and water conservation | |
---|---|---|---|
Slope range | degree (°) | ||
S1 | 3 | 15% < S ≦ 30% | 8.53 < S ≦ 16.70 |
4 | 30% < S ≦ 40% | 16.70 < S ≦ 21.80 | |
S2 | 5 | 40% < S ≦ 55% | 21.80 < S ≦ 28.81 |
S3 | 6 | 55% < S ≦ 100% | 28.81 < S ≦ 45.00 |
7 | S > 100% | S > 45.00 |
The slope classification.
Classification | Definition | Distance (m) |
---|---|---|
R1 | Close | ≤300 m |
R2 | Not close | >300 m |
The classification of distance to river.
Classification | Definition |
---|---|
A1 | Weak aspect: the four slope aspects of higher ratio of incremental landslide area. In this study, A1 are E, SE, S, and SW |
A2 | Strong aspect: the four slope aspects of lower RIL. In this study, A2 are W, NW, N, and NE |
The classification of slope aspects.
Image process | Classification | ||
---|---|---|---|
Low vegetation | Mid-to-high vegetation | ||
−1 < NDVI ≦ NDVIc* | NDVIc* < NDVI ≦ 1 | ||
Pre-event image | Barren land | N1 | N1 |
Non-barren land | N1 | N2 |
The vegetation classification.
NDVIc is the threshold value to classify low and mid-to-high vegetation index. In this study, the NDVIc was −0.35.
The geology is an important factor when considering the potential of landslide. However, the geological conditions, like soil layer depth, rock type, and strength at the site, are not usually available to researchers. Therefore, a simplified step can be used at the geology time scale to generally represent the older and younger stratum of the study area. For Chen-Yu-Lan watershed, the rock type of the area was first used to highlight the geological time scale. The same geology era contained different rock formations, and the factor of geology was classified into two groups, as shown in Table 4 and Figure 15. It was noted that there are 1798 slope units of G1 and 2463 slope units of G2.
The geology classification of Chen-Yu-Lan watershed.
Based on the Soil and Water Conservation Bureau manual, the hillside slope is classified as seven levels. In the fragility model, level 3 to level 7 slopes were considered and simply further classified as three groups, as shown in Table 5. Figure 16 shows the classification results in the Chen-Yu-Lan watershed, and 137 slope units were classified as S1, 827 as S2, and 3297 as S3.
The slope classification of Chen-Yu-Lan watershed.
The distance to the nearest river channel was classified into two groups, with the threshold value of 300 m. Table 6 and Figure 17 show the classification results, in which there are 2482 and 1779 slope units of R1 and R2, respectively.
The classification of distance to the river of Chen-Yu-Lan watershed.
The slope aspect was considered in the beginning to distinguish the range of frequent landslide on a given mountain slope. There are eight slope aspects (Figure 18) used in the study that were grouped into two classes as shown in Table 7 and Figure 19, in which there are 2051 and 2210 slope units of A1 and A2, respectively.
The slope aspects.
The slope aspect classification of Chen-Yu-Lan watershed.
The land cover status was also an important factor when estimating the landslide potential. The normalized difference vegetation index was used to represent the land cover status of a given site. Satellite images of SPOT (February 21, 2008, November 28, 2008, and October 14, 2009) were used to calculate the NDVI of the ground surface, and an empirical NDVI threshold was applied to classify barren land and non-barren land. Table 8 summarized the classification, and Figure 20 shows the results, in which there are 2765 and 1496 slope units of N1 and N2, respectively.
The vegetation index classification of Chen-Yu-Lan watershed.
The rainfall data from Typhoon Sinlaku in 2008 and Typhoon Morakot in 2009 was applied to obtain the rainfall intensity and effective accumulated rainfall in the Chen-Yu-Lan watershed. The hourly rainfall data measured at the surrounding weather stations was used to get the rainfall of each slope unit by interpolation. Figures 21 and 22 show the rainfall distribution during the two typhoon events.
The rainfall of Chen-Yu-Lan watershed during Typhoon Sinlaku: (a) max. hourly rainfall (Imax) and (b) effective accumulated rainfall (Rte).
The rainfall of Chen-Yu-Lan watershed during Typhoon Morakot: (a) max. hourly rainfall (Imax) and (b) effective accumulated rainfall (Rte).
Based on the site investigation in the past after typhoon events, the expected average landslide volume (V) was set as V = 6000 m3. By applying the relationship of
Slope S1: the slope unit is counted as a landslide when its landslide area ratio (LAR) is equal to or higher than 5% or the projected landslide area on the slope is greater than 2800 m2 (0.28 ha). Otherwise, the slope unit is not counted as a landslide area.
Slope S2: the slope unit is counted as a landslide when its landslide area ratio is equal to or higher than 5% or the projected landslide area on the slope is greater than 2400 m2 (0.24 ha). Otherwise, the slope unit is not counted as a landslide area.
Slope S3: the slope unit is counted as a landslide when its landslide area ratio is equal to or higher than 5% or the projected landslide area on the slope is greater than 2200 m2 (0.22 ha). Otherwise, the slope unit is not counted as a landslide area.
The landslide area classification of Chen-Yu-Lan watershed is shown in Figure 23. There were 1810 slope units of landslide after Typhon Sinlaku and 1544 ones after Typhoon Morakot, as shown in colored slope units in Figure 23.
The landslide area of Chen-Yu-Lan watershed during (a) Typhoon Sinlaku and (b) Typhoon Morakot.
The environmental database and rainfall data of typhoon events were applied to classify the slope units and the landslide areas. With the classification described in previous sections, there were a total of 48 classes with combinations of factors G, S, A, R, and N. Each classification was in association with two rainfall indices, the rainfall intensity and effective accumulated rainfall. The fragility of landslide, or the probability of exceeding a level of hazard, was constructed and used for landslide potential assessment. Tables 9 and 10 summarized the fragility parameters obtained from the two events, and some examples of fragility curves were shown in Figure 24. It should be noted that during the classification, insufficient samples of certain classification had led to difficulty of finding parameters needed. Therefore, these samples were combined with other classifications in order to get reasonable probability values of median and standard deviation.
Classification | Combined with* | ||||
---|---|---|---|---|---|
Median | Std. deviation | Median | Std. deviation | ||
G1S1A1R1N1 | 64.40 | 0.21 | 485.00 | 0.28 | With 21111 |
G1S1A1R1N2 | 27.53 | 1.24 | 383.77 | 0.29 | With 21112 |
G1S1A1R2N1 | 33.70 | 0.31 | 1112.62 | 0.10 | With 21121 |
G1S1A1R2N2 | 37.94 | 0.16 | 239.39 | 0.27 | With 21122 |
G1S1A2R1N1 | 44.40 | 1.10 | 290.86 | 0.24 | With 21211 |
G1S1A2R1N2 | 43.91 | 0.16 | 1007.19 | 0.71 | With 21212 |
G1S1A2R2N1 | 32.48 | 0.77 | 320.60 | 0.39 | With 21221 |
G1S1A2R2N2 | 40.58 | 0.45 | 332.07 | 0.22 | With 21222 |
G1S2A1R1N1 | 40.44 | 0.58 | 235.49 | 0.79 | With 22111 |
G1S2A1R1N2 | 72.70 | 0.32 | 384.00 | 0.67 | With 22112 |
G1S2A1R2N1 | 22.60 | 0.34 | 407.35 | 0.26 | With 22121 |
G1S2A1R2N2 | 74.16 | 1.17 | 527.59 | 1.20 | With 22122 |
G1S2A2R1N1 | 22.41 | 0.70 | 399.60 | 1.23 | With 22211 |
G1S2A2R1N2 | 42.39 | 0.28 | 252.25 | 0.62 | With 22212 |
G1S2A2R2N1 | 14.08 | 0.11 | 706.36 | 0.80 | With 22221 |
G1S2A2R2N2 | 115.74 | 0.61 | 207.21 | 0.77 | With 22222 |
G1S3A1R1N1 | 18.81 | 0.21 | 135.69 | 1.06 | |
G1S3A1R1N2 | 14.51 | 0.12 | 295.58 | 0.29 | |
G1S3A1R2N1 | 75.05 | 0.29 | 225.74 | 0.88 | |
G1S3A1R2N2 | 28.07 | 0.38 | 269.76 | 0.55 | |
G1S3A2R1N1 | 35.79 | 0.57 | 967.74 | 0.35 | |
G1S3A2R1N2 | 44.53 | 1.54 | 554.12 | 1.26 | |
G1S3A2R2N1 | 29.66 | 0.72 | 298.05 | 0.30 | |
G1S3A2R2N2 | 34.00 | 0.89 | 269.00 | 0.69 |
Fragility parameters of G1 classification.
Due to the insufficient data, some classifications were combined together in order to obtain reasonable parameters.
Classification | ||||
---|---|---|---|---|
Median | Std. deviation | Median | Std. deviation | |
G2S1A1R1N1 | 64.40 | 0.21 | 485.00 | 0.28 |
G2S1A1R1N2 | 27.53 | 1.24 | 383.77 | 0.29 |
G2S1A1R2N1 | 33.70 | 0.31 | 1112.62 | 0.10 |
G2S1A1R2N2 | 37.94 | 0.16 | 239.39 | 0.27 |
G2S1A2R1N1 | 44.40 | 1.10 | 290.86 | 0.24 |
G2S1A2R1N2 | 43.91 | 0.16 | 1007.19 | 0.71 |
G2S1A2R2N1 | 32.48 | 0.77 | 320.60 | 0.39 |
G2S1A2R2N2 | 40.58 | 0.45 | 332.07 | 0.22 |
G2S2A1R1N1 | 40.44 | 0.58 | 235.49 | 0.79 |
G2S2A1R1N2 | 72.70 | 0.32 | 384.00 | 0.67 |
G2S2A1R2N1 | 22.60 | 0.34 | 407.35 | 0.26 |
G2S2A1R2N2 | 74.16 | 1.17 | 527.59 | 1.20 |
G2S2A2R1N1 | 22.41 | 0.70 | 399.60 | 1.23 |
G2S2A2R1N2 | 42.39 | 0.28 | 252.25 | 0.62 |
G2S2A2R2N1 | 14.08 | 0.11 | 706.36 | 0.80 |
G2S2A2R2N2 | 115.74 | 0.61 | 207.21 | 0.77 |
G2S3A1R1N1 | 16.70 | 0.13 | 604.42 | 0.53 |
G2S3A1R1N2 | 72.54 | 0.58 | 305.93 | 0.41 |
G2S3A1R2N1 | 21.81 | 1.31 | 387.14 | 0.84 |
G2S3A1R2N2 | 56.01 | 1.07 | 527.88 | 0.69 |
G2S3A2R1N1 | 23.20 | 0.78 | 378.00 | 0.66 |
G2S3A2R1N2 | 14.50 | 0.11 | 151.30 | 0.10 |
G2S3A2R2N1 | 23.76 | 0.66 | 270.92 | 0.28 |
G2S3A2R2N2 | 29.86 | 1.02 | 249.28 | 0.80 |
Fragility parameters of G2 classification.
Examples of fragility curves of Chen-Yu-Lan watershed: (a) G1S3A1R1N1, (b) G2S2A1R1N1, (c) G1S3A1R2N1, and (d) G2S3A1R2N1.
The fragility curves of 48 classification slope units represented the local environmental characteristics of a given area. Instead of directly using 48 set fragility curves, it should be practical to obtain one set of representative fragility curve for a given site or location. To achieve this goal, the weighted fragility curves were introduced and applied to the Shenmu village. The weighted fragility parameters were determined using the following equations:
where
After the weighted calculation, the fragility parameters of Shenmu area are median
The fragility surface and fragility curves of Shenmu area.
The risk of landslide was demonstrated by using the critical values of rainfall hazard and landslide fragility. The concept of landslide warning was adopted in this study, and by combining both
The warning conditions based on landslide fragility (Fc) and rainfall hazard (Hc).
Cases of landslides and debris flows in Shenmu were collected from the disaster notices issued by Soil and Water Conservation Bureau of Taiwan. As shown in Table 11 and Figure 27, a total of seven cases were used to determine the critical values of
Year | Event | Disaster | Village | ||
---|---|---|---|---|---|
2009 | Typhoon Morakot | Debris flow, flood | Tongfu | 85.5 | 1130 |
2009 | Typhoon Morakot | Debris flow | Wangmei | 85.5 | 1130 |
2009 | Typhoon Morakot | Landslide | Shenmu | 47.5 | 829.5 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 42.5 | 750 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 33.5 | 641 |
2009 | Typhoon Morakot | Landslide | Shenmu | 20 | 476.5 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 38.5 | 877 |
2012 | 0610 Heavy rainfall | Debris flow, flood | Shenmu | 18.5 | 450.6 |
The disaster notices around Shenmu area.
The probability thresholds of rainfall hazard and landslide fragility in Shenmu area: (a) rainfall warning threshold and (b) landslide warning threshold.
The rainfall history of Typhoon Morakot in 2009 and 0601 Heavy Rainfall in 2016 were used to evaluate the landslide risk assessment in Shenmu. Figure 28 shows the results of event, and the dots in the figure represent the rainfall condition (hourly rainfall and cumulative rainfall) and the probability of hazard. It was noted that the dots behaved like a “snake” line going from Safe stage to Red I and Red II stages. Also, the snake line stayed shortly at Red I stage for both events and passed to Red II in a jump. This condition implied that when the situation was beyond the
The change of probability in Shenmu area during (a) Typhoon Morakot (2009) event and (b) 0601 heavy rainfall in 2016 (after [
This study had developed the landslide fragility curve model by using the spatial data and statistical methods. The fragility curves of the study area were derived for all combinations of environmental and triggering factors. The data sets included the geomorphological and vegetation condition factors, based on the landslides at the Chen-Yu-Lan watershed in Taiwan, during Typhoon Sinlaku (September 2008) and Typhoon Morakot (August 2009). This study also proposed landslide risk assessment using rainfall hazard potential and landslide fragility curves and concluded findings as follows:
Overall, the proposed model provides considerably accurate and reliable results on landslide estimations in terms of spatial distribution.
Adoption of slope unit was physically proper in modeling landslide locations.
The classifications of slope unit can be applied to different areas, and the fragility curve of each classification can be used directly.
The procedure of risk assessment was useful for practical landslide disaster preparation and prediction.
The LFC model was developed using two typhoon events. More events and landslide cases are needed to improve the LFC model in the future. Furthermore, the classification of upstream areas based on their environment is suggested for better possible estimation.
The applicability of factors should be considered before developing the model. The concerns about the model factors and the limits of satellite images can be resolved by using different methods to obtain necessary data. For example, the information of LIDAR may be used with the satellite images to provide better description on landslide identification. Therefore, the LFC model could be improved when more factors are available and applicable.
The authors would like to express their gratitude to research assistant Xingping Wang, for helping in collecting all the data relevant to the landslides in the Chen-Yu-Lan watershed. The authors also would like to thank the Soil and Water Conservation Bureau in Taiwan for supporting this research.
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In this chapter, I describe the similarities and differences between the naïve and primed pluripotent states as exemplified by mouse embryonic stem cells (mESCs), mouse epiblast stem cells (mEpiSCs), human embryonic stem cells (hESCs), and human induced pluripotent stem cells (hiPSCs). I also review the efforts for derivation of naïve human pluripotent stem cells by manipulating culture conditions during reprogramming of somatic cells and attempts to revert primed hESCs to the naïve state. Understanding the requirements for induction and maintenance of the naïve pluripotent state will facilitate studies on early human embryonic development and understanding the mechanisms involved in X inactivation in vitro. 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These cells remain in a quiescent state until they are activated by different factors, usually those generated by an alteration in the parenchymal tissue. These cells have characteristic membrane markers such as CD73, CD90, and CD105. Those are a receptor, which in response to their ligand induces strong changes in different metabolic pathways that lead to these cells, both to generate molecules with different activities and to leave their stationary phase to reproduce and even differentiate. This review describes the metabolic pathways dependent on these membrane markers and how they influence on parenchymal tissue and other stromal cells.",book:{id:"6658",slug:"stromal-cells-structure-function-and-therapeutic-implications",title:"Stromal Cells",fullTitle:"Stromal Cells - Structure, Function, and Therapeutic Implications"},signatures:"Maria Teresa Gonzalez Garza",authors:[{id:"181389",title:"Ph.D.",name:"Maria Teresa",middleName:null,surname:"Gonzalez Garza",slug:"maria-teresa-gonzalez-garza",fullName:"Maria Teresa Gonzalez Garza"}]},{id:"63044",title:"Stromal-Epithelial Interactions during Mammary Gland Development",slug:"stromal-epithelial-interactions-during-mammary-gland-development",totalDownloads:1372,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Mammary gland is an organ, which undergoes the majority of its development in the postnatal life of mammals. The complex structure of the mammary gland comprises epithelial and myoepithelial cells forming the parenchymal tissue and adipocytes, fibroblasts, vascular endothelial cells, and infiltrating immune cell composing the stromal compartment. During puberty and in adulthood, circulating hormones released from the pituitary and ovaries regulate the rate of development and functional differentiation of the mammary epithelium. In addition, growing body of evidence shows that interactions between the stromal and parenchymal compartments of the mammary gland play a crucial role in mammogenesis. This regulation takes place on a paracrine level, by locally synthesized growth factors, adipokines, and cytokines, as well as via direct cell-cell interactions. This chapter summarizes the current knowledge about the complex nature of interactions between the mammary epithelium and stroma during mammary gland development in different mammalian species.",book:{id:"6658",slug:"stromal-cells-structure-function-and-therapeutic-implications",title:"Stromal Cells",fullTitle:"Stromal Cells - Structure, Function, and Therapeutic Implications"},signatures:"Żaneta Dzięgelewska and Małgorzata Gajewska",authors:[{id:"165068",title:"Dr.",name:"Malgorzata",middleName:null,surname:"Gajewska",slug:"malgorzata-gajewska",fullName:"Malgorzata Gajewska"},{id:"249847",title:"Ms.",name:"Żaneta",middleName:null,surname:"Dzięgelewska",slug:"zaneta-dziegelewska",fullName:"Żaneta Dzięgelewska"}]},{id:"69757",title:"Flow Cytometry Applied to the Diagnosis of Primary Immunodeficiencies",slug:"flow-cytometry-applied-to-the-diagnosis-of-primary-immunodeficiencies",totalDownloads:1041,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Primary immunodeficiencies are the result of biological defects associated with functional immune abnormalities. It consists of a group of disorders showing a higher incidence and severity of infections, expression of immunological dysregulation such as inflammation and lymphoproliferation. The immunophenotyping and in vitro functional characterization of immunodeficient patients contribute, together with the clinical aspects, to define the underlying immune defect particularities. Flow cytometry applications in primary immunodeficiency assessment are multiple and include the study of a wide range of specific cell lymphocyte subpopulations. This chapter describes the main techniques used in the diagnosis of a wide variety of primary immunodeficiencies, in which intracellular proteins or activation markers involved in immunity are evaluated, as well as functional proliferation, cytokine production, phosphorylation of transcription factors, cytotoxic and degranulation capacity. Flow cytometry is a tool that allows rapid and accurate evaluation of multiple lymphocyte populations and immunological function, and this information is essential for the diagnosis and evaluation of patients with primary immunodeficiencies.",book:{id:"6913",slug:"innovations-in-cell-research-and-therapy",title:"Innovations in Cell Research and Therapy",fullTitle:"Innovations in Cell Research and Therapy"},signatures:"Mónica Martínez-Gallo and Marina García-Prat",authors:[{id:"286242",title:"Ph.D.",name:"Mónica",middleName:null,surname:"Martínez Gallo",slug:"monica-martinez-gallo",fullName:"Mónica Martínez Gallo"},{id:"286704",title:"BSc.",name:"Marina",middleName:null,surname:"García-Prat",slug:"marina-garcia-prat",fullName:"Marina García-Prat"}]},{id:"50685",title:"States of Pluripotency: Naïve and Primed Pluripotent Stem Cells",slug:"states-of-pluripotency-na-ve-and-primed-pluripotent-stem-cells",totalDownloads:4009,totalCrossrefCites:4,totalDimensionsCites:12,abstract:"Pluripotent stem cells are classified into naïve and primed based on their growth characteristics in vitro and their potential to give rise to all somatic lineages and the germ line in chimeras. In this chapter, I describe the similarities and differences between the naïve and primed pluripotent states as exemplified by mouse embryonic stem cells (mESCs), mouse epiblast stem cells (mEpiSCs), human embryonic stem cells (hESCs), and human induced pluripotent stem cells (hiPSCs). I also review the efforts for derivation of naïve human pluripotent stem cells by manipulating culture conditions during reprogramming of somatic cells and attempts to revert primed hESCs to the naïve state. Understanding the requirements for induction and maintenance of the naïve pluripotent state will facilitate studies on early human embryonic development and understanding the mechanisms involved in X inactivation in vitro. In addition, the development of naïve hiPSCs will improve the efficiency of gene targeting for the purpose of modeling human diseases as well as for generating gene‐corrected autologous pluripotent stem cells for regenerative medicine.",book:{id:"5207",slug:"pluripotent-stem-cells-from-the-bench-to-the-clinic",title:"Pluripotent Stem Cells",fullTitle:"Pluripotent Stem Cells - From the Bench to the Clinic"},signatures:"Daman Kumari",authors:[{id:"180527",title:"Dr.",name:"Daman",middleName:null,surname:"Kumari",slug:"daman-kumari",fullName:"Daman Kumari"}]}],onlineFirstChaptersFilter:{topicId:"990",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 15th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:48,paginationItems:[{id:"81799",title:"Cross Talk of Purinergic and Immune Signaling: Implication in Inflammatory and Pathogenic Diseases",doi:"10.5772/intechopen.104978",signatures:"Richa Rai",slug:"cross-talk-of-purinergic-and-immune-signaling-implication-in-inflammatory-and-pathogenic-diseases",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"81764",title:"Involvement of the Purinergic System in Cell Death in Models of Retinopathies",doi:"10.5772/intechopen.103935",signatures:"Douglas Penaforte Cruz, Marinna Garcia Repossi and Lucianne Fragel Madeira",slug:"involvement-of-the-purinergic-system-in-cell-death-in-models-of-retinopathies",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"81756",title:"Alteration of Cytokines Level and Oxidative Stress Parameters in COVID-19",doi:"10.5772/intechopen.104950",signatures:"Marija Petrusevska, Emilija Atanasovska, Dragica Zendelovska, Aleksandar Eftimov and Katerina Spasovska",slug:"alteration-of-cytokines-level-and-oxidative-stress-parameters-in-covid-19",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",subseries:{id:"18",title:"Proteomics"}}},{id:"81681",title:"Immunomodulatory Effects of a M2-Conditioned Medium (PRS® CK STORM): Theory on the Possible Complex Mechanism of Action through Anti-Inflammatory Modulation of the TLR System and the Purinergic System",doi:"10.5772/intechopen.104486",signatures:"Juan Pedro Lapuente",slug:"immunomodulatory-effects-of-a-m2-conditioned-medium-prs-ck-storm-theory-on-the-possible-complex-mech",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}}]},overviewPagePublishedBooks:{paginationCount:27,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science and Technology from the Department of Chemistry, National University of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013. She relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the National Institute of Fundamental Studies from April 2013 to October 2016. She was a senior lecturer on a temporary basis at the Department of Food Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is currently Deputy Principal of the Australian College of Business and Technology – Kandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI) Ambassador to Sri Lanka.",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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