Chemical composition of A514 [61]
\r\n\tThe fundamental research areas of Evolutionary Psychology can be divided into two broad categories: on the one hand, the basic cognitive processes, and the way they evolved within the species, and on the other, the adaptive social behaviors that derive from the theory of evolution itself: survival, mating, parenting, family and kinship, interactions with non-parents and cultural evolution. Indeed, Evolutionary Psychology explains at individual and group level the fundamental behaviors of social life, such as altruism, cooperation, competition, social exclusion, social support, etc. etc. Similar to the mechanisms of natural selection for physical characteristics, not only the mind follows biological laws, but also psychological abilities - such as the theory of mind, the ability to represent the intentions, thoughts, beliefs, and emotions of others - have had to adapt and must make themselves functional to the social life of individuals and groups. In addition, Sociology takes the same aspects into consideration, emphasizing the interaction, symbolic and otherwise, of individuals. The latter investigates the neural mechanisms underlying the same social behaviors that are of interest to evolutionary psychology. To study the neural correlates involved in such behaviors is necessary to understand the biological laws that underlie human behavior and brain functioning.
\r\n\r\n\tThis book aims to open a debate full of theoretical and experimental contributions among the different disciplines in social research, psychology, neuroscience, sociology, useful to give an innovative vision to the present research and future perspective on the topic.
",isbn:"978-1-83968-871-3",printIsbn:"978-1-83968-870-6",pdfIsbn:"978-1-83968-872-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"bd4df54e3fb185306ec3899db7044efb",bookSignature:"Dr. Rosalba Morese, Dr. Vincenzo Auriemma and Dr. Sara Palermo",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10450.jpg",keywords:"Evolutionary Psychology, Human Social Evolution, Human Social Behaviour, Social Cognition, Social Neuroscience, Functional Neuroimaging, Neuropsychology, Altruism, Cooperation, Social Exclusion, Social Support, Social Inclusion",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 18th 2020",dateEndSecondStepPublish:"December 21st 2020",dateEndThirdStepPublish:"February 24th 2021",dateEndFourthStepPublish:"May 15th 2021",dateEndFifthStepPublish:"July 14th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Rosalba Morese is carrying out research in the framework of Neuroscience and Social Psychology. She currently works at the Institute of Public Health of Faculty of Biomedical Sciences and at the Faculty of Communication, Culture, and Society of Università Della Svizzera Italiana, Lugano, Switzerland.",coeditorOneBiosketch:"Dr. Vincenzo Auriemma's focus is on the study of empathy in human interactions. He studied at the University of Essex in England, the University of Pisa, Genoa, Rome in Italy, and the University of Italian Switzerland in Switzerland. He is the principal responsible for the 'PERSEO' research which analyzes the reasons for the 'drop-out' in psychology.",coeditorTwoBiosketch:"Researcher of the EUROPEAN INNOVATION PARTNERSHIP on Active and Healthy Ageing and Assistant Specialty Chief Editor for Frontiers in Psychology - Neuropsychology.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"214435",title:"Dr.",name:"Rosalba",middleName:null,surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese",profilePictureURL:"https://mts.intechopen.com/storage/users/214435/images/system/214435.jpg",biography:"Rosalba Morese obtained a degree in psychology at the University of Parma. She subsequently held various\nteaching positions at the Department of Psychology and the Faculty of Medicine and Surgery of the\nUniversity of Parma.\nHer training continued with the attainment of the title of PhD in Neuroscience at the University of Turin,\nduring which she acquired and developed interdisciplinary skills and point of view through the application\nof bioimaging and psychophysiological methods to investigate the neurophysiological mechanisms involved\nduring communication and social interactions.",institutionString:"Universita della Svizzera Italiana",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Universita della Svizzera Italiana",institutionURL:null,country:{name:"Switzerland"}}}],coeditorOne:{id:"338363",title:"Dr.",name:"Vincenzo",middleName:null,surname:"Auriemma",slug:"vincenzo-auriemma",fullName:"Vincenzo Auriemma",profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",biography:'He is pursuing a PhD in Sociology from the University of Salerno, Italy. He is a researcher of sociology and neurosociology at the University of Salerno, Italy. His focus is on the study of empathy in human interactions and he studied at the University of Essex in England, the University of Pisa, Genoa, Rome 3 in Italy and the University of Italian Switzerland in Switzerland. He has participated in national and international conferences with about 25 reports/communications. He is the principal responsible for the "PERSEO" research which analyzes the reasons for the "drop-out" in psychology, using the methodology of the Gounded Theory and analyzing empathy, fear and panic. He is Co-Editor for Frontiers. He is also a member of the Italian Society of Sociology (AIS).',institutionString:"University of Salerno",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salerno",institutionURL:null,country:{name:"Italy"}}},coeditorTwo:{id:"233998",title:"Ph.D.",name:"Sara",middleName:null,surname:"Palermo",slug:"sara-palermo",fullName:"Sara Palermo",profilePictureURL:"https://mts.intechopen.com/storage/users/233998/images/system/233998.jpeg",biography:"Sara Palermo is a MSc in Clinical Psychology and a PhD in Experimental Neuroscience. Moreover, she obtained the National Scientific Enabling Certificate for Associate Professorship in April 2017 (ASN-2017). She is an expert in experimental neuroscience, clinical neuropsychology and advance neuropsychological testing. Moreover, she performs multidimensional geriatric evaluation and basic neurological symptomatology detection in patients with neurodegenerative disorders. She is also engaged in Activation Likelihood Estimation meta-analysis of neuroimaging studies.\r\nShe worked as a postdoc research fellow at the Department of Neuroscience 'Rita Levi Montalcini” in Turin until July 2017. Since then she works as research fellow at the Department of Psychology in Turin. To date, she owns three research Group memberships at the University of Turin (Italy). She is a member of the 'Center for the Study of Movement Disorders” (research area: Neurology) and the 'Placebo Responses Mapping Group” (research area: Physiology) at the Department of Neuroscience, and a member of the 'Neuropsychology of cognitive impairment and central nervous system degenerative diseases Group” at the Department of Psychology (Research Area: Psychobiology and physiological psychology).\r\nThe main topics of her research are the study of awareness of illness, metacognitive-executive deficits in neuropsychiatric and neurological disorders, physical and cognitive frailty in the elderly, and placebo/nocebo phenomena. Interestingly, all of them may represent appealing perspectives from which to study how neuropsychological abnormalities can be explained in terms of brain activities and with the use of neuropsychiatric and neuropsychological batteries considering a neurocognitive approach. Given her research interests and scientific publications, she has been an ordinary member of the Italian Society of Neuropsychology (SINP), of the Italian Association of Psychogeriatrics (AIP), of the Italian Society of Neurology for Dementia (SiNdem), and – finally – of the international Society for Interdisciplinary Placebo Studies (SIPS). Importantly, she is a member of the European Innovation Partnership on Active and Healthy Aging (EIP on AHA), for which she is involved in the Action Group A3 Functional decline and frailty. \r\n\r\nSara Palermo is Panel Editor for 'EC Psychology and Psychiatry'. She was recently appointed as Specialty Chief Editor for 'Frontiers in Psychology - Neuropsychology'.",institutionString:"University of Turin",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Turin",institutionURL:null,country:{name:"Italy"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"21",title:"Psychology",slug:"psychology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"259492",firstName:"Sara",lastName:"Gojević-Zrnić",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/259492/images/7469_n.png",email:"sara.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5810",title:"Socialization",subtitle:"A Multidimensional Perspective",isOpenForSubmission:!1,hash:"bfac2e9c0ec2963193e9d15d617c6a01",slug:"socialization-a-multidimensional-perspective",bookSignature:"Rosalba Morese, Sara Palermo and Juri Nervo",coverURL:"https://cdn.intechopen.com/books/images_new/5810.jpg",editedByType:"Edited by",editors:[{id:"214435",title:"Dr.",name:"Rosalba",surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7818",title:"Social Isolation",subtitle:"An Interdisciplinary View",isOpenForSubmission:!1,hash:"db3b513d7d35476f333a0d4a3147935b",slug:"social-isolation-an-interdisciplinary-view",bookSignature:"Rosalba Morese, Sara Palermo and Raffaella Fiorella",coverURL:"https://cdn.intechopen.com/books/images_new/7818.jpg",editedByType:"Edited by",editors:[{id:"214435",title:"Dr.",name:"Rosalba",surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8262",title:"The New Forms of Social Exclusion",subtitle:null,isOpenForSubmission:!1,hash:"29bf235aa7659d3651183fe9ea49dc0d",slug:"the-new-forms-of-social-exclusion",bookSignature:"Rosalba Morese and Sara Palermo",coverURL:"https://cdn.intechopen.com/books/images_new/8262.jpg",editedByType:"Edited by",editors:[{id:"214435",title:"Dr.",name:"Rosalba",surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6494",title:"Behavior Analysis",subtitle:null,isOpenForSubmission:!1,hash:"72a81a7163705b2765f9eb0b21dec70e",slug:"behavior-analysis",bookSignature:"Huei-Tse Hou and Carolyn S. Ryan",coverURL:"https://cdn.intechopen.com/books/images_new/6494.jpg",editedByType:"Edited by",editors:[{id:"96493",title:"Prof.",name:"Huei Tse",surname:"Hou",slug:"huei-tse-hou",fullName:"Huei Tse Hou"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40988",title:"Development of a Comprehensive Process Model for Hybrid Laser-Arc Welding",doi:"10.5772/45850",slug:"development-of-a-comprehensive-process-model-for-hybrid-laser-arc-welding",body:'Recently, a hybrid welding technique combining laser welding and arc welding has been finding broader applications in industry due to its unique advantages, such as higher welding efficiency and lower costs [1, 2] compared to the traditional arc welding or autogenous laser welding. Because of rapid melting and solidification occurring in the weld zone, a locally high thermal gradient inevitably exists and accompanies with the whole welding process, which really decides the final residual stress and distortion distributions of weld and affects the remained grain size in the fusion zone (FZ) and heat affected zone (HAZ). Also, the levels of residual stresses and distortions directly influence the weld quality [3]. In comparison with traditional arc welding and autogenous laser welding, the temperature field and residual stress distribution in hybrid laser-arc welding involve more variables because of the additional interaction between the laser and arc plasma [4] thus becoming much more complex and difficult to theoretically and experimentally analyze. Trial-and-error experiments are not able to fully describe those physical mechanisms involved in the hybrid laser-arc welding process. Therefore, the numerical tools have been widely used to help explain the complex welding mechanisms present in the hybrid laser-arc welding process [5, 6].
Up to now, numerical work on the welding process mostly concentrates on traditional electric arc welding, including gas tungsten arc welding (GTAW) [7 - 9], submerged arc welding (SAW) [10,11], gas metal arc welding (GMAW) [12-16], and partly on laser beam welding (LBW) [17-21]. These studies focused on the heat and mass transfer phenomena in the weld pool [8, 11-15], thermal-induced distortion and residual stresses [16, 17, 19, 20], solidification-induced dendrite growth in the FZ [21], and recrystallization in the heat affected zone (HAZ) [9]. Due to the locally rapid melting and solidification occurring in the welding process, a high temperature gradient—which inevitably exists in the weld zone—causes a high-stress concentration in the weld zone and nearby HAZ [22], which usually exceeds the yield strength of the material. Large residual stresses presented in the welded structure can obviously reduce the fatigue strength of metal components, causing crack generation and shorten the lifetime of metal component [23], which could possibly have disastrous results. A number of mitigation procedures to reduce and/or eliminate the level of residual stress have been presented by researchers, mainly including enhancement of the material ductility of solidification zone (SZ) and HAZ, and improving the thermal and mechanical conditions in the welding processes [24].
Because of the complex physical mechanisms in the welding process—which are related to the heat source properties, material performance, and welding parameters, etc.—trial-and-error methods to optimize welding parameters takes a long time and is usually more costly. Also, understanding of the physics of the welding process is limited by only using an experimental approach. Numerical simulation as accompanied by theoretical analysis has been widely applied as a cost-efficient way to help explore the welding phenomena in different welding techniques. Eagar et al. [25, 26] spent a lot of time in developing theoretical models for GTAW processes. Dong et al. [27-29] developed numerical models to predict the residual stresses as well as fatigue life of weld obtained by the multi-pass welding process. Deng et al. [30-32] developed a series of numerical models to study the residual stress distribution in variable welding joints.
Compared to the traditional electric arc welding, laser welding has unique advantages such as high energy density, narrow HAZ, low heat input, and high energy efficiency. However, laser welding is also limited by its disadvantages like poor gap bridgeability and high equipment cost. In order to fully use the advantages of both laser and arc welding techniques, Steen et al. [33] introduced for the first time a hybrid technique by combining the laser beam and arc for welding and cutting in the late 1970s. Subsequently, researcher and engineers have presented a number of works on combining the laser and electric arc in the past decades. Considering that interaction between laser beam and arc plasma is complex, the hybrid laser-arc welding and cladding processes have not been understood fully. Most available literature on these approaches is limited at the level of the experimental study including hybrid laser-GTAW, hybrid laser-GMAW, and hybrid laser-plasma arc welding for steels, magnesium alloy, aluminum alloy, titanium alloy and dissimilar materials. In order to further study the welding mechanism of hybrid laser and arc, it is necessary to develop a comprehensive model to understand the heat and mass transfer, residual stress evolution, as well as microstructure formation in the hybrid laser-arc welding process. Zhou and Tsai [34, 35] presented heat transfer and fluid flow models to study the metal inert gas (MIG) welding and laser-MIG hybrid welding processes. Rao et al. [36] reviewed the modeling of hybrid laser-gas metal arc (GMA) welding and presented further studies on synergistic interaction between the laser beam and arc, the metal transfer features, and behavior of shielding gas. Ribic et al. [37] developed a three-dimensional (3-D) finite volume model to study heat transfer and fluid flow in the hybrid laser-GTA welding process. Considering that the microstructure formation of weld has a close relationship with the macro-scale heat transfer and fluid flow, and residual stress fields, it will be very necessary to integrate the thermal, fluid flow and mechanical modeling with the microstructure evolution like grain growth in the fusion zone and HAZ. Multi-scale and multi-physics modeling is one of most interesting simulation trends in the laser-based heat processes, especially in the hybrid laser-arc welding process.
Schematic view of hybrid laser-GMA welding system
In this chapter, a 3-D mathematical model will be developed to numerically predict the transient temperature distributions and residual stresses in the hybrid laser-GMA welding of a thick plate of A514 steel in butt joint configuration, as shown in Figure 1. The numerical solution is achieved based on a finite element method by using a commercial numerical package, ANSYS. A Monte Carlo model is introduced to consider the grain growth and phase transformations in the HAZ. The laser and arc heat inputs and heat losses at the surface of coupons are considered by using ANSYS Parametric Designed Language (APDL). The influences of the processing parameters (including welding speed, laser power, wire feed rate, arc power, and stand-off distance from laser to arc) on the profile and geometrical size of the molten pool, residual stress distribution of the weld, and grain size in the HAZ are numerically studied. The numerically obtained results are experimentally verified.
In the hybrid laser-GMA welding process, laser and GMA simultaneously heat the coupon surface in local area, which makes the thermal distribution of weld much more complex. In this study, a cylindrical volume heat-source model with a Gaussian distribution is assumed to simulate the heat input by laser, and a double-ellipsoidal volume heat source is selected to consider the heat input by GMA welding. The general thermal governing equation is shown below, in which thermal conduction-induced heat transfer is considered and temperature-dependent material properties are used [38].
ρ is the density, cp is the specific heat, T is the temperature, t is time, kxx, kyy, and kzz are the thermal conductivity components along the x, y, and z axis, respectively;
So far, a number of heat source models have been developed to simulate the arc welding and laser welding processes. Laser welding usually consists of laser conduction welding or laser keyhole welding. The former one has lower energy density as compared with the latter one by which a keyhole is formed in the weld pool. A surface heat flux model is usually applied in the thermal analysis of laser conduction welding. However, a volume-distributed heat source model, like rotary Gaussian heat density distribution [39], is usually used for simulating a laser keyhole welding. Compared to the laser beam welding, electric arc welding has much lower energy density, and surface heat flux models with Gaussian distributions used to be applied to simulate the arc heat input in the arc welding process.
Considering that the enthalpy brought into the weld pool by melted wire in GMAW, volume-distributed heat source models are preferred, such as hemi-spherical power density distribution [40], ellipsoidal power density distribution [41], and double ellipsoidal power density distribution [42]. However, all of these heat source models are empirically derived based on the experimentally fitting data. Therefore, each heat source model mentioned above has a certain applicable range in the real production case. It is suggested that engineers in the welding process design should reasonably select a heat source model which matches well with the specific welding process. There are a limited number of publications available to numerically describe the hybrid laser and arc welding process because of lack of knowledge on complex interaction between the material, arc plasma, and laser beam [43]. Current heat source models of hybrid laser and arc including GTAW and GMAW were mostly developed with the help of experimental support [34-37].
In this study, a double-ellipsoidal heat source model is introduced to simulate the GMAW heat input, and a cylindrical heat source model with a sectional Gaussian distribution is used to consider the laser heat input. qfarc (x, y, z, t) and qrarc (x, y, z, t), depict heat input distributions inside the front and rear quadrants of the GMAW heat source, respectively, which can be expressed as follows [42, 44]:
where a, b, cf, cr are the characteristic parameters of heat sources, and a, b, cf, and cr are set at 4 mm, 3 mm, 3 mm and 7 mm respectively [44]. Parc denotes the nominal power of the GMAW, and Parc=µUI. Where µ is the energy efficiency of GMAW based on the welded metal, U denotes the arc voltage of GMAW, and I stands for arc current of GMAW.
where ηl is laser absorption efficiency based on the welded material, Plaser stands for the nominal power of the laser beam, x0 is the x-coordinate of the center point of laser spot at the coupon surface, Lw is the thickness of the butt joint, and Rl is the effective radius of the laser beam, Φ is the inclination angle of laser head, Dla is the laser-to-arc stand-off distance. Φ is set at 0o and Dla is set at 8 mm in this study, and v denotes the welding speed.
The boundary conditions at the sample surfaces are given by:
where n is the normal outward vector to the surface of specimen,
The mechanical analyses of hybrid laser and arc welding are similar to the previous studies on the electric arc welding and laser welding. The stress and distortion of weld are mainly caused by the thermally-induced expansion and shrinkage and the accompanying phase transformation-induced volume change. By considering the elastic-plastic material properties, stress and strain relationships in the hybrid laser-GMA weld are given by [38]:
where {σ} denotes the stress vector, [De] denotes elastic stiffness matrix, and {εel }denotes elastic strain vector expressed by [30]:
where {ε} is the total strain vector, {εth} is thermal strain vector, {εpl} is the plastic strain vector, {εΔV} is strain vector due to phase transformed induced volume change, and {εTrp} is strain vector due to phase transformation plasticity which is ignored in this study. The boundary conditions taken into consideration in the mechanical analysis assume that one edge of the butt joint is fixed, and the other one is only transversely shrinkage free.
Grain size evolution and phase transformation play a critical role in deciding the final mechanical properties of weld, and it is necessary to involve those factors in the thermo-mechanical modeling of different welding processes. Many good trials have been performed to numerically predict the grain growth in the fusion zone and heat affected zone for solidification and re-crystallization, respectively, which includes Monte Carlo (MC) model [47-50], phase field (PF) method [51], and cellular automaton (CA) model [52] combined with finite element and finite difference analyses. Here a brief introduction of MC model to predict the grain growth in HAZ will be performed. The detailed description of phase field method and cellular automaton model-based numerical prediction of grain growth in welds can be found in literature [53].
Monte Carlo model-based grain growth prediction generally includes the following several steps: (1) The representation of the considered material in a two-dimensional (2-D) or 3-D of cells, as shown in Figure 2a. The content of each cell stands for its crystallographic orientation. A region consisting of a set of consistently distributed cells with the same orientation value denotes a grain. The grain boundaries are identified by a curve in 2-D matrix or a surface in 3-D matrix between the separate planes or volumes with different orientations. (2) After selecting the matrix type and defining it by an initially random number, the free energy of a cell in the matrix with its specific crystallographic orientation based on its surroundings will be identified. (3) Randomly selecting a new crystallographic orientation for each cell. (4) Calculating the free energy of the new coming element with the new crystallographic orientation, the two energy values and their difference are then calculated. A new grain orientation that will minimize the free energy is generated with the selected transition probability [54]. These four steps will be reiterated many times at random positions in the matrix. The ultimate product is a microscopic simulation of the free energy decaying in the system, which is in fact the main driving force for grain growth. The Hamiltonian demonstrates the interaction among the closest neighbors in a particular cell, which stands for the grain boundary energy and can be calculated as follows [54]:
where, J is a positive constant that characterizes the scale of the grain boundary energy;
The grain structure in MC model with Moore neighborhood [51](a) The grain structure represented by a 2D square (b) Moore neighborhood
The transition probability W is given by [55]:
where ΔG is the change of the free energy because of the orientation alteration,
where C1 is the boundary mobility. For a continuous grain growth, the final grain size can be calculated by using the following equation [55]:
where L and L0 are the final and initial mean grain sizes respectively calculated by the linear-intercept method, n is the grain growth exponent and set at 1.84 in this study [56]. f(T) is usually computed as an Arrhenius-type equation [55], and its expression is shown as follows:
where K is the pre-exponential coefficient, Q is the activation energy for grain growth, and Rg is the universal gas constant. In this study, K is set at 3.01×10-2, and Q is set at 1.7×105 J/mol [56].
The Monte Carlo method has been proven to be an effective way to simulate grain growth with slow and uniform temperature evolution such as metal casting [53]. In the hybrid welding by laser and arc, there exists a dynamic thermal process with rapid heating and cooling resulting in an abrupt temperature gradient in the HAZ and fusion zone. In the simulation of microstructure evolution, three techniques—such as the atomistic models, a grain boundary migration (GBM), and experimentally data-based (EDB) models—have been presented [53, 58-60]. The atomistic model used to be only applied to small numbers of atoms like nanocrystals [60], and it is not suitable for a large-scale FZ or HAZ simulation. The GBM model can be a good alternative for grain-growth simulation when the isothermal grain-growth kinetics is not accessible. However, the physical properties of the material in this model have to be known, and the grain size is assumed to be proportional to the square root of time. The EDB model can avoid these shortages and be applied to simulate the grain growth in HAZ when the isothermal grain-growth kinetics of metal are available. Therefore, it can be used to relate time and temperature to the Monte Carlo simulation-time step tMCS [55]:
where λ is the discrete grid-point spacing in the Monte Carlo model, and K1 and n1 are constants. Through the regression computation of tMCS and the Monte Carlo model predicting the grain size, the values of K1 and n1 are obtained as 0.715 and 0.477, respectively [56]. In the EDB model, the relationship between the tMCS and the real time-temperature T(t) is further given by [60]:
where n is the grain growth exponent, T(t) is the mean temperature in a time interval Δti. Therefore, at any given monitoring location where the temperature is known as a function of time, tMCS can be related to the real time t, which is ∑Δti. The tMCS values at different locations calculated through Eq. (16) cannot be straightly applied to the Monte Carlo model since the selection of a grid point for updating the orientation number is stochastic in the Monte Carlo approach. Consequently, the probability of choosing each grid point is the same as in the traditional MC calculations. However, grains usually grow at higher rates in the HAZ region of higher temperature, where a sharp temperature gradient is present. This fact has to be included in any practical grain-growth calculation scheme. One solution is to develop a scheme in which grain orientations at higher-temperature locations (higher tMCS locations) are updated with a higher frequency by considering a probability gradient. In other words, the site-selection probability changes with location. The larger the tMCS at a location, the higher the corresponding site-selection probability [57, 60]:
where tMCSMAX is the maximum of tMCS in the simulation domain.
Finite element meshes for hybrid laser-GMA weld
Temperature-dependent thermal and mechanical properties of A514 steel
Numerical procedure performed in the thermo-mechanical FE analysis
An uncoupled finite element thermo-mechanical model with considering the grain growth in the HAZ by Monte Carlo model is developed to study the temperature distribution and residual stress field in the hybrid laser-GMA welding process. A non-uniform mesh is selected in which a finer mesh is used in the weld bead and a course mesh is defined in the other region of the welded coupons (see Figure 3). A temperature-dependent material property is used in the numerical modeling, as listed in Figure 4. A thermal FE analysis is performed to achieve the temperature field of hybrid laser-GMA welding process. The wire feeding into the groove to form the weld bead has been simulated by using element kill-and-birth approach which is available in ANSYS software. The achieved geometrical size of the weld zone could be compared to the micrographs of the weld cross-section obtained by an optical microscope, by which the accuracy of thermal analysis can be verified. The numerical model is then transferred to mechanical analysis module in ANSYS by switching the element type from thermal to structural. The corresponding constraints are exerted into sample boundaries. The achieved temperature histories are subsequently loaded into the mechanical model step by step to calculate the displacement, stress/strain of the sample due to the thermal expansion or shrinkage during the welding process. A bilinear hardening principle is introduced in this study to simulate the material plastic behavior. Von Mises criterion is used for considering the yield behavior of the sample material. Figure 5 shows the numerical procedure used in this study.
A 4 kW fiber laser and a GMAW torch are mounted on a robotic arm to perform the hybrid welding of a thick plate for a butt joint configuration. The photo of an experimental set-up for welding is shown in Figure 6. In order to control the gap thickness, spot welding is performed at both ends of the joint before the formal welding starts. After the welding process is completed, the achieved sample will be cut into standard tensile coupon for tensile test, the left parts will be mounted for polishing, and etching to test the micro-hardness and microstructure. The base metal is high strength steel A514; its chemical composition is listed in Table 1. The wire material is ER100S-G. Its diameter is 0.9 mm, and its chemical composition is listed in Table 2. Residual stresses were measured by using the X-ray diffraction technique. Before performing the residual stress measurement, the measurement areas were cleaned by using polishing paper.
C | Mn | P | S | Si | Cr | Mo | V | Ti | B | |
Min. | 0.12 | 0.70 | 0.20 | 0.40 | 0.15 | 0.03 | 0.01 | 0.0005 | ||
Max. | 0.21 | 1.00 | 0.035 | 0.008* | 0.35 | 0.65 | 0.25 | 0.08 | 0.04 | 0.005 |
Chemical composition of A514 [61]
Cu % max | Ni % max | Fe % max | Mn % max | Mo % max |
<0.5 | <5.0 | Balance | <5.0 | 0.50 |
Chemical composition of ER100S-G [62]
Photo of hybrid laser-GMA welding system used in this study
Figure 7 shows the surface morphology of A514 weld obtained by hybrid laser-GMA welding, and Figure 8 presents the corresponding cross-sectional view of weld. It can be seen that a sound weld quality is achieved by using hybrid laser-GMA welding and the welding-induced cracks can be effectively mitigated by reasonably selecting filler wire matched with base metal. Also, a sound mechanical property can be obtained. Figures 9a and b show the hardness distribution in the weld obtained by hybrid laser-GMA welding.
A514 sample achieved by 3.8 kW laser and 159A×30.5V GMAW with a welding speed of 12 mm/s and 8-mm stand-off distance between the laser and arc
Cross-sectional view of A514 weld sample achieved by 3.8 kW laser and 159A×30.5V GMAW with a welding speed of 12 mm/s and 8-mm stand-off distance between the laser and arc
Finite element analyses results show the temperature at the each location of weld with respect to the welding time. The temperature evolution curves at position A, B, and C are shown in Figure 10, where position A is located at the center of weld, position B is at the heat-affected zone, and position C is in the base metal, as shown in Figure 11b. Figures 11a and b show the top and cross-sectional views of weld obtained by hybrid laser-GMA welding, respectively. It is inevitable that material heating and cooling is accompanied by phase transformation and grain size change, especially in the HAZ of weld, which is the weakest zone of the weld. In this study, a Monte Carlo-based sub-model is introduced to numerically predict the grain growth in the HAZ combined with the finite element thermal analysis. Figure 12a shows the relationship of temperature versus Monte Carlo step at Position B, and Figure 12b presents the curve of Monte Carlo step versus real time at Position B. The corresponding predicted grain size distribution in the Position B is shown in Figure 12c. The numerically predicted grain size is compared to grain size shown in the micrograph of the cross-section of weld (see Figure 12d), and a qualitative agreement is achieved.
Hardness distribution transverse to the weld in A514 sample, (a) at the top surface and (b) at the bottom surface
Temperature evolution curve at the FZ, HAZ and BM during the hybrid laser-GMA welding of A514 steel
Numerically predicted isotherms at the top (a) and at the cross-section I-I of the weld (b) obtained by hybrid laser-GMA welding
a) Temperature versus Monte Carlo step, (b) Monte Carlo step versus real time, (c) numerical predicted and (d) experimentally measured grain size distributions at Position B in the HAZ of hybrid A514 weld by Monte Carlo sub-model
A finite element analysis is further performed to predict the thermally-induced residual stress distribution based on the previous thermal analysis results. The contours of transient stress, along thickness normal stress, longitudinal stress and equivalent residual stress of hybrid weld are shown in Figures 13a through d, respectively. It can be seen that the higher stress concentrations are located at the weld zone, which also indirectly verifies the previously experimentally obtained conclusions that the thermally induced cracks are usually generated at the weld zone, not in the base metal. The corresponding contours of stress distribution of the cross-section in the middle of weld length are shown in Figures 14a through d. A higher stress concentration is found to be located at the top region of cross-section
Figures 15a through c show residual stress distribution transverse to the weld bead at the different thicknesses in the middle of weld obtained by hybrid laser-GMA welding. It also validates the conclusion driven from Figure 14 that high tensile transverse and longitudinal stresses are located at the top and bottom regions of the weld center, high compressive transverse stresses are located at a half of the weld thickness. From the equivalent stress distribution point of view, the peak value of stress concentration is a little lower than that at the top and bottom of the weld. Figure 16 shows residual stress distribution transverse to the weld bead at the different locations along the top surface of weld obtained by hybrid laser-GMA welding. Figure 17 also shows residual stress distributions along the central line at the top surface of weld centerline achieved by hybrid laser-GMA welding. It is clear that stress distribution across the weld bead is uniform along the weld; only a little drop in stress magnitude exists at the both ends of weld.
a) Transverse stress SX, (b) along-thickness normal stress SY, (c) longitudinal stress SZ, and (d) von Mises equivalent residual stress SEQV mapping of weld by hybrid laser-GMA welding (unit of stress in the contour is Pa)
a) Transverse stress SX, (b) along-thickness normal stress SY, (c) longitudinal stress SZ, and (d) von Mises equivalent residual stress SEQV mapping of cross-section of weld by hybrid laser-GMA welding (unit of stress in the contour is Pa)
Residual stress distribution transverse to the weld bead at the different thicknesses in the middle of weld (z=30 mm) obtained by hybrid laser-GMA welding
Figure 18 shows a comparison of experimentally-measured and FE numerically-predicted residual stress distributions at the middle of weld length of top surface of weld by hybrid laser-GMA welding. There is a qualitative agreement between the developed numerical model and experimentally measured stress by an X-ray diffraction technique. Figures 19, 20 and 21 show the transverse, longitudinal, and equivalent stresses as well as temperature evolution with time at the positions A, B and C, respectively. It can be seen that the peak values of transient stresses at positions A, B, and C are sensitive to the temperature curve at the position A but not at the positions B and C.
Residual stress distribution transverse to the weld bead at the different locations along the top surface of weld obtained by hybrid laser-GMA welding
Residual stress distributions along the longitudinal direction of weld at the top surface of weld centerline achieved by hybrid laser-GMA welding
Comparison of experimentally-measured and FE numerically-predicted residual stress distribution at the middle of weld length of top surface of weld by hybrid laser-GMA welding
Stress and temperature evolution curves at the Position A in FZ of hybrid A514 weld
Stress and temperature evolution curves at the Position B in HAZ of hybrid A514 weld
Stress and temperature evolution curves at the Position C in BM of hybrid A514 weld
The hybrid approach combining laser and arc has unique features which definitely help to achieve a better weld quality and to improve the production efficiency. A brief overview of modeling of hybrid laser-arc welding process has been presented in which heat transfer, fluid flow, residual stress and distortion, as well as phase transformation in the weld zone and heat affected zone, are involved. As a case study, a 3D thermo-mechanical finite element model is developed to study the thermally-induced residual stress in the hybrid laser-GMA welding process. A Monte Carlo model is introduced to numerically predict the grain growth in the heat affected zone of weld combined with finite element thermal analysis, which can be used to further understand the welding mechanisms of hybrid laser-GMA welding as well as other welding technology.
The study of physical hydraulic models plays a role which is vital in the planning and designing of almost all hydraulic and hydrologic structures. May it be the stilling basins, spillways of barrages, river training works, hydraulic siphons, or even simple bridges, they are generally designed, evaluated, refined, and improved on the basis of physical hydraulic model studies. Physical model studies are comparatively expensive, costly, consume lots of time and resources to build and operate, and require technical labor and expertise in developing and testing the model. The selection of appropriate scale ratios between prototype and model plays a very significant and imperative role for the reliability and rationality of the results obtained.
\nResearchers and engineers working in the field, face a real challenge once they have to finalize on the basis of physical and/or numerical models, the rehabilitation and modernization works for any already constructed and operational hydraulic structure. The success of any rehabilitation work depends upon the precise and accurate identification of hydraulic and hydrologic problems on the prototype structure, because any failure may lead to partial or complete wastage of huge investments.
\nThe laws of similitude enable a researcher to predict the likely performance of prototype hydraulic structures from tests made with far less expensive models. We need not use the same fluid for the model as the prototype. We may obtain valuable results at a minimum cost from the tests conducted on the small scale hydraulic models. Any textbook on hydraulic physical modeling will tell us that the following similarities have to be ensured between the model and the prototype hydraulic structure [1, 2].
\nModel and prototype should have identical shapes but differ only in size as per the defined scale ratio. This would ensure geometrically similar flows. Under certain conditions, distorted models are resorted to by having different scale ratios for the lateral, longitudinal, and vertical directions, but then the same has to be incorporated during the interpretation of results.
\nRatios of the velocities on all corresponding points on the model and prototype hydraulic structure should be the same to ensure the same kinematics of flow.
\nThe quantum and direction of all forces acting on the corresponding points on the model and prototype should be in the same ratio, to ensure the same dynamics of the flow. Dynamic similarity can also be ensured by ensuring similarity of the combination of forces, by following the Froude Law, Reynolds Law, Mach Law, etc., for modeling.
\nPhysical modeling of hydraulic structures has been in use since the times of Leonardo Da Vinci. However, since then this art and science have gone manifold changes, developments, and positive improvements. Such models provide a visual insight into the hydraulic phenomena of water and fluid flows. These models also provide technical flow data through the elaborate system of instrumentation provided. The data and flow visuals can be recorded for future reference, computations, training materials, and records.
\nThe role of hydrological modeling has been well described in [3], wherein the authors reiterate that hydrological models are in fact basic, theoretical, and physical representations of the hydrologic cycle, and these are often used for the understanding and prediction of hydrological processes. They categorize the hydrological models as (a) models which are based on data collection, and (b) black-box models which are based on process description.
\nBecause of the importance and special role of physical hydraulic modeling, various renowned organizations have developed their physical hydraulic research centers. The most common and well-known are the Waterways Experiment Station (WES) of the US Army Corps of Engineers and Hydraulic Research Station (HRS) of Punjab Irrigation Department, Pakistan.
\nThe US Army Corps of Engineers Waterways Experiment Station (WES) was created in 1929 to provide support for the vast flood control plan for the entire lower Mississippi valley after the tragedy of the 1927 most horrific river flood. The WES laboratory complex located at Vicksburg, Mississippi is now the principle research, testing, and development facility, which supports studies in many other fields in addition to its primary field of hydraulic engineering. WES provides services for training, and technical assistance, research, and also software development, which reflects the state-of-the-art expertise of WES in hydrologic engineering and closely associated fields of planning analysis. In its research and development work, WES uses more application of model experiments employing the principles of hydraulics. WES has made a significant contribution through the publication and distribution of its research reports.
\nHydraulic Research Station, located at Nandipur near Gujranwala, in Pakistan is one of the largest research laboratories in the world. This field research station was established in 1926 and is under the administrative control of the Irrigation Research Institute, Lahore being its field station. The Nandipur station has 40 hectares of land divided into 22 research bays commonly called as research trays. Through a small irrigation channel, the water availability of 15 cumecs and a gravity head of 4 meters is provided, however for higher heads pumping facility is also available. The Nandipur Hydraulic Research Station meets the requirement of the study of numerous problems that are related to planning, operation, and management of water resources. Physical models for almost all the major irrigation and hydraulic structures now present in the country have been run, tested, and optimized at this station.
\nHydraulic Research Station at Nandipur has carried out model studies of almost all major hydraulic engineering projects undertaken in Pakistan and India in the pre-partition as well as the post-partition era. The major projects of Mangla Dam and Tarbela Dam which were constructed as part of the Indus Basin Treaty were also modeled in this facility. Many other barrages, weirs, link canals, and river training works have been modeled and approved prior to the finalization of their designs. A sample of the physical hydraulic modeling projects undertaken by the Hydraulic Research Station is displayed in Figures 1 and 2.
\nFlow from Flip Bucket Energy Dissipater.
Model of a Typical Barrage.
In the recent past, the rationality of the massive hydraulic structure of Jinnah Barrage [4, 5] was questioned as a model study indicated that at existing conditions of water levels the formed hydraulic jump was located on the glacis only up to a discharge of 400,000 cusecs. The hydraulic performance of the barrage, under-sluices, silt excluders, and also the subsidiary weir was yet not tested at higher discharges. Mahboob [6, 7] reviewed the design of Kalabagh Barrage and he found it acceptable only after the physical hydraulic model study because the hydraulic modeling study for energy dissipation under the conditions of existing water levels pointed out that hydraulic jump over the horizontal floor was repelled by the excessive lowering of the channel bed at the downstream (retrogression) (Figure 3).
\nModel Study of Taunsa Hydro Power Project.
The hydraulic modeling study cited here targets to examine sedimentation aspects of two cascade reservoirs on Poonch River; with the help of physical modeling and numerical simulation. A physical model of Poonch River was prepared at Nandipur Research Institute to study the sediment transport behavior [8]. After the base test, the model was used to get data for various scenarios of sediment flushing in the cascade reservoir system. The River geometry, riverbanks, hydraulic structures, cross-sections, and other physical attributes of the river were prepared from a topographic survey using AutoCAD. These files were used in HEC-RAS and BASEMENT for simulations (Figure 4).
\nModel Study of Poonch River Sedimentation Project.
Delta profile and flushing were modeled by HEC-RAS 5.0. The simulation showed that the life of the un-sluiced Gulpur HPP is about 14–15 years and that of Rajdhani is about 35 years. To enhance the life of the project, annually 4–5 days are required for flushing with an optimized discharge of about 250 m3/s. Model verification was performed by calculating the bed topography and flushing efficiency. The results obtained through the model were consistent with bed changes, demonstrating its suitability for the regeneration of regression channels and lateral erosion (Figure 5).
\nModel Study of Poonch River Sedimentation Project.
Other techniques in addition to physical hydraulic modeling available to a researcher are mathematical modeling, statistical modeling, and numerical modeling. With the advent of modern computers having speedy and fast processors, massive data storage, better data management software, and intelligent computational techniques the statistical modeling and numerical modeling have become the favorites of every researcher and engineer. The cutting edge graphics cards and attractive presentation techniques have also added to the magnetism of such indoor modeling. However, despite all this, the value and importance of physical hydraulic modeling cannot be overshadowed by these. The natural intricacies, physical behavior, the kinematics and dynamics of all fluids and especially large mass flows of water can only be studied through physical modeling.
\nWith the innovation of new materials of construction including the nano-materials, the physical hydraulic modeling has been revived. Now very intricate designs can be created and manufactured using new and modern materials. The same is true for hybrid and very strong epoxies and sealing materials which now help in making watertight models. Fabrication of models and their miniature parts has also been revolutionized by laser cutting, computerized numerical machines that can make precision model parts.
\nRevolution in measuring instruments for all hydraulic parameters has also provided a quantum jump to physical hydraulic modeling. Doppler velocimetry, very sensitive and accurate probes and pressure transducers, laser leveling gauges, and other such instrumentation can now be used to obtain and collect very sophisticated data for physical hydraulic models.
\nThe latest techniques in flow visualization have done wonders in fluid mechanics and hydraulic modeling. Modern electronics and advancement in graphics, optics, and sensors has revitalized the hydraulic modeling and made it an advanced and modern field of science and technology.
\nOn the other hand, the models based on process description also called deterministic models are rather complicated as compared to the stochastic hydrological models representing surface runoff, channel flow, subsurface flow, and evapotranspiration. Such models cannot by physically modeled, and therefore these have to be computer modeled [3].
\nThe art, science, and technique of planning, construction, and operation of physical hydraulic modeling are losing the race against numerical and computer modeling. However, there is a dire need that due to its very special place in research and investigation, this modeling technique should remain in vogue. For this very purpose its education, teaching, and engineering practice may be included in the curricula of various universities, colleges, and other technical training institutes.
\nFor very important and significant hydraulic structures, the failure of which cannot be afforded due to various reasons, it may be made mandatory that physical hydraulic modeling is carried out prior to the finalization of designs of construction and rehabilitation.
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\\n\\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
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\n\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
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\n\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\n\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
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