\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{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"}]},book:{item:{type:"book",id:"6532",leadTitle:null,fullTitle:"Drilling",title:"Drilling",subtitle:null,reviewType:"peer-reviewed",abstract:"With regard to depleted oil and gas resources, increasing world energy demands and volatile economic and political world scenarios, oil and gas industry players are working very hard to find ways to cut exploration and production costs to sustain and develop the industry to provide the world with cheap energy without harming the environment. Therefore, this book intends to provide readers with a comprehensive overview of the current state of the art in drilling, such as advanced drilling operations and techniques used by the industry, particularly in floating, underbalanced drilling, smart drilling fluid, intelligent drilling, drilling optimization, and future drilling technology and development.",isbn:"978-1-78984-304-0",printIsbn:"978-1-78984-303-3",pdfIsbn:"978-1-83881-531-8",doi:"10.5772/intechopen.71179",price:119,priceEur:129,priceUsd:155,slug:"drilling",numberOfPages:192,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"3bb91a4e4eb17b4395091940cf1c36fe",bookSignature:"Ariffin Samsuri",publishedDate:"October 31st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6532.jpg",numberOfDownloads:11638,numberOfWosCitations:7,numberOfCrossrefCitations:14,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:23,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:44,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 28th 2017",dateEndSecondStepPublish:"October 19th 2017",dateEndThirdStepPublish:"December 18th 2017",dateEndFourthStepPublish:"March 8th 2018",dateEndFifthStepPublish:"May 7th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"120519",title:"Prof.",name:"Ariffin",middleName:null,surname:"Samsuri",slug:"ariffin-samsuri",fullName:"Ariffin Samsuri",profilePictureURL:"https://mts.intechopen.com/storage/users/120519/images/system/120519.jfif",biography:"Professor Dr. Ariffin Samsuri is a Senior Professor of the Petroleum Engineering, Universiti Teknologi Malaysia (UTM) with vast experience in managing R&D projects, faculty and department. He had more than 36 years teaching and supervising experiences in petroleum engineering including academic program development, staffing, faculty, and facilities establishment. He also had been appointed as a visiting professor at Universiti Teknologi Petronas, peer reviewers, external examiners, expert and academic advisory panels. He has published 142 technical papers for conferences/seminars and journals, authored 6 books, 3 book chapters, translated 4 books, edited 11 books and 5 research monographs. He also involved in more than 30 research projects in production optimization, rock mechanics, wellbore stability, well stimulation, cement and cementing, drilling and drilling fluid, biofuel and nanotechnology application in oil & gas.",institutionString:"University of Technology Malaysia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Technology Malaysia",institutionURL:null,country:{name:"Malaysia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"708",title:"Geotechnical Engineering",slug:"engineering-civil-engineering-geotechnical-engineering"}],chapters:[{id:"60796",title:"Proposing a Patent Information Approach for Identifying Technological Trends in the Brazilian Upstream Oil and Gas Industry",doi:"10.5772/intechopen.75377",slug:"proposing-a-patent-information-approach-for-identifying-technological-trends-in-the-brazilian-upstre",totalDownloads:923,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In recent years, Brazil has emerged as a leading offshore producer with extensive proven reserves yet to be explored. As a matter of fact, the discovery of huge oil deposits in the pre-salt layer of the country’s Southeastern coast is motivating oil and gas exploration in great depths in Brazil, thereby also generating increasing demand for drilling capabilities. This study addresses the technological implications of this discovery by examining patent information. Here, we provide empirical evidence indicating an increased interest for patenting technologies designed to enhance not only ultra-deep drilling capabilities and build and maintain oil wells, but also technologies to increase oil production from formations.",signatures:"Gabriel Cavalheiro, Mariana Brandao and Saulo Rocha",downloadPdfUrl:"/chapter/pdf-download/60796",previewPdfUrl:"/chapter/pdf-preview/60796",authors:[{id:"227347",title:"Dr.",name:"Gabriel",surname:"Cavalheiro",slug:"gabriel-cavalheiro",fullName:"Gabriel Cavalheiro"},{id:"227352",title:"Dr.",name:"Mariana",surname:"Cavalheiro",slug:"mariana-cavalheiro",fullName:"Mariana Cavalheiro"},{id:"227354",title:"Dr.",name:"Saulo",surname:"Rocha",slug:"saulo-rocha",fullName:"Saulo Rocha"}],corrections:null},{id:"60352",title:"Intelligent Drilling and Coring Technologies for Unmanned Interplanetary Exploration",doi:"10.5772/intechopen.75712",slug:"intelligent-drilling-and-coring-technologies-for-unmanned-interplanetary-exploration",totalDownloads:1323,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The robotic technology, especially the intelligent robotics that can autonomously conduct numerous dangerous and uncertain tasks, has been widely applied to planetary explorations. Similar to terrestrial mining, before landing on planets or building planetary constructions, a drilling and coring activity should be first conducted to investigate the in-situ geological information. Given the technical advantages of unmanned robotics, utilizing an autonomous drill tool to acquire the planetary soil sample may be the most reliable and cost-effective solution. However, due to several unique challenges existed in unmanned drilling and coring activities, such as long-distance time delay, uncertain drilling formations, limited sensor resources, etc., it is indeed necessary to conduct researches to improve system’s adaptability to the complicated geological formations. Taking drill tool’s power consumption and soil’s coring morphology into account, this chapter proposed a drilling and coring characteristics online monitoring method to investigate suitable drilling parameters for different formations. Meanwhile, by applying pattern recognition techniques to classify different types of potential soil or rocks, a drillability classification model is built accurately to identify the current drilling formation. By combining suitable drilling parameters with the recognized drillability levels, a closed-loop drilling strategy is established finally, which can be applied to future interplanetary exploration.",signatures:"Junyue Tang, Qiquan Quan, Shengyuan Jiang, Jieneng Liang and\nZongquan Deng",downloadPdfUrl:"/chapter/pdf-download/60352",previewPdfUrl:"/chapter/pdf-preview/60352",authors:[{id:"227250",title:"Ph.D. Student",name:"Junyue",surname:"Tang",slug:"junyue-tang",fullName:"Junyue Tang"},{id:"228658",title:"Prof.",name:"Shengyuan",surname:"Jiang",slug:"shengyuan-jiang",fullName:"Shengyuan Jiang"},{id:"228668",title:"Prof.",name:"Qiquan",surname:"Quan",slug:"qiquan-quan",fullName:"Qiquan Quan"},{id:"228669",title:"Mr.",name:"Jieneng",surname:"Liang",slug:"jieneng-liang",fullName:"Jieneng Liang"},{id:"228671",title:"Prof.",name:"Zongquan",surname:"Deng",slug:"zongquan-deng",fullName:"Zongquan Deng"}],corrections:null},{id:"63944",title:"Making the Connection for Well Control on Floaters: Evolving Design Rationales for BOP Control Systems",doi:"10.5772/intechopen.77998",slug:"making-the-connection-for-well-control-on-floaters-evolving-design-rationales-for-bop-control-system",totalDownloads:1433,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, a broad technical overview is offered to illustrate the technological advancements that have made the original direct hydraulic system reach those system design features that are shown in figure overleaf, which is a modern general arrangement of the “multiplexing” type of the BOP control system. Behind each discrete advancement, it goes without saying, there was a lot of design work, influenced by the radically different conditions in the subsea marine environment than those that we experience on land. Each step of this enabling technology is reviewed with in-depth reasoning explaining the “whys” and “wherefores” of each particular development. Let us start, as the drilling industry did for the development of BOP designs, at the beginning of the industry’s step offshore around 60 years ago. Not least, it should be emphasized that the ways in which the systems’ architecture has evolved have, in large part, been “driven” by the statutes laid out by the American Petroleum Institute (API) and later by other class societies that govern design compliance within the industry. The learning objectives of this chapter are to provide factual insights into evolving BOP control system designs as the industry moved from onshore to offshore and subsequently from bottom-supported drilling installations to floating drilling installations. This technology also forms the basis of the underpinning principles of hydraulic/electro and multiplexing subsea control systems that are currently used in the control of all kinds of production trees, subsea production centers, subsea distribution, and pipe line end manifolds (PLEMs). This chapter can be considered as a foundation and introductory overview for the development of control systems used in the subsea environment and those engineering challenges and obstacles that have been successfully surmounted, resulting in the technology basis in use today in the manufacture of subsea control systems.",signatures:"Paul A. Potter",downloadPdfUrl:"/chapter/pdf-download/63944",previewPdfUrl:"/chapter/pdf-preview/63944",authors:[{id:"223603",title:"Dr.",name:"Paul",surname:"Potter",slug:"paul-potter",fullName:"Paul Potter"}],corrections:null},{id:"60612",title:"Bio-Based Oil Drilling Fluid Improvements through Carbon- Based Nanoparticle Additives",doi:"10.5772/intechopen.74674",slug:"bio-based-oil-drilling-fluid-improvements-through-carbon-based-nanoparticle-additives",totalDownloads:1083,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Performance issues of vegetable oil or bio-based oil drilling fluids are generally inferior as compared to synthetic based drilling fluids. This chapter focuses largely on thermal conductivity and rheological properties of bio-based oil drilling fluid as its core issues. Unstable drilling fluids do not only incur in downtime for maintenance, but it indirectly affects production capacity as well. To overcome these issues, nanoparticles acts as additives to improve the thermo-physical traits of bio-based oil drilling fluid. The scope of this chapter focuses on dispersion of graphene oxide at very low concentration, namely 25, 50 and 100 ppm, to improve the thermal conductivity and rheological properties of bio-based oil drilling fluid. The data obtained from thermal conductivity and rheological experimental works were validated with various thermal conductivity and rheological models.",signatures:"Yee Ho Chai, Suzana Yusup, Vui Soon Chok and Sonny Irawan",downloadPdfUrl:"/chapter/pdf-download/60612",previewPdfUrl:"/chapter/pdf-preview/60612",authors:[{id:"222825",title:"Dr.",name:"Dr Sonny",surname:"Irawan",slug:"dr-sonny-irawan",fullName:"Dr Sonny Irawan"},{id:"224630",title:"Prof.",name:"Suzana",surname:"Yusup",slug:"suzana-yusup",fullName:"Suzana Yusup"},{id:"227252",title:"Mr.",name:"Yee Ho",surname:"Chai",slug:"yee-ho-chai",fullName:"Yee Ho Chai"},{id:"235992",title:"Dr.",name:"Vui Soon",surname:"Chok",slug:"vui-soon-chok",fullName:"Vui Soon Chok"}],corrections:null},{id:"61366",title:"Solid Control System for Maximizing Drilling",doi:"10.5772/intechopen.76149",slug:"solid-control-system-for-maximizing-drilling",totalDownloads:1163,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter focuses on the development of solid control system that is suited for drilling 12.25-inch hole. The first part discusses the performance of rate of penetration (ROP), equivalent circulating density (ECD) and drill string drag while the second part of the chapter discusses about the effect of solid control system performance to mud properties plastic viscosity (PV), yield point (YP), and low gravity solid (LGS). The input parameters were gathered from two different set up of solid control systems that were used in Well A and Well B. The result is mainly based on the performance of original solid control system new design vs. old design. Installation of distributor tank and channeling the mud to respective shale shakers significantly enhanced the system and operational performance. The ROP at 12.25-inch drilling was improved by 20%. New design, on an average, improved the ECD margin by reducing additional pressure exerted using original mud from 4.9 to 2.9%. High ECD margin is not recommended because it can break the weak formation. Mud properties while drilling the 12.25-inch hole section; PV, YP and LGS values were improved by 14, 17, and 25% respectively.",signatures:"Sonny Irawan and Imros B. Kinif",downloadPdfUrl:"/chapter/pdf-download/61366",previewPdfUrl:"/chapter/pdf-preview/61366",authors:[{id:"222825",title:"Dr.",name:"Dr Sonny",surname:"Irawan",slug:"dr-sonny-irawan",fullName:"Dr Sonny Irawan"},{id:"223995",title:"Mr.",name:"Imros",surname:"Kinif",slug:"imros-kinif",fullName:"Imros Kinif"}],corrections:null},{id:"61383",title:"Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy",doi:"10.5772/intechopen.76903",slug:"rate-of-penetration-prediction-utilizing-hydromechanical-specific-energy",totalDownloads:1490,totalCrossrefCites:5,totalDimensionsCites:9,hasAltmetrics:0,abstract:"The prediction and the optimization of the rate of penetration (ROP), an important measure of drilling performance, have increasingly generated great interest. Several empirical techniques have been explored in the literature for the prediction and the optimization of ROP. In this study, four commonly used artificial intelligence (AI) algorithms are explored for the prediction of ROP based on the hydromechanical specific energy (HMSE) ROP model parameters. The AIs explored are the artificial neural network (ANN), extreme learning machine (ELM), support vector regression (SVR), and least-square support vector regression (LS-SVR). All the algorithms provided results with accuracy within acceptable range. The utilization of HMSE in selecting drilling variables for the prediction models provided an improved and consistent methodology of predicting ROP with drilling efficiency optimization objectives. This is valuable from an operational point of view, because it provides a reference point for measuring drilling efficiency and performance of the drilling process in terms of energy input and corresponding output in terms of ROP. The real-time drilling data utilized are must-haves, easily acquired, accessible, and controllable during drilling operations.",signatures:"Omogbolahan Ahmed, Ahmed Adeniran and Ariffin Samsuri",downloadPdfUrl:"/chapter/pdf-download/61383",previewPdfUrl:"/chapter/pdf-preview/61383",authors:[{id:"120519",title:"Prof.",name:"Ariffin",surname:"Samsuri",slug:"ariffin-samsuri",fullName:"Ariffin Samsuri"},{id:"222940",title:"Ph.D. Student",name:"Omogbolahan",surname:"Ahmed",slug:"omogbolahan-ahmed",fullName:"Omogbolahan Ahmed"},{id:"239856",title:"Dr.",name:"Ahmed",surname:"Adeniran",slug:"ahmed-adeniran",fullName:"Ahmed Adeniran"}],corrections:null},{id:"60598",title:"Drilling Performance Optimization Based on Mechanical Specific Energy Technologies",doi:"10.5772/intechopen.75827",slug:"drilling-performance-optimization-based-on-mechanical-specific-energy-technologies",totalDownloads:3215,totalCrossrefCites:5,totalDimensionsCites:9,hasAltmetrics:1,abstract:"Mechanical specific energy (MSE) has been widely used to quantify drilling efficiency and maximize rate of penetration (ROP) in oil and gas wells drilling. In this chapter, MSE models respectively for directional or horizontal drilling and rotating drilling with positive displacement motor (PDM) are established based on the evaluation of virtues and defects of available MSE models. Meanwhile methods for drilling performance prediction and optimization based on MSE technologies are presented. Field data presented in this chapter indicates that the developed MSE models estimate MSE values with a reasonable approximation in the absence of reliable torque measurements, the method for optimizing drilling parameters can estimate optimum WOB values with different RPM to drill a specific formation interval with PDM. It also show that the optimum WOB is low for rotating drilling with PDM compared with the conventional drilling without PDM, increasing WOB does not always increase ROP but is more likely to decrease ROP. The drilling performance prediction and optimization methods based on MSE technologies could be effectively used to maximize ROP and allow operators to drill longer and avoid unnecessary trips, and is worthy to be applied and promoted with highly diagnostic accuracy, effective optimizing and simple operation.",signatures:"Xuyue Chen, Jin Yang and Deli Gao",downloadPdfUrl:"/chapter/pdf-download/60598",previewPdfUrl:"/chapter/pdf-preview/60598",authors:[{id:"227327",title:"Dr.",name:"Xuyue",surname:"Chen",slug:"xuyue-chen",fullName:"Xuyue Chen"},{id:"240043",title:"Prof.",name:"Jin",surname:"Yang",slug:"jin-yang",fullName:"Jin Yang"},{id:"240044",title:"Prof.",name:"Deli",surname:"Gao",slug:"deli-gao",fullName:"Deli Gao"}],corrections:null},{id:"60339",title:"New Development of Air and Gas Drilling Technology",doi:"10.5772/intechopen.75785",slug:"new-development-of-air-and-gas-drilling-technology",totalDownloads:1008,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Gas drilling technology has been widely promoted and applied in recent years. Known for being capable of discovering and protecting reservoirs, improving the penetration rate and avoiding loss circulation, two key issues of gas drilling still need to be addressed. First, a more accurate way of determining the gas injection rate is needful. In this text, we present a modified mathematical model for predicting the optimum range of gas injection rate required to balance the borehole cleaning and well-integrity issues. The optimum gas injection rate should be sought between the minimum value required for hole cleaning and the maximum permissible value to avoid hole erosion. Good consistency between the model prediction and field problem-free nitrogen gas injection rate indicates the reliability of the proposed model. Second, the problem of environmental pollution and wasting of resources caused by direct discharging or combustion of the returned gas is to be solved. To address the latter issue, we introduce a new technology of gas recycling system (GRS). Our research group has carried out a comprehensive investigation, including integration design, technological process, cuttings transport analysis, separation and filter equipment selection, and control system design. The feasibility of GRS has been verified through an open-loop pilot test.",signatures:"Jun Li, Yulong Yang, Boyun Guo and Gonghui Liu",downloadPdfUrl:"/chapter/pdf-download/60339",previewPdfUrl:"/chapter/pdf-preview/60339",authors:[{id:"228602",title:"Prof.",name:"Jun",surname:"Li",slug:"jun-li",fullName:"Jun Li"},{id:"239391",title:"Prof.",name:"Gonghui",surname:"Liu",slug:"gonghui-liu",fullName:"Gonghui Liu"},{id:"239392",title:"Prof.",name:"Boyun",surname:"Guo",slug:"boyun-guo",fullName:"Boyun Guo"},{id:"239393",title:"Dr.",name:"Yulong",surname:"Yang",slug:"yulong-yang",fullName:"Yulong Yang"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"7609",title:"Enhanced Oil Recovery Processes",subtitle:"New Technologies",isOpenForSubmission:!1,hash:"62359d9c21b76f899be04fa0f8b46668",slug:"enhanced-oil-recovery-processes-new-technologies",bookSignature:"Ariffin 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Integrating artificial intelligence (AI) and robotics with traditional practices, the world of manufacturing processes is undergoing a transformation from activities that rely on human experience and skills into flexible environments, including objective decisional systems fully integrated within the industrial process. Advanced robotics is meant to develop autonomous and intelligent systems that could reduce the intervention of human workers [1] in many of the crucial and repetitive tasks that represent the core business of companies. Augmented and virtual reality can give operators more information about their tasks [2] and help them to alleviate mental stress during some jobs. Additive manufacturing [3] can speed up the production process. Internet of things (IoT) [4] allows new forms of communication between machines, giving rise to smart devices that can help humans achieve their objectives. Radiofrequency identification (RFID) technologies are used for efficient logistics and inventory warehouse management [5] reducing costs while increasing quality and competitiveness.
Industry 4.0 pillar technologies.
Among all the aforementioned technologies, AI is perhaps the one that received more interest during the years. Indeed, nowadays, the industrial interest in AI applications in various sectors is undeniable. However, for industries, artificial intelligence is both a source of enthusiasm and skepticism. One reason is that deep learning (DL) is a technology based on data, and problems solved using AI are as good or as bad as the data they are trained on. In addition, companies perceive AI as a black box and would prefer understandable and explainable processes [6]. Both these aspects should be taken into consideration when developing industrial AI solutions.
Current automation-assisted production is mostly open-loop and relies on specific checkpoints to perform product quality analysis. Early systems based on vision date back to the nineties. Such an approach suits best when critical issues can be formally expressed by taking advantage of geometrical measurements or well-known features on the inspected objects. Unfortunately, these techniques cannot perform many quality-control activities because they need a predefined sequence of actions where quality checks should be designed carefully to meet the precise production requirements. Moreover, human nature shows formidable efficiency in learning simple checks even if it would be difficult to formalize such operations with a sequence of rules. Indeed, experience plays a relevant role in human evaluation for products quality assessment. Similarly, vision inspection processes performed by automated machines will require the development of novel algorithms that should be trained and improved with time and experience.
The introduction of automation systems in the production lines that exploit AI techniques has reduced the need for human intervention in the manufacturing process of many products. This innovation had a major impact on many industrial applications, and visual inspection is by far the activity that has profited most. Thanks to deep neural networks (DNNs), difficult computer vision tasks, such as object classification or detection and image segmentation, have been addressed recently using an adequate number of training data. DNNs are scalable, experience-based, and have similar performance to human workers. Since the development of AlexNet [7], solutions based on deep learning have been encouraged, and convolutional neural networks (CNN) also have been extensively utilized for automating optical quality inspections. However, since such networks need a huge amount of labeled data for training their parameters, it is difficult to have an adequately large set of faulty samples with well-optimized industrial processes for creating a
Welding is a fundamental activity in many industrial manufacturing processes, such as automotive, shipbuilding, aerospace, and electronics. It is a crucial operation for the overall quality of the production line because a defect not detected in the early stages can determine the rejection of the entire product. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and presenting in detail a solution for quality checks in fuel injectors welding during the production stage.
Inspection analysis can be classified into one of the following categories [8]—
As of today, different methods have been proposed for inspecting the welding process online [9]. Their design is suited to diverse defects types and differ in the data processed during the evaluation. Among the sensing technologies employed in literature, optical detectors [10], acoustic measurements [11], and vision analysis [12] are surely the most utilized. While, for classification applications, artificial neural networks [13, 14, 15] and fuzzy inference systems [16, 17] are usually preferred thanks to the wide range of problems and diversity of defects they could cope with as in the case of classification of steel strip defects [18, 19].
However, the focus of these works is on defects classification and not on their detection. Therefore, they could not cope with feature understanding problems such as discriminating between good samples and defective ones. A different approach is proposed by Ak et al. [20] where X-ray images are used to detect defects in metal castings.
Recent literature is plenty of research addressing the problem of welding localization employing off-the-shelf DL architectures or introducing slight modifications on the tail of popular networks. These approaches are mostly based on the R-CNN [21], Faster R-CNN [22], and YOLO [23] architectures. The reason behind their adoption is that these architectures usually require little fine-tuning procedures for efficiently localizing welding areas and spots. Such efficiency is strictly related to the presence of plain metal surfaces in the surrounding area of the welding by enabling simple and accurate segmentation of the feature under inspection. This is the case of resistance spot welding (RSW) processes typically employed to connect metal sheets at a low cost and in a short time.
Concerning detection approaches, early methods based on traditional computer vision techniques [24] require hand-crafted features and complex threshold settings to adapt to environmental conditions. However, approaches based on deep learning allow increasing the robustness of the detection coping with environmental noise and the sensitivity of the welding processes.
The majority of approaches are built upon the above-mentioned architectures for welding spots localization. Fast R-CNN [25] is a region proposal network that computes the region of interest (ROI) on the feature map, thus improving upon the R-CNN architecture. Faster R-CNN integrates convolutional layers for object classification, feature extraction, bounding box regression, and region proposals into a network, further improving the detection performance but still not reaching real-time capabilities. Unlike the R-CNN family, which has a two-stage detection architecture, YOLO implements a regression network with a grid of bounding boxes and associated class probabilities, thus enabling real-time detection with recent hardware. In the race for timing performance, YOLOv2 [26] borrowed the anchor mechanism from SSD [27] and Faster R-CNN, which also enhanced the network
Considering the reduced dimension of small spot welds, low-resolution feature maps in the backbone, and convolution strides dimension could cause an information leak. To face this issue, the work proposed by Dai et al. [34] introduces a modified MobileNEtV3 [35] architecture obtaining a good tradeoff between
Focusing on the classification and detection of defects over the welding area or joint, off-the-shelf solutions are no more efficient by themselves, and some issues need to be faced to enable the use of DNNs. Clustering and segmentation become difficult because the feature to be recognized are not easily separable. This chapter introduces some of the most common issues in the employment of DL for industrial quality inspection discussing the practical case of detection of welding defects in diesel injectors heads.
Quality inspection systems based on vision techniques in most cases follow the workflow depicted in Figure 2. The process starts by collecting the sample images using a set of cameras or sensors exploiting an adequate source of illumination. Such samples are then processed to improve images quality. Therefore, once the features are extrapolated, the evaluation of the quality and the classification of the defect are performed. Measurement and classification could either be implemented with traditional computer vision algorithms, with modern DNN architectures, or with a fusion of both of them, as in the case presented in the following. Usually, the inspection system also provides an actuation step that triggers actions, depending on the analysis result, to the production lines that directly communicate with the control unit (commonly based on programmable logic controllers (PLCs)).
Typical visual inspection workflow.
The work discussed in the study by Sassi et al. [36] originated from industrial demands with the specific target of detecting welding defects on diesel injectors in the production line. Such a project focused on realizing the most effective combination of traditional computer vision methods and deep neural network architecture for identifying the defects in the welding. In particular, the aim was to substitute the existing vision inspection system extending the classes of detectable defects in the analysis phase.
Welding joint defects may appear in different typologies: some are related to anomalies on the surface of the joint, while others are related to its geometrical properties, such as its thickness and position. Four categories have been defined for the analysis of the welding joint, as depicted graphically in Figure 3 showing an example from each category:
D1 (
D2
D3 (
D4 (
Examples of defect classes. IN D3, green and red circles show the detected inner and outer edges of the welding joint. In D4, the red arrows highlight thin welding, while the green ones are standard ones.
Defects D3 and D4 are quantitative measurable and are examined employing an algorithm based on traditional computer vision techniques (similar to the existing commercial solution). On the contrary, the others (D1 and D2) are more qualitative and are recognized through a method based on deep learning.
Furthermore, the analysis of the defects must be performed within a time slot that depends on the actual production line (1.8 seconds cycle time in the depicted scenario) to avoid interferences with the manufacturing process. This amount of time is required for the actuation system and the welding stage to process a new injector as input to the system.
During dataset preparation, the ideal case is the one in which several samples (in the order of thousands or more) are available for each class to be detected, the classes have balanced data, and they are well separated from each other. In such an ideal case, it is possible to give the network a representative set of samples of the whole input space for the training and avoid confusing the network with an uneven distribution of the inputs or the similarities between the classes.
Unfortunately, industrial production lines having well-optimized processes are usually present with few defective products and much more good samples. Therefore, it is often unfeasible to get sets of defective samples large enough to train CNNs for classification purposes. In the majority of the cases, the objective of the training moves from defect classification to anomaly detection. The worst-case scenario is the one presenting an
Different sampling strategies could be implemented to deal with
An alternative approach that is often used to increase the robustness of the classification is
Other ways for enlarging the dataset have been experimented like passing the input data through an encoder-decoder network that applies different transformations featured with random noise [42]. Another approach worth mentioning is the generation of
Virtual data generation could be obtained by producing synthetic images with the intent to cover the whole input feature space. Generative adversarial network (GAN) [44] or the most recent conditional GAN (cGAN) [45] could be alternatively used for this purpose. However, this is computationally expensive and requires taking into account all possible configurations and boundary conditions for generating samples as close as possible to real ones. Domain randomization techniques [46] could be applied to synthetically generated data for improving the generalization capabilities and the robustness of the network.
Similar to humans, when learning new concepts or rules, if not clearly defined, the training can lead to fuzzy assumptions, possibly resulting in wrong outcomes. Additionally, when dealing with data obtained by a sensing apparatus, it is important to check the correctness of the acquired data samples to avoid possible causes of classification errors. A cleaning process should remove outliers (wrong data association of a sample with a class) and spurious samples that could confuse the learning process. Industrial processes often rely on qualitative evaluation, and unfortunately, different quality experts in the same industrial process classify the same product as belonging to different classes. If the same confusion is transferred to the DL architecture, the learning process will probably worsen the decision process. For this reason, a preprocessing stage on the data is essential. In most cases, the help of professionals of the sector for interpreting, filtering, and preprocessing the data is welcome.
A last and quite important aspect is the adoption of correct performance metrics and
It has been seen that the first layers of CNNs learn kernels acting as color blob detectors or Gabor filters. Such a property seems to be very general and the features learned do not appear to be strictly dependent on the particular training set that has been adopted. As humans can learn from experience and transfer the notion learned in diverse application domains, similarly, a DL architecture can transfer the features learned on a particular dataset to another CNN, which will be trained on a different one [51]. Such a technique is called
Following the
The work combines a traditional computer vision pipeline together with a DL architecture. This pipeline was necessary to maintain the compatibility with classical production lines and provide a correct input to the welding defect detection phase. The algorithm receives the raw image as input, converts it from Bayer format to grayscale, and improves the edge detection by equalizing the levels and applying a Gaussian blur. In a successive step, since different kinds of injectors can be analyzed by the same system, the type of injector is identified, and the position of its center is obtained. The algorithm proceeds to detect the outer shell of the injector head by estimating an external radius that approximates the detected blob. Then, using the extracted information, the algorithm performs an area search for welding points and estimates a welding circle on the joint. Subsequently, the algorithm collects statistics about the number of welding points found and their positions. In traditional industrial systems, a set of thresholds decided by the manufacturing company is used to evaluate the welding quality from the measured quantities.
A schematic overview of the algorithm is shown in Figure 4. The algorithm’s output gives quantitative information about the welding and produces a processed image to be given as input to the second analysis stage. The extracted information allows evaluating the continuity of the welding in a certain area on the injector’s head, verifying the centering of the inner part of the injector with respect to the outer one, and eventually the welding thickness. This information is also beneficial to clean the image from unnecessary data for the subsequent analysis and to center the injector images to obtain more controlled conditions on the input of the successive stage.
Schematics of the components of the geometrical analysis pipeline.
The DL architecture chosen in that work is the DenseNet-121. Figure 5 depicts the structure of the network. DenseNet efficiently simplifies the connectivity pattern between layers guaranteeing maximum information flow by reusing the features through the network. Concerning the training phase, every layer has direct access to the gradients from the original input image and the
Schematic representation of the layers and blocks in the DenseNet-121 deep learning architecture.
In the approach presented by Sassi et al. [36], the
Unfortunately, the MINC dataset is highly unbalanced. Therefore, three classes, that is,
Sometimes, during production lines maintenance or innovations, the replacement of a machine, the change of a supplier, or the change in a manufacturing process, could lead to a significant variation on the usual production procedure in terms of the visual quality of the products. Such situations could vanish the capacity of a machine computation to return the expected results.
In this context, continuing on the problem of detecting welding defects on injectors heads, the work presented by Tripicchio et al. [48] proposes possible solutions to this issue without requiring an architectural change in the learning architecture. The new case had to handle some modifications concerning the parameters associated with the welding process, producing input samples with specific artifacts that the previously designed and trained network did never encounter. In particular, such new inputs were correlated to a variation in the substance used for the soldering that generated gold-violet spots on the injector head in random positions. Such noise introduces a novel complexity in the detection of the defects because the spots can hide or visually resemble the presence of bumps and holes in the welding layer. The followed approach was to make fewer changes as possible in the architecture of the network, operating a smart preprocessing and applying filtering techniques.
The results show the ability to train a network with almost 7 million parameters on just 306 training images belonging to the new alteration, achieving a
Such a result has been achieved leveraging on two important aspects. The first is the design of a custom preprocessing and filtering stage, while the second is the adoption of a novel data balancing strategy.
A preprocessing stage is needed on the input images with the aim of erasing or smoothing the chromatic nuances that could confuse the feature learning process. In particular, three filtering approaches have been proposed and tested (Figure 6). The first filter (
Different filters applied on a sector of the same injector contour image. (a) No filter. (b) Median fill filter. (c) Patch filter.
Different analyses have been done to assess the performance improvement given by such filters. As a result, a
Concerning data imbalance, an exploration of different unbalanced splits has been performed. To prevent overfitting and lead the learning process toward generalization, the authors propose to compute the performance metrics at each evaluation step considering the input imbalance. In particular, metrics like
Defective injectors were chosen as positive samples and
Cross-validation has been applied to improve generalization concerning the stochastic gradient descent optimization. The network has been trained multiple times by combining different variations of the proportions between defective and good samples and changing the numbers of epochs. During the training phase, each epoch is compared with all previous epochs for obtaining the one with the highest performance in terms of
The
This chapter highlights the importance of the employment of deep learning architectures in the context of future industrial applications with a focus on welding and welding defects detection. The industrial sector and especially the manufacturing industry pose several challenges to the design of efficient and robust quality inspection processes. The most common issues are discussed in detail, and possible countermeasures are suggested to overcome such issues. In particular, the problem of data imbalance, scarcity of examples, environmental noises, change in the nominal conditions of the process, or the presence of artifacts are discussed. Application examples from previous works of the authors are proposed to clarify how the suggested countermeasures can be put into practice. Although many industries are still scared of adopting deep learning approaches due to a lack of knowledge of their internal processes or reasoning, extensive use of artificial intelligence applications is envisaged for the near future.
AI | Artificial Intelligence |
CNN | Convolutional Neural Network |
DL | Deep Learning |
DNN | Deep Neural Network |
GAN | Generative Adversarial Network |
ICT | Information and Communication Technologies |
IoT | Internet of Things |
PLC | Programmable Logic Controller |
ROI | Region of Interest |
RSW | Resistance Spot Welding |
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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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