This book provides an insight into the welding techniques with a cross-disciplinary treatment to address the shortcomings of contemporary learning of welding terminology. Various topics covered include introduction to welding processes, design requirements, prominence of design, case studies presenting structural defacements due to inappropriate design, comprehensive surveys on welding processes selected from various process categories, design calculations to be adopted for specific applications and sample calculations. This book is useful for researchers, engineers and professionals working on welding equipment and technologies.

This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities, namely the data analysis and data visualization techniques, which form the core of machine learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with an introduction to machine learning for applied sciences, enabling them to address global competitiveness and work on real-time technical challenges, it is also a valuable resource for scholars with domain knowledge.

This book provides an insight into the welding techniques with a cross-disciplinary treatment to address the shortcomings of contemporary learning of welding terminology. Various topics covered include introduction to welding processes, design requirements, prominence of design, case studies presenting structural defacements due to inappropriate design, comprehensive surveys on welding processes selected from various process categories, design calculations to be adopted for specific applications and sample calculations. This book is useful for researchers, engineers and professionals working on welding equipment and technologies.

This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities, namely the data analysis and data visualization techniques, which form the core of machine learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with an introduction to machine learning for applied sciences, enabling them to address global competitiveness and work on real-time technical challenges, it is also a valuable resource for scholars with domain knowledge.

This book presents the fundamentals of arc phenomena, various arc welding power sources, their control strategies, welding data acquisition, and welding optimization. In addition, it discusses a broad range of electrical concepts in welding, including power source characteristics, associated parameters, arc welding power source classification, control strategies, data acquisitions techniques, as well as optimization methods. It also offers advice on how to minimize the flaws and improve the efficacy and performance of welds, as well as insights into the mechanical behavior expressed in terms of electromagnetic phenomena, which is rarely addressed. The book provides a comprehensive review of interdisciplinary concepts, offering researchers a wide selection of strategies, parameters, and sequences of operations to choose from.

This book presents the fundamentals of arc phenomena, various arc welding power sources, their control strategies, welding data acquisition, and welding optimization. In addition, it discusses a broad range of electrical concepts in welding, including power source characteristics, associated parameters, arc welding power source classification, control strategies, data acquisitions techniques, as well as optimization methods. It also offers advice on how to minimize the flaws and improve the efficacy and performance of welds, as well as insights into the mechanical behavior expressed in terms of electromagnetic phenomena, which is rarely addressed. The book provides a comprehensive review of interdisciplinary concepts, offering researchers a wide selection of strategies, parameters, and sequences of operations to choose from.

This book offers a systematic overview of polymer joining and highlights the experimental and numerical work currently being pursued to devise possible strategies to overcome the technical issues. It also covers the fundamentals of polymers, the corresponding joining processes and related technologies. A chapter on the extrapolation of finite element analysis (FEA) for forecasting the deformation and temperature distribution during polymer joining is also included. Given its breadth of coverage, the book will be of great interest to researchers, engineers and practitioners whose work involves polymers.