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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 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.

Selected, peer reviewed papers from the 3rd International Conference on Advances in Product Development and Reliability (PDR 2012), July 28-30, 2012, Wuhan, China

Selected, peer reviewed papers from the: 2011 International Conference on Material Engineering and Mechanical Engineering, Wuhan, China, August 20-21, 2011