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Welding and Cutting Case Studies with Supervised Machine Learning

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 parametr...

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Bibliographic Details
Main Authors: Vendan, S. Arungalai, Kamal, Rajeev (Author), Karan, Abhinav (Author), Gao, Liang (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2020, 2020
Edition:1st ed. 2020
Series:Engineering Applications of Computational Methods
Subjects:
Machine Learning
Materials—analysis
Machines, Tools, Processes
Manufactures
Engineering—data Processing
Data Engineering
Characterization And Analytical Technique
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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505 0 |a Supervised machine learning in magnetically impelled arc butt welding (MIAB) -- Supervised machine learning in cold metal transfer (CMT) -- Supervised machine learning in friction stir welding (FSW) -- Supervised machine learning in wire cut electric discharge maching (WEDM) -- Appendix: coding in python, numpy, panda, scikit-learn used for analysis with emphasis on libraries 
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653 |a Characterization and Analytical Technique 
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700 1 |a Karan, Abhinav  |e [author] 
700 1 |a Gao, Liang  |e [author] 
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520 |a 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 

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