Welding and Cutting Case Studies with Supervised Machine Learning
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Beschreibung
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 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.
Combines mechanical, metallurgical, arc physics, manufacturing, electrical and data analytics expertise from an interdisciplinary perspective Introduces supervised learning for parametrical interdependency analysis using coding in Python’s Pandas, NumPy, and Scikit Learn libraries Examines the welding and cutting process from its basic configuration to experimental trials, testing and characterization, as well as the use of data visualization Presents procedures, materials, and data acquired from real-time experimentation adopted for critical applications
Autor*in
S. Arungalai Vendan
Themen in »Welding and Cutting Case Studies with Supervised Machine Learning«
Advanced Welding Processes Machine Learning Supervised Learning Cutting Processes Parametric Analysis