Welding and Cutting Case Studies with Supervised Machine Learning / by S. Arungalai Vendan, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, Akhil Garg

Von:

Vendan, S. Arungalai [VerfasserIn]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Engineering Applications of Computational Methods ; 1 | Springer eBook CollectionVerlag: Singapore : Springer Singapore, 2020Verlag: Singapore : Imprint: Springer, 2020Auflage: 1st ed. 2020Beschreibung: 1 Online-Ressource(IX, 249 p. 257 illus., 192 illus. in color.)ISBN:

9789811393822

Schlagwörter:

670 23

DOI: DOI: 10.1007/978-981-13-9382-2Online-Ressourcen:

Zusammenfassung: 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.Zusammenfassung: 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.PPN: PPN: 1724278061Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SEB | ZDB-2-SXE

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