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024 7 _a10.1007/978-981-13-9382-2
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072 7 _aTEC020000
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082 0 4 _a670
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100 1 _aVendan, S. Arungalai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_944239
245 1 0 _aWelding and Cutting Case Studies with Supervised Machine Learning
_h[electronic resource] /
_cby S. Arungalai Vendan, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, Akhil Garg.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aIX, 249 p. 257 illus., 192 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aEngineering Applications of Computational Methods,
_x2662-3374 ;
_v1
505 0 _aSupervised 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.
520 _aThis 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.
650 0 _aManufactures.
_931642
650 0 _aMachine learning.
_91831
650 0 _aEngineering—Data processing.
_931556
650 0 _aMaterials—Analysis.
_944240
650 1 4 _aMachines, Tools, Processes.
_931645
650 2 4 _aMachine Learning.
_91831
650 2 4 _aData Engineering.
_932525
650 2 4 _aCharacterization and Analytical Technique.
_944241
700 1 _aKamal, Rajeev.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_944242
700 1 _aKaran, Abhinav.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_944243
700 1 _aGao, Liang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_944244
700 1 _aNiu, Xiaodong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_944245
700 1 _aGarg, Akhil.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_944246
710 2 _aSpringerLink (Online service)
_944247
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789811393846
830 0 _aEngineering Applications of Computational Methods,
_x2662-3374 ;
_v1
_944248
856 4 0 _uhttps://doi.org/10.1007/978-981-13-9382-2
912 _aZDB-2-ENG
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