LEADER 03794nam 22006735 450 001 9910407738503321 005 20230810233051.0 010 $a981-13-9382-6 024 7 $a10.1007/978-981-13-9382-2 035 $a(CKB)4100000011280839 035 $a(MiAaPQ)EBC6221259 035 $a(DE-He213)978-981-13-9382-2 035 $a(PPN)248592548 035 $a(EXLCZ)994100000011280839 100 $a20200603d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aWelding and Cutting Case Studies with Supervised Machine Learning /$fby S. Arungalai Vendan, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, Akhil Garg 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (256 pages) 225 1 $aEngineering Applications of Computational Methods,$x2662-3374 ;$v1 311 $a981-13-9381-8 327 $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. 330 $aThis book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities, namely the data analysis and data visualization techniques, which form the core of machine learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with an introduction to machine learning for applied sciences, enabling them to address global competitiveness and work on real-time technical challenges, it is also a valuable resource for scholars with domain knowledge. 410 0$aEngineering Applications of Computational Methods,$x2662-3374 ;$v1 606 $aManufactures 606 $aMachine learning 606 $aEngineering$xData processing 606 $aMaterials$xAnalysis 606 $aMachines, Tools, Processes 606 $aMachine Learning 606 $aData Engineering 606 $aCharacterization and Analytical Technique 615 0$aManufactures. 615 0$aMachine learning. 615 0$aEngineering$xData processing. 615 0$aMaterials$xAnalysis. 615 14$aMachines, Tools, Processes. 615 24$aMachine Learning. 615 24$aData Engineering. 615 24$aCharacterization and Analytical Technique. 676 $a006.31 700 $aVendan$b S. Arungalai$4aut$4http://id.loc.gov/vocabulary/relators/aut$0909798 702 $aKamal$b Rajeev$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aKaran$b Abhinav$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGao$b Liang$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aNiu$b Xiaodong$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGarg$b Akhil$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910407738503321 996 $aWelding and Cutting Case Studies with Supervised Machine Learning$92509714 997 $aUNINA