LEADER 03865nam 2200961z- 450 001 9910637782503321 005 20231214133201.0 010 $a3-0365-5772-5 035 $a(CKB)5470000001631712 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94546 035 $a(EXLCZ)995470000001631712 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances and Applications of Machine Learning in Metal Forming Processes 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (210 p.) 311 $a3-0365-5771-7 330 $aMachine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 606 $aMining technology & engineering$2bicssc 610 $asheet metal forming 610 $auncertainty analysis 610 $ametamodeling 610 $amachine learning 610 $ahot rolling strip 610 $aedge defects 610 $aintelligent recognition 610 $aconvolutional neural networks 610 $adeep-drawing 610 $akriging metamodeling 610 $amulti-objective optimization 610 $aFE (Finite Element) AutoForm robust analysis 610 $adefect prediction 610 $amechanical properties prediction 610 $ahigh-dimensional data 610 $afeature selection 610 $amaximum information coefficient 610 $acomplex network clustering 610 $aring rolling 610 $aprocess energy estimation 610 $ametal forming 610 $athermo-mechanical FEM analysis 610 $aartificial neural network 610 $aaluminum alloy 610 $amechanical property 610 $aUTS 610 $atopological optimization 610 $aartificial neural networks (ANN) 610 $amachine learning (ML) 610 $apress-brake bending 610 $aair-bending 610 $athree-point bending test 610 $asheet metal 610 $abuckling instability 610 $aoil canning 610 $aartificial intelligence 610 $aconvolution neural network 610 $ahot rolled strip steel 610 $adefect classification 610 $agenerative adversarial network 610 $aattention mechanism 610 $adeep learning 610 $amechanical constitutive model 610 $afinite element analysis 610 $aplasticity 610 $aparameter identification 610 $afull-field measurements 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 615 7$aMining technology & engineering 700 $aPrates$b Pedro$4edt$01331649 702 $aPereira$b Andre?$4edt 702 $aPrates$b Pedro$4oth 702 $aPereira$b Andre?$4oth 906 $aBOOK 912 $a9910637782503321 996 $aRecent Advances and Applications of Machine Learning in Metal Forming Processes$93040531 997 $aUNINA