03865nam 2200961z- 450 991063778250332120231214133201.03-0365-5772-5(CKB)5470000001631712(oapen)https://directory.doabooks.org/handle/20.500.12854/94546(EXLCZ)99547000000163171220202212d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierRecent Advances and Applications of Machine Learning in Metal Forming ProcessesBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (210 p.)3-0365-5771-7 Machine 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.Technology: general issuesbicsscHistory of engineering & technologybicsscMining technology & engineeringbicsscsheet metal forminguncertainty analysismetamodelingmachine learninghot rolling stripedge defectsintelligent recognitionconvolutional neural networksdeep-drawingkriging metamodelingmulti-objective optimizationFE (Finite Element) AutoForm robust analysisdefect predictionmechanical properties predictionhigh-dimensional datafeature selectionmaximum information coefficientcomplex network clusteringring rollingprocess energy estimationmetal formingthermo-mechanical FEM analysisartificial neural networkaluminum alloymechanical propertyUTStopological optimizationartificial neural networks (ANN)machine learning (ML)press-brake bendingair-bendingthree-point bending testsheet metalbuckling instabilityoil canningartificial intelligenceconvolution neural networkhot rolled strip steeldefect classificationgenerative adversarial networkattention mechanismdeep learningmechanical constitutive modelfinite element analysisplasticityparameter identificationfull-field measurementsTechnology: general issuesHistory of engineering & technologyMining technology & engineeringPrates Pedroedt1331649Pereira AndréedtPrates PedroothPereira AndréothBOOK9910637782503321Recent Advances and Applications of Machine Learning in Metal Forming Processes3040531UNINA