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dc.contributor.editorPrates, Pedro
dc.contributor.editorPereira, André
dc.date.accessioned2022-12-06T16:11:05Z
dc.date.available2022-12-06T16:11:05Z
dc.date.issued2022
dc.identifierONIX_20221206_9783036557717_69
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/94546
dc.description.abstractMachine 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TT Other technologies and applied sciences::TTU Mining technology and engineeringen_US
dc.subject.othersheet metal forming
dc.subject.otheruncertainty analysis
dc.subject.othermetamodeling
dc.subject.othermachine learning
dc.subject.otherhot rolling strip
dc.subject.otheredge defects
dc.subject.otherintelligent recognition
dc.subject.otherconvolutional neural networks
dc.subject.otherdeep-drawing
dc.subject.otherkriging metamodeling
dc.subject.othermulti-objective optimization
dc.subject.otherFE (Finite Element) AutoForm robust analysis
dc.subject.otherdefect prediction
dc.subject.othermechanical properties prediction
dc.subject.otherhigh-dimensional data
dc.subject.otherfeature selection
dc.subject.othermaximum information coefficient
dc.subject.othercomplex network clustering
dc.subject.otherring rolling
dc.subject.otherprocess energy estimation
dc.subject.othermetal forming
dc.subject.otherthermo-mechanical FEM analysis
dc.subject.otherartificial neural network
dc.subject.otheraluminum alloy
dc.subject.othermechanical property
dc.subject.otherUTS
dc.subject.othertopological optimization
dc.subject.otherartificial neural networks (ANN)
dc.subject.othermachine learning (ML)
dc.subject.otherpress-brake bending
dc.subject.otherair-bending
dc.subject.otherthree-point bending test
dc.subject.othersheet metal
dc.subject.otherbuckling instability
dc.subject.otheroil canning
dc.subject.otherartificial intelligence
dc.subject.otherconvolution neural network
dc.subject.otherhot rolled strip steel
dc.subject.otherdefect classification
dc.subject.othergenerative adversarial network
dc.subject.otherattention mechanism
dc.subject.otherdeep learning
dc.subject.othermechanical constitutive model
dc.subject.otherfinite element analysis
dc.subject.otherplasticity
dc.subject.otherparameter identification
dc.subject.otherfull-field measurements
dc.subject.othern/a
dc.titleRecent Advances and Applications of Machine Learning in Metal Forming Processes
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-5772-4
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036557717
oapen.relation.isbn9783036557724
oapen.pages210
oapen.place.publicationBasel


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