Recent Advances and Applications of Machine Learning in Metal Forming Processes

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 rel...

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Bibliographische Detailangaben
Hauptverfasser:	Prates, Pedro, Pereira, André
Format:	Buch
Sprache:	eng
Schlagworte:	air-bending aluminum alloy artificial intelligence artificial neural network artificial neural networks (ANN) attention mechanism buckling instability complex network clustering convolution neural network convolutional neural networks deep learning deep-drawing defect classification defect prediction edge defects FE (Finite Element) AutoForm robust analysis feature selection finite element analysis full-field measurements generative adversarial network high-dimensional data History of engineering and technology hot rolled strip steel hot rolling strip intelligent recognition kriging metamodeling machine learning machine learning (ML) maximum information coefficient mechanical constitutive model mechanical properties prediction mechanical property metal forming metamodeling Mining technology and engineering multi-objective optimization n/a oil canning Other technologies and applied sciences parameter identification plasticity press-brake bending process energy estimation ring rolling sheet metal sheet metal forming Technology, Engineering, Agriculture, Industrial processes Technology: general issues thermo-mechanical FEM analysis three-point bending test topological optimization uncertainty analysis UTS
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Beschreibung
Zusammenfassung:	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.
DOI:	10.3390/books978-3-0365-5772-4