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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Other Authors: Prates, Pedro (Editor), Pereira, André (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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520 |a 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. 
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650 7 |a History of engineering & technology  |2 bicssc 
650 7 |a Mining technology & engineering  |2 bicssc 
653 |a sheet metal forming 
653 |a uncertainty analysis 
653 |a metamodeling 
653 |a machine learning 
653 |a hot rolling strip 
653 |a edge defects 
653 |a intelligent recognition 
653 |a convolutional neural networks 
653 |a deep-drawing 
653 |a kriging metamodeling 
653 |a multi-objective optimization 
653 |a FE (Finite Element) AutoForm robust analysis 
653 |a defect prediction 
653 |a mechanical properties prediction 
653 |a high-dimensional data 
653 |a feature selection 
653 |a maximum information coefficient 
653 |a complex network clustering 
653 |a ring rolling 
653 |a process energy estimation 
653 |a metal forming 
653 |a thermo-mechanical FEM analysis 
653 |a artificial neural network 
653 |a aluminum alloy 
653 |a mechanical property 
653 |a UTS 
653 |a topological optimization 
653 |a artificial neural networks (ANN) 
653 |a machine learning (ML) 
653 |a press-brake bending 
653 |a air-bending 
653 |a three-point bending test 
653 |a sheet metal 
653 |a buckling instability 
653 |a oil canning 
653 |a artificial intelligence 
653 |a convolution neural network 
653 |a hot rolled strip steel 
653 |a defect classification 
653 |a generative adversarial network 
653 |a attention mechanism 
653 |a deep learning 
653 |a mechanical constitutive model 
653 |a finite element analysis 
653 |a plasticity 
653 |a parameter identification 
653 |a full-field measurements 
653 |a n/a 
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