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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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (210 p.) |
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(CKB)5470000001631712 (oapen)https://directory.doabooks.org/handle/20.500.12854/94546 (EXLCZ)995470000001631712 |
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Prates, Pedro edt Recent Advances and Applications of Machine Learning in Metal Forming Processes Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (210 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English Technology: general issues bicssc History of engineering & technology bicssc Mining technology & engineering bicssc sheet metal forming uncertainty analysis metamodeling machine learning hot rolling strip edge defects intelligent recognition convolutional neural networks deep-drawing kriging metamodeling multi-objective optimization FE (Finite Element) AutoForm robust analysis defect prediction mechanical properties prediction high-dimensional data feature selection maximum information coefficient complex network clustering ring rolling process energy estimation metal forming thermo-mechanical FEM analysis artificial neural network aluminum alloy mechanical property UTS topological optimization artificial neural networks (ANN) machine learning (ML) press-brake bending air-bending three-point bending test sheet metal buckling instability oil canning artificial intelligence convolution neural network hot rolled strip steel defect classification generative adversarial network attention mechanism deep learning mechanical constitutive model finite element analysis plasticity parameter identification full-field measurements 3-0365-5771-7 Pereira, André edt Prates, Pedro oth Pereira, André oth |
| language |
English |
| format |
eBook |
| author2 |
Pereira, André Prates, Pedro Pereira, André |
| author_facet |
Pereira, André Prates, Pedro Pereira, André |
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p p pp a p ap |
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HerausgeberIn Sonstige Sonstige |
| title |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| spellingShingle |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| title_full |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| title_fullStr |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| title_full_unstemmed |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| title_auth |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| title_new |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| title_sort |
recent advances and applications of machine learning in metal forming processes |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2022 |
| physical |
1 electronic resource (210 p.) |
| isbn |
3-0365-5772-5 3-0365-5771-7 |
| illustrated |
Not Illustrated |
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Recent Advances and Applications of Machine Learning in Metal Forming Processes |
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