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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Detalles Bibliográficos
Otros Autores:	Prates, Pedro (Editor), Pereira, André (Otro)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Materias:	Technology: general issues History of engineering & technology Mining technology & engineering 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
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009828017706719