Recent Advances and Applications of Machine Learning in Metal Forming Processes

• Autore: Prates Pedro
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 electronic resource (210 p.)
• Soggetto topico: Technology: general issues; History of engineering & technology; Mining technology & engineering
• Soggetto non controllato: 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
• Persona (resp. second.): PereiraAndré; PratesPedro
• Sommario/riassunto: 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.
• Titolo autorizzato: Recent Advances and Applications of Machine Learning in Metal Forming Processes
• ISBN: 3-0365-5772-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910637782503321
• Lo trovi qui: Univ. Federico II