Recent Advances and Applications of Machine Learning in Metal Forming Processes

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
Descripción
Sumario: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.
Descripción Física:1 electronic resource (210 p.)
ISBN:9783036557724

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