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Recent Advances and Applications of Machine Learning in Metal Forming Processes
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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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Keywords
- air-bending
- aluminum alloy
- artificial intelligence
- artificial neural network
- artificial neural networks (ANN)
- attention mechanism
- buckling instability
- complex network clustering
- convolution neural network
- convolutional neural networks
- deep learning
- deep-drawing
- defect classification
- defect prediction
- edge defects
- FE (Finite Element) AutoForm robust analysis
- feature selection
- finite element analysis
- full-field measurements
- generative adversarial network
- High-dimensional data
- History of engineering & technology
- hot rolled strip steel
- hot rolling strip
- intelligent recognition
- kriging metamodeling
- Machine learning
- machine learning (ML)
- maximum information coefficient
- mechanical constitutive model
- mechanical properties prediction
- Mechanical Property
- metal forming
- Metamodeling
- Mining technology & engineering
- multi-objective optimization
- n/a
- oil canning
- Other technologies & applied sciences
- parameter identification
- Plasticity
- press-brake bending
- process energy estimation
- ring rolling
- sheet metal
- sheet metal forming
- Technology, engineering, agriculture
- Technology: general issues
- thermo-mechanical FEM analysis
- three-point bending test
- Topological optimization
- uncertainty analysis
- UTS
Links
DOI: 10.3390/books978-3-0365-5772-4Editions