Artificial Neural Network Prediction in Metal Forming Processes
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 843
Special Issue Editors
Interests: large plastic deformations; inverse analysis; applications to metal forming; material parameters identification; modeling and mechanical behaviour of carbon nanotubes
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; pattern recognition; financial engineering; text classification; signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: sheet metal forming; material parameters’ identification; inverse analysis; optimization; metamodeling; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial neural networks (ANN) are already being used to solve classification and regression problems in metal forming processes, such as formability analysis, process optimization, and tool design. In this context, ANN-based techniques can be combined with real and/or synthetic data to model the non-linear relationships between the parameters of the forming process and the final quality of the components, such as their geometric features, the constitutive parameters of the materials, the occurrence of defects, and the estimation of component costs.
In this Special Issue, we welcome articles whose results, obtained in different applications to metal forming processes, show the potential of artificial-neural-network-based techniques.
Prof. Dr. José Valdemar Fernandes
Prof. Dr. Bernardete Ribeiro
Prof. Dr. Pedro Prates
Guest Editors
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Keywords
- metal forming processes
- artificial neural networks
- machine learning
- deep learning
- data-driven
- process optimization
- defect prediction
- models calibration
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