Recent Advances and Applications of Machine Learning in Metal Forming Processes
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 32455
A printed edition of this Special Issue is available here.
2. Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), University of Coimbra, 3030-788 Coimbra, Portugal
Interests: sheet metal forming; material parameters identification; inverse analysis; optimization; metamodeling; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: sheet metal forming processes; material parameters identification; metamodeling; stochastic analysis; sensitivity analysis; numerical simulation; mechanical behaviour of carbon nanotubes
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. Contributions in the form of full papers, reviews, and communications about the abovementioned and related topics are very welcome.
Prof. Dr. Pedro Prates
Dr. André Pereira
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- metal forming processes
- machine learning
- numerical simulation
- defect prediction
- material parameter identification
- material modeling
- deep learning