Physics-Based and Data-Driven Modelling of Process-Structure-Property (PSP) Linkage of Structural Metals
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".
Deadline for manuscript submissions: 25 June 2024 | Viewed by 127
Special Issue Editors
Interests: severe plastic deformation; microstructure/texture characterization; finite element method; crystal plasticity; machine learning; processing–structure–property (PSP) relation
Special Issues, Collections and Topics in MDPI journals
Interests: advanced manufacturing; friction and wear; severe plastic deformation; microstructure/texture characterisation; advanced modelling; deformation mechanism; mechanics of materials; residual stress analysis; X-ray/neutron/synchrotron diffraction; advanced and emerging materials; high-entropy alloys; corrosion and erosion of materials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Metal forming/processing involves a series of thermo-mechanical deformations. Hierarchical structured materials develop during processing, which determines the final metal’s properties. An efficient approach to accelerate material development is to establish the Process–Structure–Property (PSP) linkages. This is beneficial to forward property prediction, which also enables finding optimal architected structures for given target properties in inverse material design. In addition, it accelerates the design, characterisation, evaluation, and deployment of metals.
Physics-based modelling has become an effective and efficient tool in material development due to increased computational resources, improved numerical algorithms, and progressed physical models. The application of machine learning and big data in materials science is unveiling hidden PSP relationships and can be harnessed in inverse design, e.g., optimizing processing and discovering materials. Combining materials informatics with computational materials science enables the closed-loop study of materials science, where computational materials science generates datasets and material informatics guides simulations.
This Special Issue aims to cover the latest advances in establishing PSP linkages using physics-based computational material science and machine learning methods. In this regard, original research papers, short communications, and review articles studying the following subjects are welcome in this Special Issue: metal forming/processing; microstructure characterisation; computational material science; machine learning; and data-driven materials design.
Dr. Hui Wang
Dr. Lihong Su
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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.
Keywords
- metal forming/processing
- plastic deformation
- mechanical properties
- mechanical testing
- microstructure characterisation
- computational material science
- machine learning
- data-driven material design
