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Metals
Special Issues
Advances in Metal Forming and Plasticity
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Advances in Metal Forming and Plasticity

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 660

Special Issue Editors

Dr. Gabriela Vincze

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: microstructure/property relationships; plasticity; formability; crystallographic texture and constitutive modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Marilena Butuc

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: plasticity; formability; constitutive modeling; fracture; advanced high strength steels; aluminum alloys
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metal forming is one of the most efficient and economical manufacturing processes, driving innovation across various industries. It enables the development of a wide range of products and components, from heavy equipment and infrastructure to cutting-edge technologies like battery packs and microprocessors. The overall quality of the products is strongly influenced by the intrinsic properties of the materials used, the conditions under which they are processed, and their behavior in real-world applications.

Key factors shaping mechanical properties include the material’s chemical composition, state of precipitation, work hardening, and crystallographic texture. Forming processes, which often involve large deformations and complex strain paths, are employed to produce increasingly complex shapes. However, these processes can also introduce challenges such as springback-induced distortion, material softening, the early onset of plastic instability, and fractures.

This Special Issue aims to explore the latest research on metal forming processes, including theoretical, numerical, and experimental approaches, focusing on the full spectrum from material structure to industrial application. We invite submissions of full papers, communications, and review articles that contribute to this important area of research.

Dr. Gabriela Vincze
Dr. Marilena Butuc
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

  • plasticity
  • formability
  • fracture
  • anisotropy
  • mechanical behavior
  • springback
  • microstructure
  • texture
  • multiscale modelingmodelling
  • numerical simulation
  • experimental mechanics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

17 pages, 1001 KiB  
Article
Reducing Mesh Dependency in Dataset Generation for Machine Learning Prediction of Constitutive Parameters in Sheet Metal Forming
by Dário Mitreiro, Pedro A. Prates and António Andrade-Campos
Metals 2025, 15(5), 534; https://doi.org/10.3390/met15050534 - 10 May 2025
Viewed by 359
Abstract
Given the extensive use of sheet metal-forming processes in the industry and the constant emergence of new materials, the accurate prediction of material constitutive models and their parameters is extremely important to enhance and optimise these processes. Machine learning techniques have proven to [...] Read more.
Given the extensive use of sheet metal-forming processes in the industry and the constant emergence of new materials, the accurate prediction of material constitutive models and their parameters is extremely important to enhance and optimise these processes. Machine learning techniques have proven to be highly promising for predicting these parameters using data obtained either experimentally or through numerical simulations. However, ML models are often constrained by the limited dataset coverage from numerical simulations, which restricts their predictive capability to specific finite element meshes, leading to potential dependency on the discretisation scheme. To address this challenge, a new approach is proposed that integrates ML with inter-extrapolation of strain data to a grid of points within the specimen domain, expanding the dataset coverage and reducing dependency on discrete mesh points. The current work explores this approach by interpolating and extrapolating manipulated data obtained from a Finite Element Analysis, considering a biaxial tensile test on a cruciform-shaped sample. Models are trained and evaluated for performance and robustness. The results show the high accuracy of the interpolated data, along with the excellent performance metrics and robustness of the trained models, ensuring the successful implementation of this approach. Full article
(This article belongs to the Special Issue Advances in Metal Forming and Plasticity)
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