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Metals
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Application of Machine Learning in Metallic Materials
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Application of Machine Learning in Metallic Materials

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 376

Special Issue Editors

Dr. Miha Kovačič

E-Mail Website
Guest Editor
1. Štore Steel d.o.o., Železarska Cesta 3, 3220 Štore, Slovenia
2. Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia
3. College of Industrial Engineering, Mariborska cesta 2, 3000 Celje, Slovenia
Interests: optimization; modeling; applied artificial intelligence; evolutionary computation; genetic algorithm; genetic programming
Special Issues, Collections and Topics in MDPI journals
Dr. Uroš Župerl

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: control systems; cyber-physical systems; machining; optimization; modeling; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of machine learning techniques—continually relevant across scientific and engineering disciplines—has opened new frontiers in the design, processing, characterization, and performance prediction of metallic materials. By harnessing data-driven approaches, researchers and engineers can uncover hidden correlations within complex datasets, optimize manufacturing processes, and accelerate the development of novel metallic materials.

This Special Issue will compile cutting-edge research demonstrating how machine learning can address persistent challenges in the field of metallic materials. Contributions presenting practical solutions and examples of industrial implementation are particularly encouraged.

This Special Issue in Metals covers recent advances in the modeling, prediction, and optimization of various processes related to metallic materials, including but, not limited to, primary processes (e.g., casting, powder metallurgy), deformation processes (e.g., rolling, forging, deep drawing), heat treatment, surface engineering (e.g., electroplating, thermal spraying), joining processes (e.g., welding, soldering), and machining operations (e.g., drilling, grinding, polishing).

Dr. Miha Kovačič
Dr. Uroš Župerl
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 250 words) can be sent to the Editorial Office for assessment.

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

  • metallic materials
  • machine learning
  • modeling
  • prediction
  • optimization
  • practical implementation
  • industrial study

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Published Papers (1 paper)

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Research

24 pages, 12828 KB  
Article
Surrogate-Model Prediction of Mechanical Response in Architected Ti6Al4V Cylindrical TPMS Metamaterials
by Mansoureh Rezapourian, Ali Cheloee Darabi, Mohammadreza Khoshbin, Siegfried Schmauder and Irina Hussainova
Metals 2025, 15(12), 1372; https://doi.org/10.3390/met15121372 - 15 Dec 2025
Viewed by 194
Abstract
A Machine Learning (ML)-based surrogate modeling framework is presented for mapping structure–property relationships in architected Ti6Al4V cylindrical TPMS metamaterials subjected to quasi-static compression. A Python–nTop pipeline automatically generated 3456 cylindrical shell lattices (Gyroid, Diamond, Split-P), and ABAQUS/Explicit simulations with a Johnson–Cook failure model [...] Read more.
A Machine Learning (ML)-based surrogate modeling framework is presented for mapping structure–property relationships in architected Ti6Al4V cylindrical TPMS metamaterials subjected to quasi-static compression. A Python–nTop pipeline automatically generated 3456 cylindrical shell lattices (Gyroid, Diamond, Split-P), and ABAQUS/Explicit simulations with a Johnson–Cook failure model for Ti6Al4V quantified their mechanical response. From 3024 valid designs, key mechanical properties targets including elastic modulus (E), yield stress (Y), ultimate strength (U), plateau stress (PL), and energy absorption (EA) were extracted alongside geometric descriptors such as surface area (SA), surface-area-to-volume ratio (SA/VR), and relative density (RD). A multi-output surrogate model (feedforward neural network) trained on the simulated set accurately predicts these properties directly from seven design parameters (thickness; unit cell counts in X, Y, and Z directions; unit cell orientation; height; diameter), enabling rapid property estimation across large design spaces. Topology-dependent trends indicate that Split-P exhibits the highest strength, energy absorption, and total SA, and shows the largest variation in SA/VR; Gyroid exhibits the lowest SA with a moderate SA/VR; and Diamond is the most compliant lattice and maintains a higher SA/VR than Gyroid despite lower SA. RD increases with both SA and SA/VR across all topologies. The framework provides a reusable computational tool for architectured lattices, enabling quick prescreening of implant designs without repeated finite-element analyses. Full article
(This article belongs to the Special Issue Application of Machine Learning in Metallic Materials)
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