Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Metals
Special Issues
Application of Machine Learning in Metallic Materials
share announcement

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 2736

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 6856 KB  
Article
Development of Machine Learning Model for Analysis of Total Manufacturing Cost in Medium Turning of C45E Steel
by Miloš Madić, Milan Trifunović, Dragan Rodić and Dragan Marinković
Metals 2026, 16(4), 373; https://doi.org/10.3390/met16040373 - 28 Mar 2026
Viewed by 384
Abstract
The primary goal of manufacturing technologies in the metalworking industry is to provide products with specified quality characteristics, while maximizing time and cost efficiency. The total manufacturing cost in turning depends on a number of factors. The analysis of their effects and the [...] Read more.
The primary goal of manufacturing technologies in the metalworking industry is to provide products with specified quality characteristics, while maximizing time and cost efficiency. The total manufacturing cost in turning depends on a number of factors. The analysis of their effects and the estimation of the total manufacturing cost are of practical importance in process planning. Therefore, in the present study, the relationship between four inputs (depth of cut, feed rate, cutting speed and volume of material to be removed) and the total manufacturing cost in medium turning of C45E steel was modeled by using an artificial neural network (ANN). The developed ANN model was used for the analysis of the main and interaction effects of the aforementioned inputs on the total manufacturing cost. Verification of the observed effects was also carried out by applying the connection weight approach. The total manufacturing cost was mostly affected by depth of cut, while the effect of cutting speed was least pronounced. In addition, the results also revealed the presence of two-way interactions associated with cutting speed. For the given case study (with defined volume of material to be removed and specified machine tool), an optimized cutting regime was determined by developing and solving a single-objective turning optimization problem with three constraints related to chip slenderness, cutting power and depth of cut. Cutting force, needed for the estimation of cutting power, was estimated by using the dimensional analysis-based prediction model. Full article
(This article belongs to the Special Issue Application of Machine Learning in Metallic Materials)
►▼ Show Figures

Graphical abstract

18 pages, 3765 KB  
Article
Prediction of Specific Energy Consumption in Sustainable Milling of Ti-6Al-4V with Different Machine Learning Models
by Djordje Cica, Sasa Tesic, Branislav Sredanovic, Dejan Vujasin, Milan Zeljkovic, Franci Pusavec and Davorin Kramar
Metals 2026, 16(3), 266; https://doi.org/10.3390/met16030266 - 27 Feb 2026
Viewed by 368
Abstract
Research on eco-friendly and energy-efficient machining processes has gained significant importance within the domain of sustainable production. This study is focused on enhancing the energy performance and sustainability of the milling process. Four machine learning (ML) models, namely, multiple linear regression (MLR), support [...] Read more.
Research on eco-friendly and energy-efficient machining processes has gained significant importance within the domain of sustainable production. This study is focused on enhancing the energy performance and sustainability of the milling process. Four machine learning (ML) models, namely, multiple linear regression (MLR), support vector regression (SVR), Gaussian process regression (GPR), and adaptive network-based fuzzy inference system (ANFIS), were proposed to estimate specific energy consumption (SEC) in the milling of Ti6-Al4-V under two eco-benign cooling conditions: cryogenic and minimum quantity lubrication (MQL). Several statistical metrics, including normalized mean absolute error (nMAE), mean absolute percentage error (MAPE), normalized root mean square error (nRMSE), maximum absolute percentage error (maxAPE), coefficient of determination (R2), and Willmott’s index of agreement (IA), were employed to validate the performances of the ML models. A high level of agreement between the predicted and experimental SEC data for both the training and test datasets supports the reliability of the proposed ML models. Although the MLR model performed well, the results revealed that the other ML models demonstrated better overall performance. According to the statistical metrics, the models’ predictive performance improved in the following sequence: MLR, SVR, GPR, and finally ANFIS, which demonstrated the highest predictive capability. Full article
(This article belongs to the Special Issue Application of Machine Learning in Metallic Materials)
►▼ Show Figures

Figure 1

16 pages, 2907 KB  
Article
Parallel Hybrid Modeling of Al–Mg–Si Tensile Properties Using Density-Based Weighting
by Christian Dalheim Øien, Ole Runar Myhr and Geir Ringen
Metals 2026, 16(2), 142; https://doi.org/10.3390/met16020142 - 25 Jan 2026
Viewed by 471
Abstract
A hybrid modeling framework for predicting the mechanical properties of Al-Mg-Si alloys, that blends physics-based and machine-learning models, is developed and tested. Motivated by a demand for post-consumer material (PCM) content in wrought aluminium applications, this work proposes, analyses, and discusses a parallel [...] Read more.
A hybrid modeling framework for predicting the mechanical properties of Al-Mg-Si alloys, that blends physics-based and machine-learning models, is developed and tested. Motivated by a demand for post-consumer material (PCM) content in wrought aluminium applications, this work proposes, analyses, and discusses a parallel framework that applies an adaptive weighting coefficient derived from local observation density. Based on existing datasets from a range of Al-Mg-Si alloys, such a model is trained and tested in an iterative manner to study its robustness, by emulating a shift in observed alloy composition. The results indicate that the hybrid model is able to combine the interpolative strength of machine learning for cases similar to previous observations with the explorative strength of physics-based (Kampmann–Wagner Numerical) modeling for previously unobserved parameter combinations, as the hybrid model shows higher or similar accuracy than the best of its constituents across the majority of the sequence. The observed model characteristics are promising for predicting the effect of increased compositional variation inherent in PCM. Finally, possible future research is discussed. Full article
(This article belongs to the Special Issue Application of Machine Learning in Metallic Materials)
►▼ Show Figures

Figure 1

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
Cited by 1 | Viewed by 1021
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop