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Metals
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Machine Learning Models in Metals
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Machine Learning Models in 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: closed (20 March 2025) | Viewed by 11778

Special Issue Editor

Prof. Dr. Olivier Pantale

E-Mail Website
Guest Editor
Laboratoire Génie de Production/ENIT, Institut National Polytechnique de Toulouse, 65000 Tarbes, France
Interests: artificial neural networks; finite elements; metal forming; identification of behavior laws; programming; mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational methods and simulations have greatly contributed to our understanding of the properties and behavior of metals. The integration of machine learning models, particularly neural networks, into the simulation of metal processes represents a significant stride forward in the field. In manufacturing, machine learning algorithms can optimize production processes by analyzing vast datasets in real-time, leading to increased efficiency and cost savings. Moreover, machine learning-driven material discovery has yielded exciting results, with algorithms identifying novel metal alloys with tailored properties for specific applications, such as lightweight yet strong materials for the aerospace industry. Additionally, characterizing complex microstructures and grain boundaries in metals has become more precise and efficient with neural networks, enabling researchers to better understand the relationship between microstructure and material performance. Overall, machine learning is transforming metallurgy, materials design, and numerous industries that rely on metals by accelerating innovation and enabling data-driven decision-making.

We invite researchers, scientists, and experts in the field to contribute to this Special Issue of Metals, entitled "Machine Learning Models in Metals." This Special Issue aims to provide a platform for the dissemination of cutting-edge research, novel methodologies, and innovative applications that harness the power of machine learning in the study of metals.

Suggested themes and article types for submissions

In this Special Issue, original research articles and reviews are welcome. Papers for this Special Issue should address, but are not limited to, the following topics:

  • Machine learning-driven material discovery for novel metal alloys;
  • Predictive modeling of mechanical properties, including tensile strength, hardness, and ductility;
  • Computational techniques for optimizing metal manufacturing processes;
  • Predictive modeling of metal corrosion and degradation;
  • Machine learning-based defect detection and quality control in metal production;
  • Data-driven approaches to understand metal–metal and metal–environment interactions;
  • Machine learning techniques for characterizing microstructures and grain boundaries in metals;
  • Applications of neural networks, deep learning, and reinforcement learning in metallurgy;
  • Data-driven insights into metal behavior under extreme conditions, such as high temperature or pressure.

Prof. Dr. Olivier Pantale
Guest Editor

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

  • artificial neural networks
  • machine learning in metallurgy
  • deep learning
  • data-driven materials science
  • manufacturing process modeling and simulation
  • metal alloy simulations
  • metal properties modeling
  • metal structure prediction
  • computational materials science
  • metal property prediction models

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  • 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 (8 papers)

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Research

22 pages, 17592 KiB  
Article
Impact of Feature-Selection in a Data-Driven Method for Flow Curve Identification of Sheet Metal
by Quang Ninh Hoang, Hyungbum Park, Dang Giang Lai, Sy-Ngoc Nguyen, Quoc Tuan Pham and Van Duy Dinh
Metals 2025, 15(4), 392; https://doi.org/10.3390/met15040392 - 31 Mar 2025
Viewed by 553
Abstract
This study presents an innovative data-driven methodology to model the hardening behavior of sheet metals across a broad strain range, crucial for understanding sheet metal mechanics. Conventionally, true stress–strain data from such tests are used to analyze plastic flow within the pre-necking regime, [...] Read more.
This study presents an innovative data-driven methodology to model the hardening behavior of sheet metals across a broad strain range, crucial for understanding sheet metal mechanics. Conventionally, true stress–strain data from such tests are used to analyze plastic flow within the pre-necking regime, often requiring additional experiments to inverse finite element methods, which demand extensive field data for improved accuracy. Although digital image correlation offers precise data, its implementation is costly. To address this, we integrate experimental data from standard tensile tests with a machine-learning approach to estimate the flow curve. Subsequently, we conduct finite element simulations on uniaxial tensile tests, using materials characterized by the Swift constitutive equation to build a comprehensive database. Loading force-gripper displacement curves from these simulations are then transformed into input features for model training. We propose and compare three models—Models A, B, and C—each employing different input feature selections to estimate the flow curve. Experimental validation including uniaxial tensile, plane strain, and simple shear tests on the DP590 and DP780 sheets are then carefully considered. Results demonstrate the effectiveness of our proposed method, with Model C showing the highest efficacy. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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24 pages, 1927 KiB  
Article
Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling
by Alexey G. Zinyagin, Alexander V. Muntin, Vadim S. Tynchenko, Pavel I. Zhikharev, Nikita R. Borisenko and Ivan Malashin
Metals 2024, 14(12), 1329; https://doi.org/10.3390/met14121329 - 24 Nov 2024
Cited by 2 | Viewed by 1028
Abstract
This study addresses the usage of data from industrial plate mills to calculate the mean flow stress of different steel grades. Accurate flow stress values may optimize rolling technology, but the existing literature often provides coefficients like those in the Hensel–Spittel equation for [...] Read more.
This study addresses the usage of data from industrial plate mills to calculate the mean flow stress of different steel grades. Accurate flow stress values may optimize rolling technology, but the existing literature often provides coefficients like those in the Hensel–Spittel equation for a limited number of steel grades, whereas in modern production, the chemical composition may vary by thickness, customer requirements, and economic factors, making it necessary to conduct costly and labor-intensive laboratory studies. This research demonstrates that leveraging data from industrial rolling mills and employing machine learning (ML) methods can predict material rheological behavior without extensive laboratory research. Two modeling approaches are employed: Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. The model comprising one GRU layer and two fully connected layers, each containing 32 neurons, yields the best performance, achieving a Root Mean Squared Error (RMSE) of 7.5 MPa for the predicted flow stress of three steel grades in the validation set. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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13 pages, 3078 KiB  
Article
Machine-Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys
by Yingyu Gao, Yunfeng Hu, Xinpeng Zhao, Yang Liu, Haiyou Huang and Yanjing Su
Metals 2024, 14(10), 1193; https://doi.org/10.3390/met14101193 - 20 Oct 2024
Cited by 1 | Viewed by 1632
Abstract
In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. [...] Read more.
In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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21 pages, 4955 KiB  
Article
Efficient Phase Segmentation of Light-Optical Microscopy Images of Highly Complex Microstructures Using a Correlative Approach in Combination with Deep Learning Techniques
by Björn-Ivo Bachmann, Martin Müller, Marie Stiefel, Dominik Britz, Thorsten Staudt and Frank Mücklich
Metals 2024, 14(9), 1051; https://doi.org/10.3390/met14091051 - 14 Sep 2024
Viewed by 1110
Abstract
Reliable microstructure characterization is essential for establishing process–microstructure–property links and effective quality control. Traditional manual microstructure analysis often struggles with objectivity, reproducibility, and scalability, particularly in complex materials. Machine learning methods offer a promising alternative but are hindered by the challenge of assigning [...] Read more.
Reliable microstructure characterization is essential for establishing process–microstructure–property links and effective quality control. Traditional manual microstructure analysis often struggles with objectivity, reproducibility, and scalability, particularly in complex materials. Machine learning methods offer a promising alternative but are hindered by the challenge of assigning an accurate and consistent ground truth, especially for complex microstructures. This paper introduces a methodology that uses correlative microscopy—combining light optical microscopy, scanning electron microscopy, and electron backscatter diffraction (EBSD)—to create objective, reproducible pixel-by-pixel annotations for ML training. In a semi-automated manner, EBSD-based annotations are employed to generate an objective ground truth mask for training a semantic segmentation model for quantifying simple light optical micrographs. The training masks are directly derived from raw EBSD data using modern deep learning methods. By using EBSD-based annotations, which incorporate crystallographic and misorientation data, the correctness and objectivity of the training mask creation can be assured. The final approach is capable of reproducibly and objectively differentiating bainite and martensite in optical micrographs of complex quenched steels. Through the reduction in the microstructural evaluation to light optical micrographs as the simplest and most widely used method, this way of quantifying microstructures is characterized by high efficiency as well as good scalability. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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18 pages, 1469 KiB  
Article
Exploring the Impact of Pre-Mechanical Activation of Nickel Powder on the Structure of Deposited Metal: A Deep Neural Network Perspective
by Ivan Malashin, Nikolay Kobernik, Alexandr Pankratov, Yuri Andriyanov, Vitalina Aleksandrova, Vadim Tynchenko, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Dmitry Martysyuk and Andrey Galinovsky
Metals 2024, 14(8), 929; https://doi.org/10.3390/met14080929 - 15 Aug 2024
Cited by 1 | Viewed by 1069
Abstract
This study explores the potential application of the mechanical activation (MA) of nickel powder for incorporation into the composition of powder wire blends for the deposition of wear-resistant coatings. Nickel powder of PNE-1 grade was processed in a vibrational mill for various durations [...] Read more.
This study explores the potential application of the mechanical activation (MA) of nickel powder for incorporation into the composition of powder wire blends for the deposition of wear-resistant coatings. Nickel powder of PNE-1 grade was processed in a vibrational mill for various durations (4 to 16 min) with different combinations of grinding media. The influence of MA parameters on the bulk density and apparent particle size of nickel powder was investigated. The greatest effect was observed at the maximum processing time of 16 min, where electron microscopy revealed significant deformation and an increase in discoid particles, leading to enhanced energy accumulation. Nickel powder processed with a combination of 6 balls that are 20 mm in diameter and 8 balls that are 10 mm in diameter showed significant changes, though no major alteration in chemical composition was noted. XRMA indicated that the powder’s surface was partially covered with oxides, with a composition of 96.8–98.4% Ni and 0.8–1.7% O2. Additionally, the effect of nickel powders after the treatment on the structure of deposited metal was determined, demonstrating alterations in the morphology and a slight increase in hardness. Furthermore, a convolutional neural network (CNN)-based approach was proposed to discern fragments within images depicting surface microstructures, both with and without MA. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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18 pages, 867 KiB  
Article
On Least Squares Support Vector Regression for Predicting Mechanical Properties of Steel Rebars
by Renan Bessa, Guilherme Alencar Barreto, David Nascimento Coelho, Elineudo Pinho de Moura and Raphaella Hermont Fonseca Murta
Metals 2024, 14(6), 695; https://doi.org/10.3390/met14060695 - 12 Jun 2024
Cited by 4 | Viewed by 1615
Abstract
Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in [...] Read more.
Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in mind, we revisit this problem by developing a design methodology that amalgamates two powerful concepts in parsimonious model building: (i) sparsity, in the sense that few support vectors are required for building the predictive model, and (ii) locality, in the sense that simpler models can be fitted to smaller data partitions. In this regard, two regression models based on the Least Squares Support Vector Regression (LSSVR) model are developed. The first one is an improved sparse version of the one introduced in a previous work. The second one is a novel local LSSVR-based regression model. The task of interest is the prediction of four output variables (the mechanical properties YS, UTS, UTS/YS, and PE) based on information about its chemical composition (12 variables) and the parameters of the heat treatment rolling (6 variables). The proposed LSSVR-based regression models are evaluated using real-world data collected from steel rebar manufacturing and compared with the global LSSVR model. The local sparse LSSVR approach was able to consistently outperform the standard single regression model approach in the task of interest, achieving improvements in the average R2 from previous studies: 5.04% for UTS, 5.19% for YS, 1.96% for UTS/YS, and 3.41% for PE. Furthermore, the sparsification of the dataset and the local modeling approach significantly reduce the number of SV operations on average, utilizing 34.0% of the total SVs available for UTS estimation, 44.0% for YS, 31.3% for UTS/YS, and 32.8% for PE. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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20 pages, 5234 KiB  
Article
SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects
by Yueyang Wu, Ruihan Chen, Zhi Li, Minhua Ye and Ming Dai
Metals 2024, 14(6), 650; https://doi.org/10.3390/met14060650 - 30 May 2024
Cited by 3 | Viewed by 1566
Abstract
Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and [...] Read more.
Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model’s adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model’s mAP50 still exceeds 70%, demonstrating the model’s strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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19 pages, 4453 KiB  
Article
Machine Learning-Based Prediction of Elastic Properties Using Reduced Datasets of Accurate Calculations Results
by Kirill Sidnov, Denis Konov, Ekaterina A. Smirnova, Alena V. Ponomareva and Maxim P. Belov
Metals 2024, 14(4), 438; https://doi.org/10.3390/met14040438 - 10 Apr 2024
Cited by 2 | Viewed by 1680
Abstract
In this paper, the applicability of machine learning for predicting the elastic properties of binary and ternary bcc Ti and Zr disordered alloys with 34 different doping elements is explored. The original dataset contained 3 independent elastic constants, bulk moduli, shear moduli, and [...] Read more.
In this paper, the applicability of machine learning for predicting the elastic properties of binary and ternary bcc Ti and Zr disordered alloys with 34 different doping elements is explored. The original dataset contained 3 independent elastic constants, bulk moduli, shear moduli, and Young’s moduli of 1642 compositions calculated using the EMTO-CPA method and PAW-SQS calculation results for 62 compositions. The architecture of the system is made as a pipeline of a pair of predicting blocks. The first one took as the input a set of descriptors of the qualitative and quantitative compositions of alloys and approximated the EMTO-CPA data, and the second one took predictions of the first model and trained on the results of the PAW-SQS calculations. The main idea of such architecture is to achieve prediction accuracy at the PAW-SQS level, while reducing the resource intensity for obtaining the training set by a multiple of the ratio of the training subsets sizes corresponding to the two used calculation methods (EMTO-CPA/PAW-SQS). As a result, model building and testing methods accounting for the lack of accurate training data on the mechanical properties of alloys (PAW-SQS), balanced out by using predictions of inaccurate resource-effective first-principle calculations (EMTO-CPA), are demonstrated. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals)
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