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Machine Learning Techniques in Materials Science and Engineering

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: closed (10 June 2024) | Viewed by 19442

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Guest Editor
1. College of Engineering, University of Sulaimani, Kurdistan Region, Sulaimani-Kirkuk Rd, Sulaymaniyah 46001, Iraq
2. College of Engineering, American University of Iraq, Sulaimani, Kurdistan region, Sulaimani-Kirkuk Rd, Sulaymaniyah 46001, Iraq
Interests: cement; concrete; soil mechanics; rock mechanics; sustainability; modeling
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Special Issue Information

Dear Colleagues,

Numerous modeling approaches using non-conventional adsorbents have been used to develop suitable and more effective adsorbents to eliminate and optimize experimental lab work. Since soft computing techniques are needed in composite materials engineering, they could be used in different phases of materials engineering. It is especially related to functional analysis, design, testing, prediction, and optimization. Neural networks (NNs), fuzzy logic, and evolutionary and classification algorithms are the most popular soft-computing techniques.

Articles submitted to this journal can also be concerned about the most significant recent developments in computational and numerical methods and their applications in structural engineering and Materials Engineering, including nano-sized and smart materials. We invite researchers to contribute original research articles and review articles that will stimulate the continuing research effort on applications of the soft computing approaches to model structural engineering and materials problems.

Prof. Dr. Panagiotis G. Asteris
Dr. Ahmed Salih Mohammed
Guest Editors

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Keywords

  • artificial neural networks (ANNs)
  • computational biology/bioinformatics
  • computational science and engineering
  • evolutionary multimodal optimization
  • forecasting models
  • fuzzy set theory and hybrid fuzzy models
  • genetic algorithm and genetic programming
  • heuristic models
  • hybrid intelligent systems
  • image processing and computer vision
  • machine learning techniques
  • multicriteria decision making (MCDM)
  • multiexpression programming

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Published Papers (9 papers)

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26 pages, 4834 KiB  
Article
Artificial Neural Network Model for Predicting Mechanical Strengths of Economical Ultra-High-Performance Concrete Containing Coarse Aggregates: Development and Parametric Analysis
by Ling Li, Yufei Gao, Xuan Dong and Yongping Han
Materials 2024, 17(16), 3908; https://doi.org/10.3390/ma17163908 - 7 Aug 2024
Viewed by 833
Abstract
Ultra-high-performance concrete with coarse aggregates (UHPC-CA) has the advantages of high strength, strong shrinkage resistance and a lower production cost, presenting a broad application prospect in civil engineering construction. In view of the difficulty in establishing a mathematical model to accurately predict the [...] Read more.
Ultra-high-performance concrete with coarse aggregates (UHPC-CA) has the advantages of high strength, strong shrinkage resistance and a lower production cost, presenting a broad application prospect in civil engineering construction. In view of the difficulty in establishing a mathematical model to accurately predict the mechanical properties of UHPC-CA, the back-propagation artificial neural network (BP-ANN) method is used to fully consider the various influential factors of the compressive strength (CS) and flexural strength (FS) of UHPC-CA in this paper. By taking the content of cement (C), silica fume (SF), slag, fly ash (FA), coarse aggregate (CA), steel fiber, the water–binder ratio (w/b), the sand rate (SR), the cement type (CT), and the curing method (CM) as input variables, and the CS and FS of UHPC-CA as output objectives, the BP-ANN model with three layers has been well-trained, validated and tested with 220 experimental data in the studies published in the literature. Four evaluating indicators including the determination coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the integral absolute error (IAE) were used to evaluate the prediction accuracy of the BP-ANN model. A parametric study for the various influential factors on the CS and FS of UHPC-CA was conducted using the BP-ANN model and the corresponding influential mechanisms were analyzed. Finally, the inclusion levels for the CA, steel fiber, and the dimensionless parameters of the W/B and sand rate were recommended to obtain the optimal strength of UHPC-CA. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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23 pages, 9849 KiB  
Article
Comparative Analysis of Machine Learning Models for Predicting the Mechanical Behavior of Bio-Based Cellular Composite Sandwich Structures
by Danial Sheini Dashtgoli, Seyedahmad Taghizadeh, Lorenzo Macconi and Franco Concli
Materials 2024, 17(14), 3493; https://doi.org/10.3390/ma17143493 - 15 Jul 2024
Cited by 1 | Viewed by 2215
Abstract
The growing demand for sustainable materials has significantly increased interest in biocomposites, which are made from renewable raw materials and have excellent mechanical properties. The use of machine learning (ML) can improve our understanding of their mechanical behavior while saving costs and time. [...] Read more.
The growing demand for sustainable materials has significantly increased interest in biocomposites, which are made from renewable raw materials and have excellent mechanical properties. The use of machine learning (ML) can improve our understanding of their mechanical behavior while saving costs and time. In this study, the mechanical behavior of innovative biocomposite sandwich structures under quasi-static out-of-plane compression was investigated using ML algorithms to analyze the effects of geometric variations on load-bearing capacities. A comprehensive dataset of experimental mechanical tests focusing on compression loading was employed, evaluating three ML models—generalized regression neural networks (GRNN), extreme learning machine (ELM), and support vector regression (SVR). Performance indicators such as R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the models. It was shown that the GRNN model with an RMSE of 0.0301, an MAE of 0.0177, and R2 of 0.9999 in the training dataset, and an RMSE of 0.0874, MAE of 0.0489, and R2 of 0.9993 in the testing set had a higher predictive accuracy. In contrast, the ELM model showed moderate performance, while the SVR model had the lowest accuracy with RMSE, MAE, and R2 values of 0.5769, 0.3782, and 0.9700 for training, and RMSE, MAE, and R2 values of 0.5980, 0.3976 and 0.9695 for testing, suggesting that it has limited effectiveness in predicting the mechanical behavior of the biocomposite structures. The nonlinear load-displacement behavior, including critical peaks and fluctuations, was effectively captured by the GRNN model for both the training and test datasets. The progressive improvement in model performance from SVR to ELM to GRNN was illustrated, highlighting the increasing complexity and capability of machine learning models in capturing detailed nonlinear relationships. The superior performance and generalization ability of the GRNN model were confirmed by the Taylor diagram and Williams plot, with the majority of testing samples falling within the applicability domain, indicating strong generalization to new, unseen data. The results demonstrate the potential of using advanced ML models to accurately predict the mechanical behavior of biocomposites, enabling more efficient and cost-effective development and optimization processes in the field of sustainable materials. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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17 pages, 6778 KiB  
Article
Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks
by Christian Witzgall, Moh’d Sami Ashhab and Sandro Wartzack
Materials 2024, 17(3), 729; https://doi.org/10.3390/ma17030729 - 3 Feb 2024
Cited by 1 | Viewed by 1185
Abstract
Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different [...] Read more.
Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different fibre orientations is to be taken into account. It is therefore important to gain the greatest possible amount of knowledge from the limited number of available tests. In order to achieve this, this study aims to utilise adaptive sampling, which is used in numerous areas of computational engineering, for the design of experiments on fatigue life testing. Artificial neural networks (ANNs) are therefore trained on data for the short-fibre-reinforced material PBT GF30, and their areas of greatest model uncertainty are queried. This was undertaken with ANNs from various numbers of hidden layers, which were analysed for their performance. The ideal case turned out to be four hidden layers, for which a squared error as small as 1 × 10−3 was recorded. Locally resolved, the ANN was used to identify the region of greatest uncertainty for samples of vertical orientation and small numbers of cycles. With information such as this, additional data can be obtained in such uncertain regions in order to improve the model prediction—almost halving the recorded error to only 0.55 × 10−3. In this way, a model of comparable value can be found with less experimental effort, or a model of better quality can be set up with the same experimental effort. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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17 pages, 3801 KiB  
Article
A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels
by Julia Contreras-Fortes, M. Inmaculada Rodríguez-García, David L. Sales, Rocío Sánchez-Miranda, Juan F. Almagro and Ignacio Turias
Materials 2024, 17(1), 147; https://doi.org/10.3390/ma17010147 - 27 Dec 2023
Cited by 2 | Viewed by 1413
Abstract
Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one [...] Read more.
Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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24 pages, 2092 KiB  
Article
Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach
by Faiza Qayyum, Murad Ali Khan, Do-Hyeun Kim, Hyunseok Ko  and Ga-Ae Ryu
Materials 2023, 16(23), 7322; https://doi.org/10.3390/ma16237322 - 24 Nov 2023
Cited by 9 | Viewed by 1849
Abstract
The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as “black boxes”, which poses [...] Read more.
The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as “black boxes”, which poses a difficulty in emphasizing the significance of producing lucid and readily understandable model outputs. In addition, the assessment of model performance requires careful deliberation of several essential factors. The objective of this study is to utilize a deep learning framework called TabNet to predict lead zirconate titanate (PZT) ceramics’ dielectric constant property by employing their components and processes. By recognizing the crucial importance of predicting PZT properties, this research seeks to enhance the comprehension of the results generated by the model and gain insights into the association between the model and predictor variables using various input parameters. To achieve this, we undertake a thorough analysis with Shapley additive explanations (SHAP). In order to enhance the reliability of the prediction model, a variety of cross-validation procedures are utilized. The study demonstrates that the TabNet model significantly outperforms traditional machine learning models in predicting ceramic characteristics of PZT components, achieving a mean squared error (MSE) of 0.047 and a mean absolute error (MAE) of 0.042. Key contributing factors, such as d33, tangent loss, and chemical formula, are identified using SHAP plots, highlighting their importance in predictive analysis. Interestingly, process time is less effective in predicting the dielectric constant. This research holds considerable potential for advancing materials discovery and predictive systems in PZT ceramics, offering deep insights into the roles of various parameters. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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18 pages, 6897 KiB  
Article
Estimation of Cyclic Stress–Strain Curves of Steels Based on Monotonic Properties Using Artificial Neural Networks
by Tea Marohnić, Robert Basan and Ela Marković
Materials 2023, 16(14), 5010; https://doi.org/10.3390/ma16145010 - 15 Jul 2023
Viewed by 1790
Abstract
This paper introduces a novel method for estimating the cyclic stress–strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant [...] Read more.
This paper introduces a novel method for estimating the cyclic stress–strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant literature, and divided into subgroups according to alloying elements content (unalloyed, low-alloy, and high-alloy steels). Only monotonic properties that were proven to be relevant for the estimation of points on the stress–strain curve were used. The performance of the developed ANNs was assessed using an independent set of data, and the results were compared to experimental values, values obtained by existing empirical estimation methods, and by previously developed ANNs. The results showed that the new approach which combines relevant monotonic properties and plastic strain amplitudes as inputs to ANNs for cyclic stress–strain curve estimation is better than the previously used approach where ANNs estimate the parameters of the Ramberg–Osgood material model separately. This shows that a more favorable approach to the estimation of cyclic stress–strain behavior would be to directly estimate corresponding material curves using monotonic properties. Additionally, this may also reduce inaccuracies resulting from simplified representations of the actual material behavior inherent in the material model. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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15 pages, 1654 KiB  
Article
Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography
by Mihai Iovea, Andrei Stanciulescu, Edward Hermann, Marian Neagu and Octavian G. Duliu
Materials 2023, 16(4), 1654; https://doi.org/10.3390/ma16041654 - 16 Feb 2023
Viewed by 1575
Abstract
In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage [...] Read more.
In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20–160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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16 pages, 4374 KiB  
Article
Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks
by Waqas Qayyum, Rana Ehtisham, Alireza Bahrami, Charles Camp, Junaid Mir and Afaq Ahmad
Materials 2023, 16(2), 826; https://doi.org/10.3390/ma16020826 - 14 Jan 2023
Cited by 25 | Viewed by 4405
Abstract
Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a [...] Read more.
Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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15 pages, 1520 KiB  
Article
Determination of TTT Diagrams of Ni-Al Binary Using Neural Networks
by Leonardo Hernández-Flores, Angel-Iván García-Moreno, Enrique Martínez-Franco, Guillermo Ronquillo-Lomelí and Jhon Alexander Villada-Villalobos
Materials 2022, 15(24), 8767; https://doi.org/10.3390/ma15248767 - 8 Dec 2022
Cited by 1 | Viewed by 2287
Abstract
The heat treatment of a metal is a set of heating and cooling cycles that a metal undergoes to change its microstructure and, therefore, its properties. Temperature–time–transformation (TTT) diagrams are an essential tool for interpreting the resulting microstructures after heat treatments. The present [...] Read more.
The heat treatment of a metal is a set of heating and cooling cycles that a metal undergoes to change its microstructure and, therefore, its properties. Temperature–time–transformation (TTT) diagrams are an essential tool for interpreting the resulting microstructures after heat treatments. The present work describes a novel proposal to predict TTT diagrams of the γ phase for the Ni-Al alloy using artificial neural networks (ANNs). The proposed methodology is composed of five stages: (1) database creation, (2) experimental design, (3) ANNs training, (4) ANNs validation, and (5) proposed models analysis. Two approaches were addressed, the first to predict only the nose point of the TTT diagrams and the second to predict the complete curve. Finally, the best models for each approach were merged to compose a more accurate hybrid model. The results show that the multilayer perceptron architecture is the most efficient and accurate compared to the simulated TTT diagrams. The prediction of the nose point and the complete curve showed an accuracy of 98.07% and 86.41%, respectively. The proposed final hybrid model achieves an accuracy of 96.59%. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)
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