Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 9825

Share This Special Issue

Special Issue Editor

Dr. Avik Mahata

E-Mail Website
Guest Editor

School of Engineering, Brown University, Providence, RI 02912, USA
Interests: machine learning; neural networks; molecular dynamics; first principle calculation; phase field; finite element; materials informatics

Special Issue Information

Dear Colleagues,

Computational materials science is continuously evolving and adapting new technologies to predict and reproduce experimental data, both qualitatively and quantitatively. The integration of machine learning has made the process of computation more efficient and flexible. Machine-learning-based models also require the handling of large datasets. Therefore, research in the emerging technologies can be divided into two categories. First, the developed methods can be implemented in various systems, and can be compared with available experimental data. The second way to make progress in this research area is to improve the present methodologies, both from theoretical and computational perspectives.

The aim of this Special Issue is to investigate the latest research trends and recent development of the computational methods in materials science and engineering.

Dr. Avik Mahata
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (6 papers)

Download All Papers

Research

29 pages, 10344 KiB

Open AccessArticle

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl₂O₃ Composites Using Machine Learning and Response Surface Approaches

by Sally Elkatatny, Mohammed F. Alsharekh, Abdulrahman I. Alateyah, Samar El-Sanabary, Ahmed Nassef, Mokhtar Kamel, Majed O. Alawad, Amal BaQais, Waleed H. El-Garaihy and Hanan Kouta

Appl. Sci. 2023, 13(13), 7483; https://doi.org/10.3390/app13137483 - 25 Jun 2023

Cited by 3 | Viewed by 981

Abstract

This study comprehensively investigates the impact of various parameters on aluminum matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu matrix composite (2xxx series) was employed in the current study, and Al₂O₃ was used as a reinforcement. The performance evaluation of the Al-4Cu/Al₂O₃ composite involved analyzing the influence of the Al₂O₃ weight percent (wt. %), the height-to-diameter ratio (H/D) of the compacted samples, and the compaction pressure. Different concentrations of the Al₂O₃ reinforcement, namely 0%, 2.5%, 5.0%, 7.5%, and 10% by weight, were utilized, while the compaction process was conducted for one hour under varying pressures of 500, 600, 700, 800, and 900 MPa. The compacted Al-4Cu/Al₂O₃ composites were in the form of cylindrical discs with a fixed diameter of 20 mm and varying H/D ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. Moreover, the machine learning (ML), design of experiment (DOE), response surface methodology (RSM), genetic algorithm (GA), and hybrid DOE-GA methodologies were utilized to thoroughly investigate the physical properties, such as the relative density (RD), as well as the mechanical properties, including the hardness distribution, fracture strain, yield strength, and compression strength. Subsequently, different statistical analysis approaches, including analysis of variance (ANOVA), 3D response surface plots, and ML approaches, were employed to predict the output responses and optimize the input variables. The optimal combination of variables that demonstrated significant improvements in the RD, fracture strain, hardness distribution, yield strength, and compression strength of the Al-4Cu/Al₂O₃ composite was determined using the RSM, GA, and hybrid DOE-GA approaches. Furthermore, the ML and RSM models were validated, and their accuracy was evaluated and compared, revealing close agreement with the experimental results. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Figure 1

12 pages, 5140 KiB

Open AccessArticle

Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning

by Xuze Zhang, Fang Liu, Min Shen, Donggui Han, Zilong Wang and Nu Yan

Appl. Sci. 2023, 13(4), 2524; https://doi.org/10.3390/app13042524 - 15 Feb 2023

Cited by 2 | Viewed by 1379

Abstract

The fatigue life evaluation of metallic materials plays an important role in ensuring the safety and long service life of metal structures. To further improve the accuracy and efficiency of the ultra-high-cycle fatigue life prediction of metallic materials, a new prediction method using machine learning was proposed. The training database contained the ultra-high-cycle fatigue life of different metallic materials obtained from fatigue tests, and two fatigue life prediction models were constructed based on the gradient boosting (GB) and random forest (RF) algorithms. The mean square error and the coefficient of determination were applied to evaluate the performance of the two models, and their advantages and application scenarios were also discussed. The ultra-high-cycle fatigue life of GCr15 bearing steel was predicted by the constructed models. It was found that only one datapoint of the GB model exceeded the triple error band, and the RF model had higher stability. The network model coefficient of determination and mean square error for the GB and RF models were 0.78, 0.79 and 0.69, 3.79, respectively. Both models could predict the ultra-high-cycle fatigue life of metallic materials quickly and effectively. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Figure 1

12 pages, 3563 KiB

Open AccessArticle

GNSS Antenna Pattern Prediction and Placement Optimization: A Prototype Method Using Machine Learning to Aid Complex Electromagnetic Simulations Validated on a Vehicle Model

by Franciele Cicconet, Rui Silva and Paulo M. Mendes

Appl. Sci. 2023, 13(4), 2197; https://doi.org/10.3390/app13042197 - 08 Feb 2023

Viewed by 1185

Abstract

An antenna’s radiation pattern is dependent on its geometrical characteristics and its antenna’s surroundings, materials, and geometries. As such, to predict the antenna’s performance in complex environments, such as that of small antennas on large vehicles, it is necessary to obtain a model that represents such a full scenario, so that the simulation may be accomplished in the process of antenna design and placement. Due to the complex and electrically large nature of some electromagnetic problems, the detailed representation, even for a simplified model, may imply a large computational effort, both in terms of time and memory, is needed to perform the simulation. This paper evaluates how machine learning models can be used to mitigate the computational effort required to predict the behavior of antennas requiring complex modeling. It is proposed to start from a more simplified model of the electromagnetic structure to obtain a prediction for the correct solution, without needing to simulate the full structure in every iteration, and to combine this with prediction algorithms to obtain the solution of the full problem. The proposed solution uses convolutional neural networks (U-Net) of a certain accuracy to help with the correct placement of small antennas on autonomous vehicles. The standard approach requires the simulation of a full model at each test position, requiring high computational time and memory. With this new proposal, it is possible to analyze more positions and radiation patterns in a much shorter time, and with less memory, when compared with the solution from the full model. Along with this methodology for each simulation, a Bayesian optimizer is proposed to improve the search process for the best location, leading to a reduction in the required steps. This methodology was applied to support the correct positioning of a GNSS antenna with reference to a set of performance indicators required for autonomous vehicles, but it can be also applied to larger and more complex structures, allowing one to reduce the simulation time of a large electromagnetic structure and the search time for the optimum location. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Figure 1

24 pages, 13784 KiB

Open AccessArticle

Implicit to Explicit Algorithm for ABAQUS Standard User-Subroutine UMAT for a 3D Hashin-Based Orthotropic Damage Model

by M. R. T. Arruda, M. Trombini and A. Pagani

Appl. Sci. 2023, 13(2), 1155; https://doi.org/10.3390/app13021155 - 15 Jan 2023

Cited by 8 | Viewed by 3072

Abstract

This study examines a new approach to facilitate the convergence of upcoming user-subroutines UMAT when the secant material matrix is applied rather than the conventional tangent (also known as Jacobian) material matrix. This algorithm makes use of the viscous regularization technique to stabilize the numerical solution of softening material models. The Newton–Raphson algorithm predictor-corrector of ABAQUS then applies this type of viscous regularization to a UMAT using only the secant matrix. When the time step is smaller than the viscosity parameter, this type of regularization may be unsuitable for a predictor-corrector with the secant matrix because its implicit convergence is incorrect, transforming the algorithm into an undesirable explicit version that may cause convergence problems. A novel 3D orthotropic damage model with residual stresses is proposed for this study, and it is analyzed using a new algorithm. The method’s convergence is tested using the proposed implicit-to-explicit secant matrix as well as the traditional implicit and explicit secant matrices. Furthermore, all numerical models are compared to experimental data. It was concluded that both the new 3D orthotropic damage model and the new proposed time step algorithm were stable and robust. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Figure 1

13 pages, 3142 KiB

Open AccessArticle

Effect of Compression Tights on Skin Temperature in Women with Lipedema

by Jose Luis Sanchez-Jimenez, Jose Ignacio Priego-Quesada, María José Gisbert-Ruiz, Rosa M. Cibrian-Ortiz de Anda, Pedro Perez-Soriano and Inmaculada Aparicio

Appl. Sci. 2023, 13(2), 1133; https://doi.org/10.3390/app13021133 - 14 Jan 2023

Cited by 1 | Viewed by 1523

Abstract

The aim was to analyze the effect of compression tights on skin temperature in women with lipedema and to assess the effect of different knitting on skin temperature. Twenty-four women with lipedema (Grade I = 25%; Grade II = 75%) were divided into three groups according to the compression tights prototype assigned: control (n = 9), Flat (n = 7) and circular (n = 8). The participants performed a gait test two times, separated by 15 days: before wearing the tights of the study and after the treatment (15 days employing compression tights). Skin temperature was measured using infrared thermography before and after the gait test on both days, and six regions of interest were determined in the anterior and posterior leg. The skin temperature decreased in the different regions of interest after exercise in all the groups (e.g., anterior thigh (IC95% (−1.1, −0.7 °C) p < 0.001), but no differences were observed in skin temperature between groups before and after walking (p > 0.05). The use of compressing tights for 15 days does not alter skin temperature in women with lipedema before and after walking. The absence of differences in skin temperature between tights in the different assessments allows for obtaining the benefits of wearing compression tights during exercise without negative thermal effects. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Graphical abstract

13 pages, 1479 KiB

Open AccessArticle

Modeling Liquid Thermal Conductivity of Low-GWP Refrigerants Using Neural Networks

by Mariano Pierantozzi, Sebastiano Tomassetti and Giovanni Di Nicola

Appl. Sci. 2023, 13(1), 260; https://doi.org/10.3390/app13010260 - 25 Dec 2022

Cited by 4 | Viewed by 1077

Abstract

The thermal conductivity of refrigerants is needed to optimize and design the main components of HVAC&R systems. Consequently, it is crucial to have reliable models that are able to accurately calculate the temperature and pressure dependence of the thermal conductivity of refrigerants. For the first time, this study presents a neural network specifically developed to calculate the liquid thermal conductivity of various low-GWP-based refrigerants. In detail, a feed-forward network algorithm with 5 input parameters (i.e., the reduced temperature, the critical pressure, the acentric factor, the molecular weight, and the reduced pressure) and 1 hidden layer was applied to a large dataset of 3404 experimental points for 7 halogenated alkene refrigerants. The results provided by the neural network algorithm were very satisfactory, achieving an absolute average relative deviation of 0.389% with a maximum absolute relative deviation of 6.074% over the entire dataset. In addition, the neural network ensured lower deviations between the experimental and calculated data than that produced using different literature models, proving its accuracy for the liquid thermal conductivity of the studied refrigerants. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science

Share This Special Issue

Special Issue Editor

Special Issue Information

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI