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Advances in Computation and Modeling of Materials Mechanics
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Advances in Computation and Modeling of Materials Mechanics

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 1833

Special Issue Editors

Dr. Hai Huang

E-Mail Website
Guest Editor
School of Physics, Zhengzhou University, Zhengzhou 450052, China
Interests: multiscale simulations; nanocomposites; radiation tolerance; mechanical behavior; radiation shielding
Special Issues, Collections and Topics in MDPI journals
Dr. Abduljabar Alsayoud

E-Mail Website
Guest Editor
Mechanical Engineering Department, College of Engineering and Physics, King Fahd University of Petroleum & Minerals, Daharan 34464, Saudi Arabia
Interests: modeling and atomistic simulations of materials using both classical molecular dynamics and density functional theory
Prof. Dr. Yucheng Lan

E-Mail Website
Guest Editor
Department of Physics and Engineering Physics, Morgan State University, Baltimore, MD 21251, USA
Interests: renewable energy; photo-/electro-catalysts; nanosensing; mechanical response; electron microscopy; nanomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue “Advances in Computation and Modeling of Materials Mechanics” aims to explore the forefront of research in the field of advanced material mechanics using theoretical computation and simulation techniques. This Special Issue focuses on investigating the mechanical behavior and properties of advanced materials at different scales, which have significant implications for various industries and applications, including aerospace, nuclear, automotive, and structural engineering. Therefore, the topics covers a wide range of research areas, including but not limited to the following: (1) Development and application of computational models and simulation methods for analyzing the mechanical properties of advanced materials; (2) Investigation of the mechanical response of advanced materials under different loading conditions, such as tensile, compressive, and shear forces; (3) Exploration of the relationships between the microstructure and mechanical properties of advanced materials; (4) Study of the effects of various factors, such as grain boundaries, defects, and interfaces, on the mechanical behavior of advanced materials; (5) Advancements in computational techniques for modeling and simulating the mechanical phenomena at different scales. The research published in this Special Issue will contribute to a deeper understanding of the mechanical behavior of advanced materials, facilitate the design and development of advanced materials with tailored properties, and foster interdisciplinary collaborations among researchers in the areas of materials science, nanotechnology, and computational mechanics. Original research papers, state-of-the-art reviews, communications, and discussions are welcomed.

Dr. Hai Huang
Dr. Abduljabar Alsayoud
Prof. Dr. Yucheng Lan
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 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. Materials 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 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

  • advanced alloys
  • nanocomposites and nanomaterials
  • nanostructured structure-property correlations
  • mechanical properties
  • microstructure evolution
  • modeling and simulations
  • machine learning in materials science

Published Papers (3 papers)

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Research

20 pages, 7907 KiB  
Article
The Integration of ANN and FEA and Its Application to Property Prediction of Dual-Performance Turbine Disks
by Yanqing Li, Ziming Zhang, Junyi Cheng, Zhaofeng Liu, Chao Yin, Chao Wang and Jianzheng Guo
Materials 2024, 17(13), 3045; https://doi.org/10.3390/ma17133045 - 21 Jun 2024
Viewed by 327
Abstract
Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present [...] Read more.
Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present substantial challenges. The prediction of yield strength is typically derived from the analysis of microstructures under various heat treatment regimes. This method is time-consuming, expensive, and the accuracy often depends on the precision of microstructural characterization. This study successfully employed a coupled method of Artificial Neural Network (ANN) and finite element analysis (FEA) to reveal the relationship between the heat treatment process and yield strength. The coupled method accurately predicted the location specified and temperature-dependent yield strength based on the heat treatment parameters such as holding temperatures and cooling rates. The root mean square error (RMSE) and mean absolute percentage deviation (MAPD) for the training set are 50.37 and 3.77, respectively, while, for the testing set, they are 50.13 and 3.71, respectively. Furthermore, an integrated model of FEA and ANN is established using a Abaqus user subroutine. The integrated model can predict the yield strength based on temperature calculation results and automatically update material properties of the FEA model during the loading process simulation. This allows for an accurate calculation of the stress–strain state of the turbine disk during actual working conditions, aiding in locating areas of stress concentration, plastic deformation, and other critical regions, and provides a novel reliable reference for the rapid design of the turbine disk. Full article
(This article belongs to the Special Issue Advances in Computation and Modeling of Materials Mechanics)
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26 pages, 15125 KiB  
Article
Fatigue Life Data Fusion Method of Different Stress Ratios Based on Strain Energy Density
by Changyin Wang, Jianyao Yao, Xu Zhang, Yulin Wu, Xuyang Liu, Hao Liu, Yiheng Wei and Jianqiang Xin
Materials 2024, 17(12), 2982; https://doi.org/10.3390/ma17122982 - 18 Jun 2024
Viewed by 467
Abstract
To accurately evaluate the probabilistic characteristics of the fatigue properties of materials with small sample data under different stress ratios, a data fusion method for torsional fatigue life under different stress ratios is proposed based on the energy method. A finite element numerical [...] Read more.
To accurately evaluate the probabilistic characteristics of the fatigue properties of materials with small sample data under different stress ratios, a data fusion method for torsional fatigue life under different stress ratios is proposed based on the energy method. A finite element numerical modeling method is used to calculate the fatigue strain energy density during fatigue damage. Torsional fatigue tests under different stresses and stress ratios are carried out to obtain a database for research. Based on the test data, the Wt-Nf curves under a single stress ratio and different stress ratios are calculated. The reliability of the models is illustrated by the scatter band diagram. More than 85% of points are within ±2 scatter bands, indicating that the fatigue life under different stress ratios can be represented by the same Wt-Nf curve. Furthermore, P-Wt-Nf prediction models are established to consider the probability characteristics. According to the homogeneity of the Wt-Nf model under different stress ratios, we can fuse the fatigue life data under different stress ratios and different strain energy densities. This data fusion method can expand the small sample test data and reduce the dispersion of the test data between different stress ratios. Compared with the pre-fusion data, the standard deviations of the post-fusion data are reduced by a maximum of 21.5% for the smooth specimens and 38.5% for the notched specimens. And more accurate P-Wt-Nf curves can be obtained to respond to the probabilistic properties of the data. Full article
(This article belongs to the Special Issue Advances in Computation and Modeling of Materials Mechanics)
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16 pages, 16176 KiB  
Article
Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions
by Kehao Qiang, Shisong Wang, Haowen Wang, Zhulin Zeng and Liangzhao Qi
Materials 2024, 17(3), 619; https://doi.org/10.3390/ma17030619 - 27 Jan 2024
Viewed by 625
Abstract
The hot deformation behavior of titanium matrix composites plays a crucial role in determining the performance of the formed components. Therefore, it is significant to establish an accurate constitutive relationship between material deformation parameters and flow stress. In this study, hot compression experiments [...] Read more.
The hot deformation behavior of titanium matrix composites plays a crucial role in determining the performance of the formed components. Therefore, it is significant to establish an accurate constitutive relationship between material deformation parameters and flow stress. In this study, hot compression experiments were conducted on a (2.5 vol%TiB + 2.5 vol%TiC)/TC4. The experiments were performed under temperatures ranging from 1013.15 to 1133.15 K and strain rates ranging from 0.001 to 0.1 s−1. Based on the stress–strain data obtained from the experiment, the constitutive models were established by using the Arrhenius model and the BP neural network algorithm, respectively. Considering the relationship between strain rate, hot working temperature, and flow stress, a comparative analysis was conducted to evaluate the prediction accuracy of two different constitutive models. The research results indicate that the flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 increases with decreasing temperature and increasing strain rate, and the stress–strain curve shows obvious work hardening and softening behaviors. Both the Arrhenius model and the BP neural network algorithm are effective in predicting the hot compression flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4, but the average relative error and root mean square error of the BP neural network algorithm are smaller and the correlation coefficient is higher, thus possessing higher accuracy and reliability. Full article
(This article belongs to the Special Issue Advances in Computation and Modeling of Materials Mechanics)
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