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Artificial Intelligence in Materials Science and Engineering

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 5485

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Special Issue Editors

Prof. Dr. Piotr Lacki

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Department of Civil Engineering, Faculty of Civil Engineering, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: materials sciences; metal forming; refill friction stir spot welding; numerical simulation; civil engineering; composite beams; genetic algorithms; neural networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Janina Adamus

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Department of Civil Engineering, Faculty of Civil Engineering, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: metal forming; tribology; heat transfer through building partitions; artificial intelligence in technical solutions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Anna Derlatka

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Department of Civil Engineering, Faculty of Civil Engineering, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: artificial neural network; thin-walled structures; composite beams; steel-concrete composite structures; refill friction stir spot welding; numerical simulation

Prof. Dr. Wojciech Więckowski

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Department of Technology and Automation, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: metal forming; sheet metal stamping; tribology; bioengineering; biomaterials; numerical simulation; friction stir welding; artificial neural network

Prof. Dr. Krzysztof Cpałka

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Institute of Computational Intelligence, Czestochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, Poland
Interests: artificial intelligence; fuzzy systems; population-based algorithms; neural networks; interpretability

Special Issue Information

Dear Colleagues,

The combination of Artificial Intelligence (AI) and Materials Science and Engineering gives rise to innovative approaches that accelerate the discovery, development and optimization of materials and technologies with improved properties. This constructive interaction holds immense promise for revolutionizing industries ranging from civil engineering to metal forming, ushering in a new era of material innovation.

AI plays a crucial role in predictive modeling, enabling researchers to simulate and understand the behavior of materials under various conditions. Machine learning algorithms analyze complex datasets to predict material responses to different external factors, such as temperature, pressure, or chemical exposure. This capability enhances our ability to design materials with tailored properties for specific applications. As we delve deeper into this interdisciplinary collaboration, the synergies between AI and Materials Science are expected to yield breakthroughs with far-reaching implications for diverse industries and technological advancements.

The objective of this Special Issue is to establish a knowledge platform that encourages researchers and engineers to advance research in the field of Materials Science and Engineering, employing the diverse applications of artificial intelligence.

This Special Issue invites the submission of manuscripts that explore the utilization of AI in Materials Science and Engineering, particularly concerning through classical and state-of-the-art manufacturing techniques. We encourage the submission of full papers on this subject.

Prof. Dr. Piotr Lacki
Prof. Dr. Janina Adamus
Prof. Dr. Anna Derlatka
Prof. Dr. Wojciech Więckowski
Prof. Dr. Krzysztof Cpałka
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

artificial intelligence
artificial neural network
fuzzy system
population-based algorithm
genetic algorithms
bioengineering
metal forming
civil engineering
composite structures
friction stir welding
numerical simulation

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Research

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20 pages, 1981 KiB

Open AccessArticle

Analysis of Thermal Properties of Materials Used to Insulate External Walls

by Marta Pomada, Klaudia Kieruzel, Adam Ujma, Paweł Palutkiewicz, Tomasz Walasek and Janina Adamus

Materials 2024, 17(19), 4718; https://doi.org/10.3390/ma17194718 - 26 Sep 2024

Abstract

This article emphasizes the significance of understanding the actual thermal properties of thermal insulation materials, which are crucial for avoiding errors in building design and estimating heat losses within the energy balance. The aim of this study was to analyse the thermal parameters of selected thermal insulation materials, particularly in the context of their stability after a period of storage under specific conditions. The materials chosen for this study include commonly used construction insulations such as polystyrene and mineral wool, as well as modern options like rigid foam composites. Experimental studies were conducted, including the determination of the thermal conductivity coefficient λ, as well as numerical analyses and analytical calculations of heat flow through a double-layer external wall with a window. The numerical analyses were performed using the TRISCO software version 12.0w, based on the finite element method (FEM). A macrostructural analysis of the investigated materials was also performed. The findings indicated that improper storage conditions adversely affect the thermal properties of insulation materials. Specifically, storing materials outdoors led to a deterioration in insulating properties, with an average reduction of about 4% for the standard materials and as much as 19% for the tested composite material. Insufficient understanding of the true thermal properties of insulation materials can result in incorrect insulation layer thickness, degrading the fundamental thermal parameters of external walls. This, in turn, increases heat loss through major building surfaces, raises heating costs, and indirectly contributes to greenhouse gas emissions. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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12 pages, 2490 KiB

Open AccessArticle

Impact of Scattering Foil Composition on Electron Energy Distribution in a Clinical Linear Accelerator Modified for FLASH Radiotherapy: A Monte Carlo Study

by James C. L. Chow and Harry E. Ruda

Materials 2024, 17(13), 3355; https://doi.org/10.3390/ma17133355 - 7 Jul 2024

Viewed by 660

Abstract

This study investigates how scattering foil materials and sampling holder placement affect electron energy distribution in electron beams from a modified medical linear accelerator for FLASH radiotherapy. We analyze electron energy spectra at various positions—ionization chamber, mirror, and jaw—to evaluate the impact of Cu, Pb-Cu, Pb, and Ta foils. Our findings show that close proximity to the source intensifies the dependence of electron energy distribution on foil material, enabling precise beam control through material selection. Monte Carlo simulations are effective for designing foils to achieve desired energy distributions. Moving the sampling holder farther from the source reduces foil material influence, promoting more uniform energy spreads, particularly in the 0.5–10 MeV range for 12 MeV electron beams. These insights emphasize the critical role of tailored material selection and sampling holder positioning in optimizing electron energy distribution and fluence intensity for FLASH radiotherapy research, benefiting both experimental design and clinical applications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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15 pages, 6097 KiB

Open AccessArticle

Crack Initiation in Compacted Graphite Iron with Random Microstructure: Effect of Volume Fraction and Distribution of Particles

by Xingling Luo, Konstantinos P. Baxevanakis and Vadim V. Silberschmidt

Materials 2024, 17(13), 3346; https://doi.org/10.3390/ma17133346 - 6 Jul 2024

Viewed by 637

Abstract

Thanks to the distinctive morphology of graphite particles in its microstructure, compacted graphite iron (CGI) exhibits excellent thermal conductivity together with high strength and durability. CGI is extensively used in many applications, e.g., engine cylinder heads and brakes. The structural integrity of such metal-matrix materials is controlled by the generation and growth of microcracks. Although the effects of the volume fraction and morphology of graphite inclusions on the tensile response of CGI were investigated in recent years, their influence on crack initiation is still unknown. Experimental studies of crack initiation require a considerable amount of time and resources due to the highly complicated geometries of graphite inclusions scattered throughout the metallic matrix. Therefore, developing a 2D computational framework for CGI with a random microstructure capable of predicting the crack initiation and path is desirable. In this work, an integrated numerical model is developed for the analysis of the effects of volume fraction and nodularity on the mechanical properties of CGI as well as its damage and failure behaviours. Finite-element models of random microstructure are generated using an in-house Python script. The determination of spacings between a graphite inclusion and its four adjacent particles is performed with a plugin, written in Java and implemented in ImageJ. To analyse the orientation effect of inclusions, a statistical analysis is implemented for representative elements in this research. Further, Johnson–Cook damage criteria are used to predict crack initiation in the developed models. The numerical simulations are validated with conventional tensile-test data. The created models can support the understanding of the fracture behaviour of CGI under mechanical load, and the proposed approach can be utilised to design metal-matrix composites with optimised mechanical properties and performance. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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15 pages, 4736 KiB

Open AccessArticle

A Finite Element Model for Simulating Stress Responses of Permeable Road Pavement

by Jhu-Han Siao, Tung-Chiung Chang and Yu-Min Wang

Materials 2024, 17(12), 3012; https://doi.org/10.3390/ma17123012 - 19 Jun 2024

Viewed by 601

Abstract

Permeable road pavements, due to their open-graded design, suffer from low structural strength, restricting their use in areas with light traffic volume and low bearing capacity. To expand application of permeable road pavements, accurate simulation of stress parameters used in pavement design is essential. A 3D finite element (3D FE) model was developed using ABAQUS/CAE 2021 to simulate pavement stress responses. Utilizing a 53 cm thick permeable road pavement and a 315/80 R22.5 wheel as prototypes, the model was calibrated and validated, with its accuracy confirmed through t-test statistical analysis. Simulations of wheel speeds at 11, 15, and 22 m/s revealed significant impact on pavement depths of 3 cm and 8 cm, while minimal effects were observed at depths of 13 cm and 33 cm. Notably, stress values at a depth of 3 cm with 15 m/s speed in the open-graded asphalt concrete (OGFC) surface layer exceeded those at the speed of 11 m/s, while at a depth of 8 cm in the porous asphalt concrete (PAC) base layer, an opposite performance was observed. This may be attributed to the higher elastic modulus of the OGFC surface layer, which results in different response trends to velocity changes. Overall, lower speeds increase stress responses and prolong action times for both layers, negatively affecting pavement performance. Increasing the moduli of layers is recommended for new permeable road pavements for low-speed traffic. Furthermore, considering the effects of heavy loads and changes in wheel speed, the recommended design depth for permeable road pavement is 30 cm. These conclusions provide a reference for the design of permeable road pavements to address climate change and improve performance. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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30 pages, 22061 KiB

Open AccessArticle

Durability Analysis of Cold Spray Repairs: Phase I—Effect of Surface Grit Blasting

by Daren Peng, Caixian Tang, Jarrod Watts, Andrew Ang, R. K. Singh Raman, Michael Nicholas, Nam Phan and Rhys Jones

Materials 2024, 17(11), 2656; https://doi.org/10.3390/ma17112656 - 31 May 2024

Cited by 1 | Viewed by 464

Abstract

This paper presents the results of an extensive investigation into the durability of cold spray repairs to corrosion damage in AA7075-T7351 aluminium alloy specimens where, prior to powder deposition, the surface preparation involved grit blasting. In this context, it is shown that the growth of small naturally occurring cracks in cold spray repairs to simulated corrosion damage can be accurately computed using the Hartman–Schijve crack growth equation in a fashion that is consistent with the requirements delineated in USAF Structures Bulletin EZ-SB-19-01, MIL-STD-1530D, and the US Joint Services Structural Guidelines JSSG2006. The relatively large variation in the da/dN versus ΔK curves associated with low values of da/dN highlights the fact that, before any durability assessment of a cold spray repair to an operational airframe is attempted, it is first necessary to perform a sufficient number of tests so that the worst-case small crack growth curve needed to perform the mandated airworthiness certification analysis can be determined. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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16 pages, 2367 KiB

Open AccessArticle

Numerical Simulation of Lost-Foam Casting for Key Components of A356 Aluminum Alloy in New Energy Vehicles

by Chi Sun, Zhanyi Cao, Yanzhu Jin, Hongyu Cui, Chenggang Wang, Feng Qiu and Shili Shu

Materials 2024, 17(10), 2363; https://doi.org/10.3390/ma17102363 - 15 May 2024

Viewed by 680

Abstract

The intricate geometry and thin walls of the motor housing in new energy vehicles render it susceptible to casting defects during conventional casting processes. However, the lost-foam casting process holds a unique advantage in eliminating casting defects and ensuring the strength and air-tightness of thin-walled castings. In this paper, the lost-foam casting process of thin-walled A356 alloy motor housing was simulated using ProCAST software (2016.0). The results indicate that the filling process is stable and exhibits characteristics of diffusive filling. Solidification occurs gradually from thin to thick. Defect positions are accurately predicted. Through analysis of the defect volume range, the optimal process parameter combination is determined to be a pouring temperature of 700 °C, an interfacial heat transfer coefficient of 50, and a sand thermal conductivity coefficient of 0.5. Microscopic analysis of the motor housing fabricated using the process optimized through numerical simulations reveals the absence of defects such as shrinkage at critical locations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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21 pages, 15097 KiB

Open AccessArticle

The Potential of Multi-Task Learning in CFDST Design: Load-Bearing Capacity Design with Three MTL Models

by Zhenyu Wang, Jian Zhou and Kang Peng

Materials 2024, 17(9), 1994; https://doi.org/10.3390/ma17091994 - 25 Apr 2024

Cited by 2 | Viewed by 554

Abstract

Concrete-filled double steel tubes (CFDSTs) are a load-bearing structure of composite materials. By combining concrete and steel pipes in a nested structure, the performance of the column will be greatly improved. The performance of CFDSTs is closely related to their design. However, existing codes for CFDST design often focus on how to verify the reliability of a design, but specific design parameters cannot be directly provided. As a machine learning technique that can simultaneously learn multiple related tasks, multi-task learning (MTL) has great potential in the structural design of CFDSTs. Based on 227 uniaxial compression cases of CFDSTs collected from the literature, this paper utilized three multi-task models (multi-task Lasso, VSTG, and MLS-SVR) separately to provide multiple parameters for CFDST design. To evaluate the accuracy of models, four statistical indicators were adopted (R², RMSE, RRMSE, and ρ). The experimental results indicated that there was a non-linear relationship among the parameters of CFDSTs. Nevertheless, MLS-SVR was still able to provide an accurate set of design parameters. The coefficient matrices of two linear models, multi-task Lasso and VSTG, revealed the potential connection among CFDST parameters. The latent-task matrix V in VSTG divided the prediction tasks of inner tube diameter, thickness, strength, and concrete strength into three groups. In addition, the limitations of this study and future work are also summarized. This paper provides new ideas for the design of CFDSTs and the study of related codes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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Review

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21 pages, 1925 KiB

Open AccessReview

Machine Learning for Additive Manufacturing of Functionally Graded Materials

by Mohammad Karimzadeh, Deekshith Basvoju, Aleksandar Vakanski, Indrajit Charit, Fei Xu and Xinchang Zhang

Materials 2024, 17(15), 3673; https://doi.org/10.3390/ma17153673 - 25 Jul 2024

Cited by 2 | Viewed by 524

Abstract

Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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