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Modeling and Optimization of Material Properties and Characteristics

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 991

Special Issue Editors


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Guest Editor
Engineering Academy of Serbia, 11000 Belgrade, Serbia
Interests: production engineering; machining; non-traditional machining processes; additive manufacturing (AM); 3D and 4D printing; cutting tools; minimum quantity lubrication (MQL); programming of CNC machine; reliability; statistical analysis; response surface methodology (RSM); artificial neuron network (ANN); design of experiments (DoE); bibliometric and citation analysis
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Guest Editor
Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy
Interests: friction modelling in dry and wet contacts; control of mechanical systems and mechatronics; testing methodologies of frictional materials in automotive and industrial environments
Special Issues, Collections and Topics in MDPI journals

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Special Issue Information

Dear Colleagues,

Materials influence every dimension of our culture and contemporary lifestyle (transportation, housing, communication, recreation, food production, etc.). Throughout history, the growth and progress of civilization have been closely connected to a society's ability to create and manage materials to meet its needs. In relatively recent times, scientists have begun to comprehend the connection between the structural components of materials and their properties. This understanding, acquired over the past century or so, has allowed them to discover novel materials and determine their attributes. Consequently, thousands of materials with highly specialized properties have been developed to satisfy the demands of our contemporary society, including various forms of metals, plastics, ceramics, glass, and fibers.

Additionally, the following groups of modern materials have had the greatest impact on the efficiency, reliability and cost of new products (elements, devices, and systems) in all areas of society: composite materials, nanomaterials, smart materials, advanced materials, etc.

This means that the main task of today's engineers is the correct selection of an appropriate material for a particular application.

This task is often difficult, and requires experimental research and the determination of a mathematical model that can ascertain the dependence of material property parameters and characteristics (such as: strength, toughness, hardness, hardenability, brittleness, malleability, ductility, fatigue, plasticity, creep, resilience, total thickness reduction, aging time, aging temperature, short annealing temperature, coefficient of friction, wear, etc.) on input factors, as well as the optimization of these parameters.

This Special Issue, entitled ”Modeling and Optimization of Material Properties and Characteristics”, is the perfect opportunity for researchers to present their research on the behavior, parameters, characteristics and application of various materials.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Theoretical and experimental studies on the material properties, characteristics, and behavior of a specific material across various applications and production conditions;
  • The mathematical modeling of the parameters of material properties and characteristics across various applications and the production conditions of a specific material using regression models, artificial neural networks (ANN), and other methods;
  • The application and implementation of the design of experiments (DoE) and the Taguchi method (TM) for the realization of experimental research that determines the parameters of material properties and characteristics using response surface methodology (RSM);
  • The optimization of the properties and characteristics of materials across various applications and production conditions using optimization algorithms and techniques;
  • The mathematical modeling and optimization of the parameters of different types of machining and additive manufacturing materials with various properties and characteristics using regression models, response surface methodology (RSM), artificial neural networks (ANN), and other methods.

Prof. Dr. Predrag Dašić
Prof. Dr. Adolfo Senatore
Prof. Dr. Alessandro Ruggiero
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

  • material properties
  • material characteristics
  • composite materials
  • nano-materials
  • smart materials
  • regression models
  • response surface methodology (RSM)
  • artificial neural networks (ANN)
  • optimization algorithms
  • multi‐objective optimization (MOO)

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Published Papers (3 papers)

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Research

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15 pages, 2034 KiB  
Article
Evaluation of the Effect of Using Different Types of Clinker Grinding Aids on Grinding Performance by Numerical Analysis
by Yahya Kaya, Veysel Kobya, Murat Eser, Naz Mardani, Metin Bilgin and Ali Mardani
Materials 2025, 18(12), 2712; https://doi.org/10.3390/ma18122712 (registering DOI) - 9 Jun 2025
Abstract
To develop more environmentally friendly and sustainable cementitious systems, the use of grinding aids (GAs) during the clinker grinding process has increasingly gained attention. Although the mechanisms of the action of grinding aids (GAs) are known, the selection of an effective grinding aid [...] Read more.
To develop more environmentally friendly and sustainable cementitious systems, the use of grinding aids (GAs) during the clinker grinding process has increasingly gained attention. Although the mechanisms of the action of grinding aids (GAs) are known, the selection of an effective grinding aid (GA) can be difficult due to the complexity of appropriate selection criteria. For this reason, it is important to model the effect of GA properties on grinding performance. In this study, seven different types of GAs were used in four different dosages, and time-dependent grinding was performed. The Blaine fineness values of cements were compared after each grinding process. In addition, the modeling of these parameters using machine learning and ensemble learning methods was discussed. The Synthetic Minority Over-sampling Technique (Smote) was used to generate artificial data and increase the number of data for the grinding efficiency experiment. The data were modeled using methods such as Artificial Neural Networks (ANNs), Attentive Interpretable Tabular Learning (TabNet), Random Forests (RFs), and the XGBoost Regressor (XGBoost), and the ranking of the parameters affecting the Blaine properties was determined using the XGBoost method. The XGBoost method achieved the best results in the MAE, RMSE, and LogCosh metrics with values of 21.0384, 33.7379, and 15.4846, respectively, in the experimental modeling studies with augmented data. This study contributes to a better understanding of the relationship between GA selection and milling process performance. Full article
(This article belongs to the Special Issue Modeling and Optimization of Material Properties and Characteristics)
22 pages, 22135 KiB  
Article
Analysis of the Damage and Failure Mechanism of Q345 Steel Plate with Initial Defect Under Different Temperature Conditions by Peridynamics
by Wudang Ying, Jinhai Zhao, Heipie Zhou, Yuchen Zhu, Yuquan Yang and Xinzan Hu
Materials 2025, 18(8), 1886; https://doi.org/10.3390/ma18081886 - 21 Apr 2025
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Abstract
The high temperature performance of steel structures has long been a focus of research, but research on the damage and crack propagation mechanism of steel with initial defects at high temperature is relatively low. The high temperature performance of most steel structures in [...] Read more.
The high temperature performance of steel structures has long been a focus of research, but research on the damage and crack propagation mechanism of steel with initial defects at high temperature is relatively low. The high temperature performance of most steel structures in engineering has an important impact on the function and safety of the whole structure. At present, Peridynamics (PD) theory uses the integral method that has unique advantages compared with traditional methods to solve structural damage and fracture problems. Therefore, the effect of temperature change on steel properties is introduced into the PD, and the PD constitutive equation at high temperature is proposed. The damage and crack propagation mechanisms of 2D Q345 steel plates with bilateral cracks and different bolt holes at 20 °C, 300 °C, 400 °C and 600 °C were analyzed by applying temperature action and external load to double-cracked steel specimens by the direct thermostructural coupling method. At the same time, the damage values, displacement changes in X direction and Y direction under different temperatures were compared and analyzed, and the effects of temperature, bolt hole and external load on the damage, displacement and crack growth path of different parts of the structure were obtained. Full article
(This article belongs to the Special Issue Modeling and Optimization of Material Properties and Characteristics)
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Review

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37 pages, 1157 KiB  
Review
Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review
by Jan Lean Tai, Mohamed Thariq Hameed Sultan, Andrzej Łukaszewicz, Jerzy Józwik, Zbigniew Oksiuta and Farah Syazwani Shahar
Materials 2025, 18(11), 2466; https://doi.org/10.3390/ma18112466 - 24 May 2025
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Abstract
Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the [...] Read more.
Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques. This review systematically examines recent advancements in digital NDT approaches with a particular focus on their application to composite materials. Drawing from 140 peer-reviewed articles published between 2016 and 2024, this review highlights the role of numerical modeling, simulation, machine learning (ML), and deep learning (DL) in enhancing defect detection sensitivity, automating data interpretation, and supporting predictive maintenance strategies. Numerical techniques, such as the finite element method (FEM) and Monte Carlo simulations, have been shown to improve inspection reliability through virtual defect modeling and parameter optimization. Meanwhile, ML and DL algorithms demonstrate transformative capabilities in automating defect classification, segmentation, and severity assessment, significantly reducing the inspection time and human dependency. Despite these promising developments, this review identifies a critical gap in the field: the limited translation of advanced digital methods into field-deployable solutions specifically tailored for FRP piping systems. The unique structural complexities and operational demands of FRP pipes require dedicated research for the development of validated digital models, application-specific datasets, and industry-aligned evaluation protocols. This review provides strategic insights and future research directions aimed at bridging the gap and promoting the integration of digital NDT technologies into real-world FRP pipe inspection and lifecycle management frameworks. Full article
(This article belongs to the Special Issue Modeling and Optimization of Material Properties and Characteristics)
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