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146 articles for “Material Property Prediction”
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning
Abstract: Polymer composites show great promise as engineering materials because of their mechanical performance, resistance to corrosion, lightweight nature, and adaptability in design. Aerospace, automotive, biomedical, maritime, and civil engineers all rely on mechanical property prediction to cut down on trial expenses, expedite product development, and optimize material selection. Speedy design optimization is not possible using traditional numerical and experimental methods due to the high costs associated with material characterisation, computational …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1258–1284 Read article
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A Comprehensive Survey of Polymer Detection Techniques and Computer-Based Analysis Methods for Advanced Material Characterization
Abstract: Polymers are widely used in aerospace, automotive, biomedical, packaging, electronics, and manufacturing industries because of their lightweight nature, durability, and versatility. Accurate polymer identification and characterization are essential for quality control, recycling, performance assessment, and the development of advanced materials. Characterization helps determine important properties such as chemical composition, molecular structure, thermal stability, mechanical strength, and surface morphology, which influence material performance and application suitability. Traditional polymer detection methods include …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 921–929 Read article
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Computational Modeling of Polymer Semiconductors for Electronic Applications
Abstract: Polymer semiconductors have become important materials in modern electronic applications because they combine semiconducting behavior with mechanical flexibility, low-cost processing, and tunable molecular structure. Their growing use in organic field-effect transistors, organic photovoltaics, organic light-emitting diodes, and flexible sensing devices has increased the need for accurate computational approaches that can predict material properties and device performance before experimental fabrication. This paper reviews the major computational modeling techniques used for polymer …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 132–146 Read article
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AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films
Abstract: The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1026–1041 Read article
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Analysis of Machine Learning in Metal Processing: A Novel Prospect
Abstract: Metal is processed by a wide range of procedures, from forming and casting to machining and riveting. Metal processing is a crucial part of modern manufacturing. The application of machine learning (ML) is driving a significant change in the sector, which has historically depended on empirical knowledge and trial-and-error techniques. Increased production, improved product quality, and resource optimization are expected outcomes of this action. This study aims to explore the …
Published in Journal of Materials & Metallurgical Engineering · Vol. 16, Issue 1, 2026 · pp. 41–51 Read article
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Artificial Intelligence for Polymer and Nanocomposite Materials: Performance Prediction, Manufacturing Optimization, and Future Perspectives
Abstract: The exceptional mechanical properties, design flexibility, and lightweight nature of polymer composite and nanocomposite materials make them indispensable in a wide range of applications, including aerospace, automotive, construction, biomedical, and energy sectors. The optimization of the strength, durability, and manufacturing efficiency of polymer composite and nanocomposite materials is highly challenging because their performance depends on matrix composition, reinforcement type, fiber or nanoparticle distribution, interfacial interactions, processing conditions, and environmental factors. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Role of Artificial Intelligence in Quantum Materials Research
Abstract: Quantum materials have emerged as a transformative class of advanced materials due to their extraordinary electronic, magnetic, optical, and topological properties governed by quantum mechanical phenomena. These materials are expected to revolutionize next-generation technologies such as quantum computing, spintronics, superconducting electronics, nanoelectronics, intelligent sensing systems, and energy-efficient devices. However, conventional methods for discovering and optimizing quantum materials are often expensive, time-consuming, and computationally intensive because of the enormous complexity of …
Published in Journal of Materials & Metallurgical Engineering · Vol. 16, Issue 2, 2026 · pp. 13–27 Read article
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Affect of Finite Element Modelling on Static Behavior of Underwater Shell
Abstract: The objective of the present study is to compare and contrast different numerical models based on their mesh quality and material properties in predicting the response of an underwater shell under an external pressure of 1 MPa. Two different mesh qualities with the same element size are developed for solid geometries without and with slicing the solids at geometric discontinuities to enhance the element quality. From the static response of …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 154–163 Read article
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Data-Driven Design Framework for Biofunctional Polymer Composite Materials
Abstract: This paper introduces a knowledge-based design platform of biofunctional polymer composite substances through the combination of machine learning, materials informatics, and digital twins applications. The framework allows the effortless forecasting and maximization of mechanical, biological and degradation characteristics based on supervised, unsupervised and deep learning models. A materials database is accompanied by the AI algorithms to find the best material compositions and microstructure-property relationships. Experimental validation proves to be more …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.
Abstract: This research investigates predictive modelling and optimization technique for the tensile and flexural strength of PlA (Poly Lactic Acid) in Fused Deposition Modelling (FDM) 3D printing. Employing Decision Trees and Bayesian Optimization enhances comprehension and control of 3D printing process. Precise model predicts PLA material properties based on input parameters. Methodology involves rigorous data preprocessing, encompassing, cleaning, transformation, and normalization. Hyperparameter optimization via grid search systematically explores configurations, optimizing model …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 203–214 Read article
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Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article
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AI-Optimized Biodegradable Polymer Composites for Medical Applications
Abstract: The value of biodegradable polymer composites in the medical practice has been massive as the composites may be deployed to provide temporary structural support, and they are also safe to degrade within the human body. However, the conventional material design process is trial and error, which is ineffective and inefficient. The article proposes a hybrid model involving experimental characterization, as well as an artificial intelligence (AI)-based model, to optimize biodegradable …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
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Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites
Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates
Abstract: Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 2, Issue 1, 2024 · pp. 34–45 Read article
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Fatigue Analysis of the FSAE Vehicle's Front Wheel Hub
Abstract: This study illustrates the design and analysis of the FSAE vehicle's front wheel hub. Because wheel hubs are subjected to cyclic loads on a continuous basis, there is a risk of fatigue, which leads to material failure. The start and spread of cracks in a material as a result of cyclic loading is known as fatigue. The brake discs could not be easily removed since the disc is positioned between …
Published in Journal of Automobile Engineering and Applications Read article
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Fatigue Analysis of the FSAE Vehicle's Front Wheel Hub
Abstract: This study illustrates the design and analysis of the FSAE vehicle's front wheel hub. Because wheel hubs are subjected to cyclic loads on a continuous basis, there is a risk of fatigue, which leads to material failure. The start and spread of cracks in a material as a result of cyclic loading is known as fatigue. The brake discs could not be easily removed since the disc is positioned between …
Published in Journal of Automobile Engineering and Applications · Vol. 9, Issue 1, 2022 · pp. 18–26 Read article