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134 articles for “property prediction modeling”
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AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams
Abstract: Biodegradable polymer composites have emerged as a sustainable alternative to petroleum-based materials in packaging, biomedical, and structural applications. However, traditional formulation techniques for reinforced polymer composites often lack precision and fail to adapt to real-time variations during processing, resulting in suboptimal material performance. This research proposes a real-time AI-IoT-enabled framework to optimize biopolymer composite formulations. The goal is to intelligently tune composite properties such as mechanical strength, moisture resistance, and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 85–100 Read article
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AI-Driven Innovation in Biomaterials: Predictive Modeling and Design for the Future
Abstract: The integration of artificial intelligence (AI) is revolutionizing the field of biomaterials, paving the way for innovative approaches in their development and production. This paper examines the connection between AI and biomaterials, emphasizing the substantial impact of predictive modeling on the evolution of the field. By examining recent research and cutting-edge uses, the document shows how AI-powered predictive modeling has revolutionized biomaterial design, marking a period of unparalleled precision and …
Published in Trends in Machine design · Vol. 11, Issue 3, 2024 · pp. 25–35 Read article
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Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites
Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article
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House Price Prediction Using Linear Regression In Machine Learning
Abstract: In the modern world, real estate is among the most important investments, particularly in a city like Chennai, which is where many people aspire to work and settle down. Due to people's high purchasing power, this will cause property prices to rise daily. When purchasing a home, buyers will consider whether or not it will yield a healthy profit margin. Hence, before spending your hard-earned money on any property, it …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 92–100 Read article
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Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites
Abstract: Agricultural biomass can reduce the environmental burden of polymer composites, yet its heterogeneous structure creates competing effects on strength, moisture resistance, density, and process ability. This study developed an artificial intelligence-assisted framework for balanced composite formulation. Experimental data of agricultural biomass reinforced polymer composites were gathered, harmonized and validated using leakage controlled validation. The mechanical and physical properties were predicted by artificial neural networks and conventional regression models. Explainable analysis …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Fracture Analysis of Functionally Graded Material (FGM) Plates Using Extended Finite Element Method: A Review.
Abstract: Functionally Graded Materials (FGMs), have drawn a lot of interest in various engineering applications due to their superior mechanical properties and ability to withstand extreme conditions. Fracture analysis in FGMs focuses on understanding how cracks initiate and propagate within these complex materials. The stress distribution becomes irregular due to spatial property variations which produces different crack paths than what occurs in homogeneous materials. The examination of FGM plates under fracture …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 9–15 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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Interest Level Prediction in Rental Properties Using Data Science
Abstract: A key component of forecasting home prices and rental patterns is real estate market analysis. Data science, data mining methodologies, and statistical models are some of the strategies that have been created in recent years to solve this problem. A few problems are still required to be resolved, such as the obstacles caused by the availability and quality of the data; the presence of outliers, missing values, and inconsistent formats …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 28–34 Read article
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Advanced Computational Models for Predicting Molecular Interactions
Abstract: Understanding molecular interactions is essential for a number of disciplines, including biochemistry, materials science, and medication development. Traditional experimental methods, while accurate, are often time-consuming and expensive. Advanced computational models have emerged as powerful tools to predict molecular interactions efficiently. In order to predict the behavior and interactions of molecules at the atomic and subatomic levels, this paper reviews the most recent developments in computational techniques, such as machine learning …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 1, 2024 · pp. 8–13 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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Optimizing Mechanical and Durability Properties of Eco-Friendly Composite Materials Using Recycled Fillers and ML Techniques
Abstract: The increasing demand for sustainable construction materials has intensified the exploration of recycled fillers as partial or full replacements for natural aggregates in composite materials. This study investigates the mechanical and durability performance of polymer matrix composites incorporating processed recycled fillers derived from construction and demolition (C&D) waste. Three distinct processing methods were employed to prepare the recycled fillers: untreated (URF), single processed (SPRF), and double processed (DPRF), with replacement …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 269–309 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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Mechanical Performance Assessment of Hybrid FRP Laminates with Carbon Fiber Core Using Experimental and Numerical Approaches
Abstract: The high strength-to-weight ratio, corrosion resistance and design flexibility of Fiber-reinforced polymer (FRP) composites have attracted considerable attention in aerospace, automotive and structural applications. This work presents an experimental and finite element study on the tensile and flexural behavior of epoxy-based hybrid FRP laminates. Five laminate configurations were manufactured, including a unidirectional carbon fiber laminate and four hybrid laminates, Kevlar–Carbon–Kevlar (K/C/K), Glass–Carbon–Glass (G/C/G), Kevlar–Carbon–Glass (K/C/G), and Glass–Carbon–Kevlar (G/C/K). For all …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Investigation of Mechanical Properties of Banana, Linen and Their Hybrid Reinforced Composite Laminates in Adverse Condition and Analyze Using ML
Abstract: This research investigates the mechanical performance of composite laminates reinforced with banana and linen fibers, focusing on both individual and hybrid fiber combinations. The primary objective is to assess how these natural fiber composites behave under extreme environmental conditions, particularly high humidity and fluctuating temperatures, which are common in aerospace and automotive applications.Key mechanical properties—tensile strength, flexural strength, and impact resistance—are experimentally evaluated to assess the performance and long-term reliability …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 25–31 Read article
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Interfacial Bonding Efficiency in Jute-Carbon Fiber Polyester Matrix Composites Evaluated Through Short Beam Shear and Interlaminar Fracture Toughness Testing
Abstract: The natural Fiber–synthetic Fiber hybrid composites are jute–carbon Fiber hybrid in polyester matrix which could be used in semi-structural applications of automotive, construction and marine industry as an eco-friendly and cost-effective alternative of conventional carbon Fiber reinforced polymer. But the hydrophilic nature of jute Fibers and the relatively inert surface of carbon Fiber's cause weak bonding at the interface of the polyester matrix, which causes premature delamination, poor interlaminar shear …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering
Abstract: The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1–19 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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AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review
Abstract: The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1083–1097 Read article
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Machine Learning Based House Price Forecasting
Abstract: This research endeavours to craft a predictive model leveraging machine learning to estimate the market value of houses in Delhi. By integrating Python and its powerful libraries, pandas for data processing, Plot for interactive visualizations, scikit-learn for implementing machine learning algorithms, XGBoost for boosting the model's prediction accuracy, and to evaluate the model's performance cross-validation techniques are used. An interactive user interface is created using a Flask web application to …
Published in Current Trends in Information Technology · Vol. 14, Issue 1, 2024 · pp. 5–11 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