Search
251 articles for “Predictive Material Modeling”
-
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
-
Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article
-
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
-
Polymer Encapsulants for Photovoltaic Modules: Electrical Insulation Performance and Long-Term Durability under Accelerated UV Exposure
Abstract: Influence on the reliability and service life of PV modules is a major issue, which is influenced by the encapsulant degradation in long-term exposure to the sun's ultraviolet (UV) radiation. A systematic accelerated UV aging study of Ethylene Vinyl Acetate (EVA), Polyolefin Elastomer (POE), and Polyvinyl Butyral (PVB) films under IEC 61215-2:2021 specified test chamber conditions of Q-UV is conducted. The material properties were tested at various exposure intervals including …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 384–408 Read article
-
Model to Predict a Ratio Control of Hydrocarbon Acid and Water in a Packed Bed Reactor
Abstract: Model development was carried out to examine the ratio of hydrochloric acid gas and water in a packed bed reactor. The research predicted increase in output with increase in time, revealing the effectiveness ratio control of hydrochloric acid separation from water using absorption column mechanism. The density of the products played an active role in the separation process as well as in control action function. The developed model can be …
Published in Emerging Trends in Chemical Engineering · Vol. 8, Issue 1, 2021 · pp. 45–52 Read article
-
Federated Learning Framework for Sustainable Multi-Scale Design of Recyclable Thermoplastic Graphene Composites in Smart Manufacturing Environments
Abstract: The growing demand for sustainable advanced materials has accelerated the development of recyclable thermoplastic graphene composites for next-generation smart manufacturing systems. The typical central optimization methods have challenges with data privacy, scalability, and poor collaboration between distributed manufacturing sites. By combining material informatics, edge intelligence and distributed artificial intelligence, this study introduces a Federated Learning (FL) framework to design recyclable thermoplastic graphene composites at multiple scales sustainably. The proposed framework …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
-
Viscoelastic Behavior, Interfacial Mechanics, and Reliability of Polymer Interlayers in Laminated Glass Composites: A Comprehensive Review
Abstract: The laminated glass systems are regarded as hybrid polymer–glass composites where the viscoelastic behavior of polymer interlayers mostly controls mechanical response. These interlayers (polyvinyl butyral (PVB), ionoplast, ethylene-vinyl acetate (EVA), etc.) have time-, temperature- and rate-dependent properties which significantly affect shear transfer, energy dissipation, and fracture resistance. But the baseline polymer-relevant processes at the molecular and interfacial level are to a large extent unknown [1]. This review provides a materials-focused …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 258–268 Read article
-
Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials
Abstract: In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum–Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 · pp. 19–35 Read article
-
Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design
Abstract: Advanced polymer materials are widely used in modern engineering and manufacturing because of their lightweight nature, flexibility, durability, and adaptability to different applications. However, designing polymer materials with enhanced thermal and electrical properties remains a challenging task. The performance of polymers is influenced by a complex combination of molecular structures, filler materials, processing parameters, and nanoscale interactions. Conventional optimization methods often require extensive experimental trials and computational resources, making it …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 386–407 Read article
-
Sustainable Concrete Solutions: Chemically Predictive Modeling of Geopolymer Versus Ordinary Concrete in a Circular Economy
Abstract: Concrete is a fundamental structural material widely used in civil engineering projects. It plays a key role in various types of building structures, with its quality directly influencing the durability of the construction. This research focuses on assessing the current state of concrete construction, highlighting critical issues and technical aspects in the process, while emphasizing the importance of improving construction quality and management practices. Geopolymer concrete (GPC) presents a viable …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 170–192 Read article
-
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
-
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
-
Phase – Field Modeling of Brittle and Ductile Fracture Under Complex Loading Conditions
Abstract: Phase-field modeling has emerged as a powerful computational framework for predicting fracture behavior in engineering materials, offering a unified description of crack initiation, propagation, branching, and coalescence without the need for explicit crack tracking. This study presents an in-depth examination of phase-field modeling applied to both brittle and ductile fracture under complex loading conditions, including multiaxial stress states, cyclic loading, thermal gradients, and dynamic impact. The phase-field approach regularizes the …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 3, Issue 2, 2025 · pp. 13–18 Read article
-
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 · pp. 68–80 Read article
-
Evaluating UX Design Factors Affecting Efficiency of Composite Material Design and Analysis Platforms
Abstract: Within engineering software platforms that involve the design, simulation and characterization of composite materials, user experience (UX) design has become a key determinant for efficient use. This research aims to quantify how user experience design parameters relate to productivity in composite engineering workflows by analyzing the relationship between usability, learnability, accessibility, complexity of the UI, navigation efficiency and users engineering results satisfaction. Computational techniques in python were used in the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 341–366 Read article
-
Biopolymer–Cement Hybrid Panels from Recycled Paper Mill Reject: Experimental Characterisation and Machine Learning Optimization
Abstract: The increased rate of the accumulation of industrial residues in the developing countries is a major cause of concern for the environment. The current study brings forth the use of industrial residues in the form of the production of eco-friendly building materials as a sustainable approach to their valorization. The valorization of recycled paper mill reject, a cellulose-based biopolymeric industrial residue, is being addressed in this study as a reinforcement …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 67–90 Read article
-
Analysis of Injection Moulding Process Parameters on PVC Material via Taguchi-ANOVA
Abstract: This paper deals with the development of prediction model for injection moulding machine using Taguchi. In this work, all the process parameters namely filling time (FT), refill time (RFT), tonnage time (TT) and ejector retraction time (ERT) are modeled using Taguchi method. PVC (polyvinylchloride) taken as process material in this experimental work under optimal working conditions. The influence of filling time (FT), refill time (RFT), tonnage time (TT) and ejector …
Published in Journal of Experimental & Applied Mechanics · Vol. 6, Issue 2, 2015 · pp. 13–21 Read article
-
Parametric optimization of end milling operation on EN24 – A Mapping Review
Abstract: The milling of EN24 hardened steels has been applied in many cases in production. The purpose of this review is to study the effect of influence of milling operation parameters such as cutting speed, feed and depth of cut on the material removal rate and surface roughness in milling of EN24 steel. The effect of milling operation parameters are evaluated for minimum surface roughness and maximizing material removal rate. Maximum …
Published in Journal of Production Research & Management · Vol. 8, Issue 3, 2018 · pp. 42–48 Read article
-
Next-Generation Catalysts: Enhancing Efficiency and Selectivity in Chemical Reactions
Abstract: This study investigates the fundamental principles of coatings and their rejuvenation mechanisms, focusing on the development of advanced coatings that not only protect but also restore the performance of degraded surfaces. The article delves into the various types of coatings, including organic, inorganic, and hybrid formulations, emphasizing their distinct characteristics and applications. A significant portion of the study is dedicated to the mechanisms of rejuvenation, where we analyze how specific …
Published in Journal of Catalyst & Catalysis · Vol. 11, Issue 3, 2024 · pp. 24–28 Read article