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62 articles for “material nonlinearity”
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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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Facile Synthesis and Characterization of Cd²⁺ Doped NiAl₂O₄ Nanoparticles for Advanced Polymer Composite Integration
Abstract: Ni1-xCdxAl2O4, where 0 ≤ x ≤ 0.5, was successfully synthesized using the chemical co-precipitation technique, a scalable and cost-effective route suitable for composite material applications. X-ray diffraction (XRD) analysis revealed a crystalline cubic spinel structure for the resulting nanoparticles, with determined crystallite sizes ranging from 36 to 12 nm for NiAl2O4 and Cd-doped NiAl2O4. The respective band gaps for the materials were found to be 3.51 and 4.18 eV. Infrared …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 59–72 Read article
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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
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Machine Learning Based Optimization of Polymer Structure Property Relationships in Composite Material Systems
Abstract: In modern engineering applications, polymer-based composite materials have garnered a lot of attention because of their lightweight nature, high strength-to-weight ratio, and changing physical features. In order to maximize the relationships between polymer structure and properties in composite materials, this study suggests a strategy based on reinforcement learning (RL). The research utilized the Polymer Composite Properties Dataset, which contains 12,700 records associated with polymer matrices, reinforcement fillers, interfacial bonding characteristics, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 242–255 Read article
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Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes
Abstract: The rapid expansion of intelligent sensing in the Internet of Things (IoT) has revealed the pressing need for materials and algorithms capable of self-adaptation in volatile environments. Conventional polymer-based sensors and static control strategies often fail to capture nonlinear thermo-mechanical dynamics, leaving them unsuitable for unpredictable operating conditions. Although prior studies have improved polymer composites or introduced algorithmic optimization independently, few attempts have coupled the adaptability of smart materials with …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 370–391 Read article
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Particle Swarm Optimization based PID controller for Extrusion Process
Abstract: The process called extrusion was used for making objects of fixed cross-sectional profile by pushing or pulling the material through the die of the preferred cross-section. The extrusion process was a nonlinear process in which the quality of the product relies on various parameters such as temperature, extrusion speed, etc.; hence there should be a controller working in an optimized manner required to maintain these parameters properly suitable for the …
Published in Journal of Control & Instrumentation · Vol. 11, Issue 2, 2020 · pp. 10–16 Read article
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Enhancing Surface Roughness of Polylactic Acid (PLA) 3D-Printed Parts Using CO₂ Laser Scanning: An Experimental Study on Parameter Optimization
Abstract: Fused deposition modeling (FDM) of polylactic acid (PLA) often suffers from poor surface finish due to the inherent layer-by-layer deposition process, limiting its use in high-precision applications. This study investigates CO₂ laser scanning as an efficient post-processing technique to reduce the surface roughness (Ra) of PLA parts while maintaining structural integrity. Specimens (100 × 80 × 5 mm) were fabricated with varying infill densities (35%, 70%, and 100%) to assess …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 503–511 Read article
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Physics-Informed Neural Networks for Multiphysics Analysis of Biomedical Polymer Composite Systems
Abstract: Physics-Informed Neural Networks (PINNs) offer an effective model of solving coupled multiphysics equations in biomedical polymer composite systems, which are data-driven. In the given work, the PINN method is presented where equations of elasticity, mass diffusion, and heat transfer are integrated to model the complex processes that take place in composite biomaterials. The neural network loss is specified to include the governing partial different equations which enables both the system …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Debris Flow Kinetics in Planetary Environments: A Systems Perspective
Abstract: Debris flow kinetics in planetary environments represent a critical intersection of geomorphology, fluid mechanics, and planetary science. These gravity-driven flow mixtures of solids, liquids, and gases play a key role in shaping planetary surfaces and recording environmental histories. This study adopts a systems perspective to analyze debris flow behavior across different planetary contexts, emphasizing the interconnected roles of material properties, energy transformations, and environmental forcing. By integrating rheological models with …
Published in International Journal of Universe · Vol. 1, Issue 2, 2025 · pp. 08–17 Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 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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AI-Optimized Nano-Silica Reinforced PCM Composites for Predictive Solar-Thermal Energy Storage Networks
Abstract: This study presents an AI-optimized nano-silica reinforced polymer composite phase change material (PCM) for predictive solar-thermal energy storage networks. The proposed composite combines paraffin wax, high-density polyethylene (HDPE), and uniformly dispersed nano-silica particles to improve thermal conductivity, structural stability, leakage resistance, and long-term cycling performance. The composite was fabricated through melt blending and ultrasonication-assisted nanoparticle dispersion, followed by comprehensive morphological, chemical, thermal, and thermophysical characterization using scanning electron microscopy (SEM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Overview of Ionic Polarization: A Model Based Novel Approach
Abstract: This study presents a comprehensive analysis of ionic polarization through a novel model-based approach that integrates theoretical, computational, and experimental methodologies. Ionic polarization, which significantly influences the dielectric properties of materials, is examined through the lens of the Clausius-Mossotti equation and the Debye relaxation model, providing a theoretical framework for understanding the relationship between ionic displacement and dielectric behavior. To explore ionic displacement and polarization at the atomic level, advanced …
Published in International Journal of Cheminformatics · Vol. 2, Issue 2, 2024 · pp. 18–25 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 Read article
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Modeling and Simulation of Radiative MHD Casson-Type Polymer Composite Flow over a Porous Wedge with Variable Thermal Source and Sink
Abstract: This study presents a numerical investigation of the radiative magnetohydrodynamic (MHD) flow and heat transfer characteristics of a Casson-type polymer fluid over a moving and extending porous wedge under the influence of a spatially varying heat source and sink. The Casson fluid model, representing a class of viscoplastic polymeric materials, is analyzed within the MHD framework to explore the combined effects of magnetic field intensity, rheological behavior, and porous medium …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 837–850 Read article
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POLYMER AND COMPOSITE-BASED GEOSYNTHETIC REINFORCEMENTS FOR SEISMIC STABILITY OF SOIL RETAINING STRUCTURES: MATERIALS, MECHANICS, AND PERFORMANCE REVIEW
Abstract: Geosynthetic materials based on polymer and composites have become important items for the structural performance and seismic resilience of the reinforced soil retaining systems. Mechanically stabilized earth walls in recent geotechnical engineering practice are increasingly based on enhanced polymeric reinforcements for enhanced tensile strength, durability, flexibility, and energy dissipation under dynamic loading. High-density polyethylene, polypropylene, polyester, and fiber-reinforced polymer composites are usually used. The present review focus on the recent …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Experimental Analysis of Glass Fiber Composite on Low Velocity Impacts
Abstract: This research specifically deals with determining the retained tensile strength after loading Glass Fiber Reinforced Polymer (GFRP) composites following low-velocity impact. The purpose is to determine the degree to which these impacts affect the structural performance and mechanical integrity of GFRP materials. Experimental tests were conducted on glass fiber composite specimens in order to observe variation in tensile strength upon impact. The results indicated significant tensile strength reduction in impact …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 789–796 Read article
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Growth, Structural and Optical characteristics of Citric Acid Doped Copper Sulphate Single Crystals Polymer Composites Photonic Applications
Abstract: The organic material Citric Acid doped Copper Sulphate single crystals (CACS) was synthesized and single crystal was grown by slow evaporation method. The incorporation of citric acid, an organic compound, introduces polymer-like functional behavior into the crystal matrix, forming an organic-inorganic hybrid composite with enhanced optical and structural characteristics. Single crystal XRD confirmed the triclinic system with unit cell parameters: a = 5.96 Å, b = 6.11 Å, c = …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 482–488 Read article
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Manganese Mercury Thiocyanate Doped Zirconium Silicate Crystal Structure for Photo-Electronic, Electro-Optic and Sensor Applications
Abstract: Manganese mercury thiocyanate is a well-known organometallic crystal prized for its nonlinear optical properties. The high second harmonic efficiency of nearly 18 times that of urea and the wide optical transmittance window (373–2250 nm) of MMTC indicate that this material is an excellent candidate for photonics device fabrication. The metal thiocyanates and their Lewis-base adducts are one of the interesting themes of structural chemistry. For MMTC is a very flexible …
Published in Journal of Materials & Metallurgical Engineering · Vol. 16, Issue 2, 2026 Read article
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Prediction of Depth-Induced Stress Distribution and Maintenance Cost Implications for Submerged Structural Components
Abstract: This study investigates the influence of water depth on stress distribution and structural integrity of submerged mechanical components . Structural models fabricated from mild steel, stainless steel, carbon steel, and copper alloy were examined under hydrostatic loading corresponding to water depths between 30 cm and 150 cm. Results indicate that normal and shear stresses increased proportionally with depth due to intensified hydrostatic pressure. Mild steel exhibited the highest stress concentrations, …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 3, Issue 2, 2025 · pp. 29–35 Read article