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47 articles for “delamination”
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Response of Sea Water Immersed GFRP Composite to Thermal Shock
Abstract: The work involves examining the effects of sea water immersion and temperature fluctuations, precisely that of up and down thermal shocks (lower to higher and vice-versa), on the glass fiber /epoxy laminated composites. These effects are studied in terms of sea water absorption, degradation in inter laminar shear strength (ILSS), alternations in the glass transition temperature (Tg) as compared to the untreated samples and finally listing up of the mode …
Published in Journal of Materials & Metallurgical Engineering · Vol. 3, Issue 1, 2013 · pp. 34–41 Read article
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Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision
Abstract: The biomedical composite materials are intelligent failure-detecting, which is necessary to ensure the reliability, safety, and durability of the current healthcare equipment. This paper describes a machine vision design, which incorporates convolutional neural networks, transformer models, and ensemble learning to correctly detect and localize material defects. The proposed system takes advantage of the capabilities of high-resolution imaging, advanced preprocessing software, and deep feature learning in the identification of the intricate …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Finite Element, Experimental, and Machine Learning-Based Optimization of Machining Stability for Polymer Composite Material Processing
Abstract: The machining of polymer composite materials, particularly fibre-reinforced polymer-matrix composites, requires stable spindle-tool performance to avoid delamination, fibre pull-out, matrix cracking, thermal softening, poor surface integrity, and premature tool wear. In line with the scope of the Journal of Polymer & Composites, this study presents an integrated finite element, experimental, and machine learning framework for improving machining stability during end-milling of composite material systems. The spindle-tool assembly is modelled using …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Development of Multilayer Metallic Coatings with Graded Interfaces for Enhanced Wear and Fatigue Resistance in Aerospace Components
Abstract: This study presents a comprehensive investigation into the development and characterization of multilayer metallic coating systems with compositionally graded interfaces, specifically engineered to enhance wear and fatigue resistance in aerospace structural and rotating components. Four coating architectures were systematically designed: (i) CrN/TiAlN bilayer, (ii) TiN/TiAlN/TiAlSiN gradient trilayer, (iii) CrAlN/AlCrN nanolaminate, and (iv) a novel five-layer CrN–TiN–TiAlN–AlCrN–TiAlSiN functionally graded system (FGS). Coatings were deposited via high-power impulse magnetron sputtering (HiPIMS) and …
Published in Journal of Thin Films, Coating Science Technology & Application · Vol. 13, Issue 2, 2026 · pp. 50–67 Read article
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A IoT-Enabled Predictive Intelligence for Real-Time Failure and Damage Evolution Monitoring of Polymer Composites
Abstract: Damage assessment of carbon-fibre-reinforced polymer composites is still challenging since the damage occurs as a combination of matrix cracking, interfacial debonding, delamination and fibre fracture. The present work proposes a framework for predictive-intelligence based on IoT for multiaxial fatigue and compression-after-impact (CAI) CFRP experiments, employing publicly available acoustic-emission (AE) datasets. A causal CNN–GRU attention model is developed by integrating time-domain, spectral, wavelet, loading-history and trend features to estimate the damage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Hybrid Machine Learning and Finite Element Framework for Predicting Damage Behavior in Fiber-Reinforced Polymer Composites
Abstract: Fiber Reinforced Polymer (FRP) composites have broad spread use in aerospace, automotive, marine and structural applications due to its high specific strength, stiffness and corrosion resistance. The various damage mechanisms such as matrix cracking, fiber breakage, delamination and interfacial failure, however, make the forecasting of damage particularly complex. In this work, a hybrid machine learning (ML) and finite element (FE) system is proposed for predicting the damage behavior of FRP …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article