Experimental and predictive analysis of solid particle erosion wear in interlaced E-glass/carbon hybrid polymer composites

This study presents an experimental and predictive investigation of solid-particle erosion wear in interlaced E-glass/Carbon hybrid polymer composites. Hybrid polymer composites were fabricated through vacuum-assisted resin transfer moulding followed by autoclave curing, and their solid particle erosion behaviour was systematically evaluated under different operating conditions using a Taguchi L16 orthogonal array. Key process parameters, including impact velocity, impingement angle, and laminate layer configuration, were systematically varied during the erosion experiments. Analysis of variance showed that impact velocity was the primary factor influencing erosion wear, primarily due to increased kinetic energy and intensified micro-cutting mechanisms, followed by impingement angle and laminate layer configuration. SEM microstructural examination revealed severe matrix degradation, exposed reinforcement fibers, interfacial debonding, and irregular debris formation under aggressive erosive environments. To establish an accurate predictive framework, metaheuristic optimization techniques were used to tune the machine learning model parameters. Among the proposed predictive models, the Jaya-optimized Gradient Boosting framework demonstrated superior agreement with the experimental results, achieving a test R 2 of 0.9750 and a minimum MSE of 8.27 × 10 −10 . The excellent agreement between experimental measurements and predicted results validates the effectiveness of the proposed framework for accurate erosion wear prediction of hybrid polymer composites.

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Publication Details

Journal
Journal of Composite Materials
Published
2026-08-26
DOI
https://doi.org/10.1177/00219983261483167
Primary Topic
Erosion and Abrasive Machining
Type
article
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article

Experimental and predictive analysis of solid particle erosion wear in interlaced E-glass/carbon hybrid polymer composites

Amar Patnaik, Vikas Kukshal, Pinkesh Kumar Sharma, Yash Jamine
Journal of Composite Materials
Erosion and Abrasive Machining
article

Experimental and predictive analysis of solid particle erosion wear in interlaced E-glass/carbon hybrid polymer composites

Amar Patnaik, Vikas Kukshal, Pinkesh Kumar Sharma, Yash Jamine
article en

Abstract

This study presents an experimental and predictive investigation of solid-particle erosion wear in interlaced E-glass/Carbon hybrid polymer composites. Hybrid polymer composites were fabricated through vacuum-assisted resin transfer moulding followed by autoclave curing, and their solid particle erosion behaviour was systematically evaluated under different operating conditions using a Taguchi L16 orthogonal array. Key process parameters, including impact velocity, impingement angle, and laminate layer configuration, were systematically varied during the erosion experiments. Analysis of variance showed that impact velocity was the primary factor influencing erosion wear, primarily due to increased kinetic energy and intensified micro-cutting mechanisms, followed by impingement angle and laminate layer configuration. SEM microstructural examination revealed severe matrix degradation, exposed reinforcement fibers, interfacial debonding, and irregular debris formation under aggressive erosive environments. To establish an accurate predictive framework, metaheuristic optimization techniques were used to tune the machine learning model parameters. Among the proposed predictive models, the Jaya-optimized Gradient Boosting framework demonstrated superior agreement with the experimental results, achieving a test R 2 of 0.9750 and a minimum MSE of 8.27 × 10 −10 . The excellent agreement between experimental measurements and predicted results validates the effectiveness of the proposed framework for accurate erosion wear prediction of hybrid polymer composites.

Journal of Composite Materials
University of Manchester (GB), National Institute of Technology Uttarakhand, Malaviya National Institute of Technology Jaipur (IN), National Institute of Technology Srinagar (IN)
Openalex Percentile: Top 11%
Erosion and Abrasive Machining
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