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Parametric Optimization and Prediction of Abrasion Wear Behavior of Marble-Particle-Filled Glass–Epoxy Composites Using Taguchi Design Integrated with Neural Network

  • Machine Learning in Design, Synthesis, and Characterization of Composite Materials
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Abstract

Glass fiber composites are emerging as alternatives to conventional materials. The aim of the present research is to investigate the three-body abrasion wear behavior of marble-powder-filled glass–epoxy composites in an abrasive environment. Three-body abrasion tests were conducted on composites using a rubber wheel abrasion tester as per ASTM-G-65 based on a Taguchi L16 orthogonal array design. The contribution of various control factors is identified based on analysis of variance tests. The abrasion resistance of the composites is observed to increase on addition of microsized marble powder. Two predictive models based on the regression equation and a neural network are developed to predict the abrasion loss of the hybrid composites. The artificial neural network model was found to be more convenient than the regression model to predict the wear loss of these composites. Microscopic observations of the abraded surfaces identified the different wear mechanisms.

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  1. Department of Mechanical and Electrical Engineering, CAET, OUAT, Bhubaneswar, 751003, India

    Subhrajit Ray

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  1. Subhrajit Ray
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Ray, S. Parametric Optimization and Prediction of Abrasion Wear Behavior of Marble-Particle-Filled Glass–Epoxy Composites Using Taguchi Design Integrated with Neural Network. JOM 73, 2050–2059 (2021). https://doi.org/10.1007/s11837-021-04698-8

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  • DOI: https://doi.org/10.1007/s11837-021-04698-8

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