Optimization of tribological characteristics in cryo-treated plastic/graphene oxide modified CFRP via ANN-based predictive modeling for aerospace applications

Abstract

Carbon fiber reinforced polymers (CFRP) were modified with polycarbonate (PC)/acrylic butadiene styrene (ABS), and silanized graphene oxide deposited (using electrophoretic deposition) carbon fibers. Using Pin-on-Disc (POD) experiment, the wear rate (WR) and coefficient of friction (COF) of modified CFRP samples were determined for room-temperature (RT) and cryo-treated (CT) conditions. For RT samples, the largest reduction in COF compared to NEAT samples was for SGO samples by 25.6%, while the largest reduction in WR was for PC/ABS samples at 42.1%. But for CT samples, the maximum reduction in COF was 32.6% by SGO, and PC/ABS achieved the maximum reduction in WR by 41%. Also, Analysis of Variance (ANOVA) found “sample composition” to be the most critical component for volume loss mean, whereas Taguchi analysis refined the parameters and achieved the desired outcome. To predict wear behaviour, the Levenberg-Marquardt algorithm, artificial neural network, and linear regression were used. Based on mean squared error (MSE) loss, the Matlab-based Neural Network method performed best, with 0.35% MSE for RT samples and 0.88% for CT samples. This work helps to understand and enhance CFRP materials for wear-resistant applications using optimization techniques.