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.

中文翻译：

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通过基于 ANN 的航空航天应用预测模型优化冷冻处理塑料/氧化石墨烯改性 CFRP 的摩擦学特性

碳纤维增强聚合物（CFRP）采用聚碳酸酯（PC）/丙烯酸丁二烯苯乙烯（ABS）和硅烷化氧化石墨烯沉积（使用电泳沉积）碳纤维进行改性。使用销盘 (POD) 实验，确定了改性 CFRP 样品在室温 (RT) 和低温处理 (CT) 条件下的磨损率 (WR) 和摩擦系数 (COF)。对于 RT 样品，与 NEAT 样品相比，SGO 样品的 COF 最大降低了 25.6%，而 PC/ABS 样品的 WR 最大降低了 42.1%。但对于 CT 样品，SGO 的 COF 最大降低量为 32.6%，PC/ABS 的 WR 最大降低量为 41%。此外，方差分析 (ANOVA) 发现“样本成分”是体积损失平均值的最关键组成部分，而田口分析则完善了参数并达到了预期的结果。为了预测磨损行为，使用了 Levenberg-Marquardt 算法、人工神经网络和线性回归。根据均方误差 (MSE) 损失，基于 Matlab 的神经网络方法表现最好，RT 样本的 MSE 为 0.35%，CT 样本的 MSE 为 0.88%。这项工作有助于使用优化技术了解和增强 CFRP 材料的耐磨应用。