This article explores the drilling behavior of polymer nanocomposites reinforced by Graphene oxide/Carbon fiber using a hybrid method of Grey theory and Principal component analysis (GR-PCA). An online digital dynamometer was employed for the evaluation of Thrust Force and Torque. The image processing technique computes the delamination. Response surface methodology (RSM) considers the parameters, namely, drilling speed (S), feed rate (F), Graphene Oxide wt.% (G) in designing the experimentation array. Principal component analysis (PCA) was used to tackle the response priority weight during the combination of multiple functions. Analysis of variance (ANOVA) scrutinized the influence of parameters and intended the regression models to predict the response. GR-PCA evaluated the optimal parametric setting and remarked that feed rate acts as the most predominant factor. The higher feed rate and wt.% of G is responsible for surface damages like fiber pull-out, fiber fracture and cracks. A significant improvement in drilling responses has been obtained and also validates through confirmatory test and microstructure investigations.

本文使用灰色理论和主成分分析 (GR-PCA) 的混合方法探讨了由氧化石墨烯/碳纤维增强的聚合物纳米复合材料的钻孔行为。在线数字测功机用于评估推力和扭矩。图像处理技术计算分层。响应面法 (RSM) 考虑了参数，即钻孔速度 ( S )、进给速率 ( F )、氧化石墨烯重量百分比 ( G) 设计实验阵列。主成分分析（PCA）用于处理多个函数组合过程中的响应优先权重。方差分析 (ANOVA) 仔细检查参数的影响，并使用回归模型来预测响应。GR-PCA 评估了最佳参数设置，并指出进给速率是最主要的因素。较高的进料速率和G 的重量百分比是造成表面损伤的原因，如纤维拉出、纤维断裂和裂纹。已经获得了钻井响应的显着改善，并且还通过验证性测试和微观结构研究进行了验证。