The interest in the application of Graphene oxide/polymer nanocomposites is recently rising in various industries such as drug delivery, bioimaging, aircraft structures, sensors, battery application, etc., due to the improved features of Graphene oxide. The machining behavior of polymers is remarkably different from metallic alloys due to anisotropic and high synergistic effects. This article investigates the machinability aspect and machining response optimization of the developed hybrid nanocomposites. The drilling responses, namely, average surface roughness (Ra), Mean roughness depth (Rz), and Circularity error (Ce), were optimized by using the integrated approach of Combined Compromise Solution and Principal Component Analysis (CoCoSo-PCA). Response Surface Methodology (RSM) array is utilized to execute the drilling tests. The optimal setting is obtained as drill speed 2400 rpm, feed 80 mm/min, 1 weight% of Graphene oxide (G%). The feed rate shows a primary role in controlling both the surface roughness indices and Circularity error. The high feed value affects the development of the drilling-induced defect and cracks. The SEM analysis of the machined samples was performed to check the machined hole quality and surface finishing. ANOVA estimates the model adequacy of the proposed hybrid model. The obtained results have been validated by a confirmatory test, which proves the proposed hybrid module practicality in the manufacturing environment. Besides, the proposed approach is compared with the recently developed optimization modules to evaluate the application potential.

由于氧化石墨烯的改进的特性，近年来在诸如药物递送，生物成像，飞机结构，传感器，电池应用等的各个行业中，对氧化石墨烯/聚合物纳米复合材料的应用的兴趣正在上升。由于各向异性和高协同作用，聚合物的加工性能与金属合金显着不同。本文研究了已开发的杂化纳米复合材料的可加工性和机械加工响应的优化。钻削响应，即平均表面粗糙度（Ra），平均粗糙度深度（Rz）和圆度误差（Ce），通过使用组合折衷解决方案和主成分分析（CoCoSo-PCA）的集成方法进行了优化。响应面方法（RSM）阵列用于执行钻探测试。 rpm，进料 80mm / min，1重量％的氧化石墨烯（G％）。进给速度在控制表面粗糙度指数和圆度误差中起着主要作用。高进给值会影响钻孔引起的缺陷和裂纹的发展。进行机加工样品的SEM分析，以检查机加工孔的质量和表面光洁度。方差分析估计所提出的混合模型的模型充分性。获得的结果已通过验证测试得到验证，这证明了所提出的混合模块在制造环境中的实用性。此外，将所提出的方法与最近开发的优化模块进行了比较，以评估其应用潜力。