Predictive modeling and machining performance optimization during drilling of polymer nanocomposites reinforced by graphene oxide/carbon fiber

Abstract

This paper explores the parametric appraisal and machining performance optimization during drilling of polymer nanocomposites reinforced by graphene oxide/-carbon fiber. The consequences of drilling parameters like cutting velocity, feed, and weight % of graphene oxide on machining responses, namely surface roughness, thrust force, torque, delamination (In/Out) has been investigated. An integrated approach of a Combined Quality Loss concept, Weighted Principal Component Analysis (WPCA), and Taguchi theory is proposed for the evaluation of drilling efficiency. Response surface methodology was employed for drilling of samples using the titanium aluminum nitride tool. WPCA is used for aggregation of multi-response into a single objective function. Analysis of variance reveals that cutting velocity is the most influential factor trailed by feed and weight % of graphene oxide. The proposed approach predicts the outcomes of the developed model for an optimal set of parameters. It has been validated by a confirmatory test, which shows a satisfactory agreement with the actual data. The lower feed plays a vital role in surface finishing. At lower feed, the development of the defect and cracks are found less with an improved surface finish. The proposed module demonstrates the feasibility of controlling quality and productivity factors.