Fuzzy rule based optimization in machining of glass fiber reinforced polymer (GFRP) composites

Abstract

With the increasing use of Fiber Reinforced Polymer (FRP) composites outside the defense, space and aerospace industries; machining of these materials is gradually assuming a significant role. The current knowledge of machining FRP composites is in transition phase for its optimum economic utilization in various fields of applications. Therefore, material properties and theoretical mechanics have become the predominant research areas in this field. With increasing applications, economical techniques of production are indeed very important to achieve fully automated large-scale manufacturing cycles. Although FRP composites are usually molded, for obtaining close fits and tolerances and also achieving near-net shape, certain amount of machining has to be carried out. Due to their anisotropy, and non-homogeneity, FRP composites face considerable problems in machining like fibre pull-out, delamination, burning, etc. There is a remarkable difference between the machining of conventional metals and their alloys and that of composite materials. Further, each composite differs in its machining behavior since its physical and mechanical properties depend largely on the type of fibre, the fibre content, the fibre orientation and variabilities in the matrix material. Considerable amount of literature is readily available on the machinability of conventional metals/alloys and also polymers to some extent; with very limited work on FRP composites. Therefore, machining process optimization for all types FRP composites is still an emerging area of research. In this context, the present research highlights a multi-objective extended optimization methodology to be applied in machining FRP-polyester/epoxy composites with contradicting requirements of quality as well as productivity. Attempt has been made to develop a robust methodology for multi-response optimization in FRP composite machining 6 for continuous quality improvement and off-line quality control. Design of Experiment(DOE) has been be selected based on Taguchi’s orthogonal array design with varying process control parameters like: spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined FRP product along with Material Removal Rate (MRR) of the machining process have been optimized simultaneously. A Fuzzy Inference System (FIS) integrated with Taguchi’s philosophy has been proposed for providing feasible means for meaningful aggregation of multiple objective functions into an equivalent single performance index (MPCI). This Multi Performance Characteristic Index (MPCI) has been optimized finally. Detailed methodology of the proposed fuzzy based optimization approach has been illustrated in this reporting and validated by experiments.