2019 : Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches

Ir. Bobby Oedy Pramoedyo S. MSc., Ph.D.
Bambang Pramujati ST, MSc.Eng, Ph.D
Mohammad Khoirul Effendi S.T., M.Sc.Eng.


Abstract

An integrated approach has been applied to predict and optimize multi-performance characteristics, such as optimum thrust force (Fz), torque (Mz), hole surface roughness (Ra), delamination (D) and hole roundness (R), in drilling process of Kevlar fiber reinforced polymer. The experiments were performed by varying drill point geometry and drilling process parameters, i.e., drill point angle, feed rate, and spindle speed. The quality characteristics Fz, Mz, Ra, D, and R were the smaller the better. Taguchi orthogonal array (OA) L18 was used as the design of experiments. Grey fuzzy analysis was first applied to obtain a rough estimation of the optimum drill point geometry and drilling process parameters. Backpropagation neural network (BPNN) model was developed and utilized to predict the optimum Fz, Mz, Ra, D, and R. Genetic algorithm (GA) was performed to search for global optimum of drilling process …