DOE coupled MLP-ANN for optimization of thrust force and torque during drilling of CCFRP composite laminates

Advancements in technology and the compulsion to use environment-friendly materials have been challenging tasks for researchers for the past two decades. Researchers have been focusing on the utilization of plant fibers to produce good quality fiber-reinforced polymer/polyester composites for automobile, structural, and building applications. Researchers have been looking for high-quality and cost-effective drilling processes. The primary goal of this study is to identify optimal drilling conditions for CCFRP composite laminates, affecting thrust force and torque. This is achieved by manipulating drilling process variables using Taguchi's Design of Experiments (TDOE), Analysis of variance (ANOVA), Response Surface Methodology (RSM), Desirability Function Analysis (DFA) and Artificial Neural Network (ANN). From the results, it was observed that the spindle speed of 2000 rpm, feed of 15 mm/min, point angle of 90 degrees , fiber length of 6 mm, fiber volume of 30%, and fiber diameter of 7 microns gave the optimum results for obtaining minimum thrust force and torque. Further RSM revealed that an increase in fiber vol % and a decrease in spindle speed resulted in an increase in thrust force and torque. From DFA optimization results, the minimum thrust force of 24.0042 N and minimum torque of 0.8001 N-m was obtained. Finally, the experimental values of thrust force and torque were compared with the corresponding values predicted by the MLP-ANN model. The average error percentage for thrust force and torque was 1.75% and 6.56% respectively.