Sensitivity Analysis of GFRP Composite Drilling Parameters and Genetic Algorithm-Based Optimisation

Abstract

In this article, a genetic algorithm (GA) is used for optimizing a metamodel of surface roughness (R_a ) in drilling glass-fibre reinforced plastic (GFRP) composites. A response surface methodology (RSM) based three levels (-1, 0, 1) design of experiments is used for developing the metamodel. Analysis of variance (ANOVA) is undertaken to determine the importance of each process parameter in the developed metamodel. Subsequently, after detailed metamodel adequacy checks, the insignificant terms are dropped to make the established metamodel more rigorous and make accurate predictions. A sensitivity analysis of the independent variables on the output response helps in determining the most influential parameters. It is observed that f is the most crucial parameter, followed by the t and D. The optimization results depict that the R_a increases as the f increases and a minor value of drill diameter is the most appropriate to attain minimum surface roughness. Finally, a robustness test of the predicted GA solution is carried out.