Machining behaviour of Al-based in-situ composites: Experimental and statistical analyses

Abstract

Metal matrix composites play a dynamic role in manufacturing industries due to their superior engineering properties. The current research investigation analyses the effects of machining input parameters on process performance and proposes a robust hybrid optimisation technique for effective drilling of Al-4.5%Cu-1.5%Mg-7%(AlB₂-Al₂O₃) composites. The machining input parameters considered are cutting speed, feed rate, drill diameter and point angle. The Taguchi L₁₈ design has been applied to conduct drilling experiments. The main objective of this research work was to achieve the best drilling performance characteristics, such as low thrust force, torque and surface roughness. Main effect plots, chip morphology and tool wear mechanism were analysed and reported. From ANOVA results, the feed rate has been identified as a significant drilling parameter influencing the machining response characteristics. Regression models were developed and experimentally verified for each of the drilling response characteristics. The findings of the confirmation test revealed the desired improvement in machining responses. With a lower mean error (5.61%), the Taguchi grey relation analysis integrated with principal component analysis (TGRA-PCA) approach was determined to be better, suggesting a statistically significant model. The response surface methodology with non-dominated sorting genetic algorithm-II (RSM-NSGA-II) predicted a lower value of the optimal thrust force, torque, and surface roughness with a desirability of 0.9789 in comparison with RSM-DF and TGRA-PCA.