Sustainable Optimization of Drilling Parameters for AA2017/AlN Composite Materials: A Grey Relational Analysis Approach

¹ Research Scholar, Department of Mechanical Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
² Associate Professor, Centre for Materials Engineering and Regenerative Medicine, Bharath Institute of Higher Education and Research, Chennai, India.

^* Corresponding Author: senthil.mech.phd@gmail.com

Abstract

Modern engineering materials have seen remarkable development. Because conventional materials can no longer meet the needs of modern applications, numerous composites are being employed as viable alternatives. Drilling is the most important production step for most uses, and the resulting holes are high-stress zones that must be handled carefully. Scientists and engineers have long been interested in the challenge of finding the best combination of drilling settings for cutting-edge composite materials. As part of this research, AA2017/AlN composites with matrices made of AA2017 aluminium alloy and reinforcements made of 5, 10, and 15 wt % AlN were produced using stir casting. By experimenting with different input parameters, we were able to utilise the L9 OA to determine the best machining arrangement for drilling materials. The investigation focused on critical drilling characteristics, including burr height (H), thrust force (T), and surface roughness (SR), with a keen emphasis on sustainability.By considering the burr height (H), thrust force (T) and surface roughness (SR) and this work used grey relational analysis (GRA) to establish the optimum cutting parameters for drilling holes in the cutting-edge composite AA2017/AlN. The significance of different machining settings and their effect on the typical characteristics of the drilling were analysed using GRA. However, a confirmation experiment was conducted to ensure the highest quality outcomes. Test results and GRA indicate that the best grey relational grade is achieved with the following parameters: spindle speed (S) of 3500 rpm, feed rate (F) of 60 m/s, drill material (D) of Tungsten Carbide, and reinforcement (R) of 10%.. Analysis of variance (ANOVA) revealed statistically significant impacts on GRG from the drill material (29.08%), the feed rate (24.24%), and the spindle speed (19.52%). The error term was a function of the reinforcement percent and its interactions with all other parameters; however, the influence of the interaction between feed rate and drill material on GRG was small. The GRG is 0.856, which is higher than the prediction of 0.824. The calculated and measured values agree quite well and 3.7% is a negligible margin of error. The mathematical models for all reactions depending on the drill bits employed was also constructed.