Multi-Objective Optimization of Machining Parameters for Drilling LM5/ZrO2 Composites Using Grey Relational Analysis

Abstract

In today’s world, engineering materials have changed dramatically. Traditional materials are failing to satisfy the demands of present applications, so several composites are being used to address these issues. Drilling is the most vital manufacturing process in most applications, and the drilled holes serve as maximum stress areas that need to be treated with extreme caution. The issue of selecting optimal parameters for drilling novel composite materials has fascinated researchers and professional engineers for a long time. In this work, LM5/ZrO2 composites are manufactured by stir casting using 3, 6, and 9 wt% zirconium dioxide (ZrO2) as reinforcement and LM5 aluminium alloy as matrix. Fabricated composites were drilled using the L27 OA to determine the optimum machining parameters by varying the input parameters. The purpose of this research is to find the optimal cutting parameters while simultaneously addressing the thrust force (TF), surface roughness (SR), and burr height (BH) of drilled holes for the novel composite LM5/ZrO2 using grey relational analysis (GRA). The significance of machining variables on the standard characteristics of the drilling as well as the contribution of machining parameters were found using GRA. However, to obtain the optimum values, a confirmation experiment was conducted as a last step. The experimental results and GRA reveal that a feed rate (F) of 50 m/s, a spindle speed (S) of 3000 rpm, Carbide drill material, and 6% reinforcement are the optimum process parameters for accomplishing maximum grey relational grade (GRG). Analysis of variance (ANOVA) reveals that drill material (29.08%) has the highest influence on GRG, followed by feed rate (24.24%) and spindle speed (19.52%). The interaction of feed rate and drill material has a minor impact on GRG; the variable reinforcement percentage and its interactions with all other variables were pooled up to the error term. The predicted GRG is 0.824, and the experimental value is 0.856. The predicted and experimental values match each other well. The error is 3.7%, which is very minimal. Mathematical models were also developed for all responses based on the drill bits used.

GRA & DFA Approach for Optimum Material Selection of Aluminium Metal Matrix Composites: A Comparative Study

“…Using grey relational analysis (GRA). Jebarose Juliyana, S et al (17) derived the optimal cutting parameters for addressing the best thrust force (TF), surface roughness (SR), and burr height (BH) of drilled holes for the composite LM5/ZrO2 . To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied by Layatitdev Das et.al, and the effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the machining responses (18) .…”

Optimization on the electrical discharge machining (EDM) process parameters of aged AA7075/TiC metal matrix composites

“…Conducted experiments on AISI D2 steel involved the incorporation of additives into the dielectric to analyze and enhance the surface properties of the steel, leading to improved surface finish during cutting (Kumar, 2014). According to the experimental findings and GRA, the best process parameters for achieving the maximum grey relational grade (GRG) are a a spindle speed of 3000 rpm, Carbide drill material, feed rate (F) of 50 m⋅s -1 , and 6% reinforcement (Jebarose et al, 2023).…”