Taguchi Grey Relational Analysis for Multi-Response Optimization of Wear in Co-Continuous Composite

Abstract

Co-continuous composites have potential in friction and braking applications due to their unique tribological characteristics. The present study involves Taguchi grey relational analysis-based optimization of wear parameters such as applied load, sliding speed and sliding distance, and their effect on dry sliding wear performance of AA6063/SiC co-continuous composite manufactured by gravity infiltration. A Taguchi L9 orthogonal array was designed and nine experimental runs were performed based on the designed experiments. The coefficient of wear and specific wear rate were recorded for each experiment. Based on the average responses computed from Taguchi grey relational analysis, an applied load of 60 N, sliding speed of 1 m/s and sliding distance of 1000 m were estimated to be the optimal parameters. An Analysis of Variance (ANOVA) was conducted to identify the predominant factor and established all the three factors as being significant. The sliding distance was found to have the highest significant influence of 61.05% on the wear of the C4 composite. Confirmation experiments conducted using the optimal parameters indicated an improvement of 35.25% in grey relational grade. Analysis of the worn surfaces of the confirmation experiment revealed adhesive and abrasive wear as the governing mechanisms.

Experimental Investigation of Friction Stir Welding on 6061-T6 Aluminum Alloy using Taguchi-Based GRA

“…On the other hand, the signal-to-noise-ratio is used to measure process robustness and to evaluate deviation from desired values based on the selected quality characteristics. The signal-to-noise-ratio quality characteristics are categorized into three main groups: larger is better, nominal is best, and smaller is better [34][35][36][37]. In this research, used larger is better quality characteristics for both hardness and tensile responses.…”

“…The Taguchi trial method is suitable to govern the optimal settings of process parameters for a solitary or mono-objective characteristic. Contingent upon two or more responses, Grey Relational Analysis method (GRA) being Taguchi-based is preferable [37]. GRA is one of multiple response optimization tools used to conduct a relational analysis of the uncertainty of a system model and solving sophisticated interconnection among multi-objective responses [39,40].…”

A Management and Environmental Performance Evaluation of China’s Family Farms Using an Ultimate Comprehensive Cross-Efficiency Model (UCCE)

“…Gray relational analysis is one of the most widely used models of gray system theory that has been applied in various fields of engineering and management [46][47][48]. The agricultural production system is actually a gray system with incomplete information.…”

Optimal laser welding process parameters and expected weld bead profile for P92 steel

“…Taguchi method is adequate to identify the optimum process parameters for a single response characteristic. In the case of multiple responses having dissimilar quality characteristics, multi-objective optimization using grey relational analysis (GRA) is being utilized extensively [40][41][42][43][44][45]. There is a possibility to represent functionally the dissimilar quality characteristics of multiple responses into a single response characteristic after non-dimensioning them.…”

“…Industries expect simple, reliable and easy to implement procedures for solving optimization problems. Several researchers [46] are on use of many other algorithms (such as grey relational analysis (GRA) [40][41][42][43][44][45], genetic algorithm (GA) [47,48], teacher learning base algorithm (TLBA) [49], response surface methodology (RSM) [50], particle swarm optimization (PSO) [51], etc.) without highlighting drawbacks in existing theories.…”