Multi-objective Optimization of Welding Parameters in MMAW for Nano-structured Hardfacing Material Using GRA Coupled with PCA

Abstract

Hardfacing is one of the most productive and practical approaches to cut down operating expense on the maintenance front and at the same time to improve performance and reliability of the equipment. Presence of nano-particles in hard facing materials significantly enhances surface area to volume ratio and accordingly it improves conductivity, hardness, heat and wear resistant properties. The main objective of this paper is to efficiently apply manual metal arc welding process for hardfacing of nano-structure based electrode. The most important process variables that have been considered in conducting the experiments are welding current, arc voltage and welding speed; while the response parameters include weld bead width, reinforcement and bead hardness, respectively. Taguchi’s (L25) orthogonal array has been used to perform the experimental runs. A combination of grey relational analysis coupled with principal component analysis has been applied to identify optimal settings of the input process parameters. Moreover, the exact input and output welding parameters have been examined with the help of genetic algorithm. Finally, confirmation test has also been carried out with the optimal welding process parameters to validate the experiment result.