Optimisation of welding processes using quasi-oppositional-based Jaya algorithm

Abstract

Welding processes are characterised as a multi-input processes. Selection of optimum combination of process parameters of a welding process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to improve the performance of submerged arc welding (SAW) process, electron beam welding (EBW) process, friction stir welding (FSW) process and gas tungsten arc welding (GTAW) process through parameter optimisation using a variant of Jaya algorithm, which is fast, robust and convenient. This variant is named as ‘Quasi-oppositional-based Jaya algorithm’ (QO-Jaya). The optimisation case studies for each of the above-mentioned welding processes are considered and the results obtained by the QO-Jaya algorithm are compared with the results obtained by well-known optimisation algorithms such as genetic algorithm (GA), simulated annealing (SA), teaching-learning-based optimisation (TLBO) and basic Jaya algorithm.

Keywords:

• Jaya algorithm
• oppositional-based learning
• welding processes
• optimisation