Performance analysis of GA, PSO and JA for determining the optimal parameters in friction drilling process

The friction drilling process, a technique for making sheet metal holes, differs from the conventional dril-ling process. Instead of cutting the workpiece, this process forms bush by using high heat generated at the contact region of the rotating conical tool and work material. The surface quality of the bush is not like a shiny and smooth surface due to the generation of high heat energy. Experimental investigation of fric-tion drilling process has been done for optimization of surface roughness and bushing length by various researchers. This research work attempts application of meta-heuristics such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Jaya Algorithm (JA) and compares the results in order to evaluate their performances. GA, PSO and JA are applied to minimize the surface roughness of the friction drilled holes which depends on parameters viz rotational speed, friction angle and workpiece thickness. Also, these algorithms are applied to maximize bushing length by considering the significant parameters viz friction angle, friction contact area ratio, feed rate and spindle speed. The results are compared for surface roughness value and bushing length signal to noise ratio. The comparative analysis shows that the Jaya algorithm performs more efficiently with quick convergence to the solution compared to other two algo-rithms. The comparison of PSO with GA shows that, the PSO performs better in terms of convergence to the solution for both the objectives. Further, it has been found that JA shows the robustness in terms of consistency of results for different population sizes and number of iterations.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).