Optimization of Surface Roughness in Drilling of GFRP Composite Using Harmony Search Algorithm

Abstract

Now-a-days the application of Glass Fiber reinforced polymer (GFRP) composite materials has increased a lot in the field of engineering. Afterward, the need for better surface finish of GFRP composite materials machining has increased greatly. In this paper a hybrid model of Harmony Search (HS) with Response Surface Methodology (RSM), has been developed for optimizing the surface roughness of three different GFRP composite materials during drilling operation. The machining parameters viz., cutting speed, feed rate, cutting tool point angle and lip clearance angle were optimized with the consideration surface roughness using HS algorithm. In addition, the optimized results using HS algorithm is compared with desirably analysis. Finally, the effect of different machining parameters on surface roughness are also studied. 

Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer

“…One of the methods to do that is called metaheuristic. There are some studies in optimization applying metaheuristic methods such as Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Response Surface Methodology (RSM), Harmony Search (HS), and others [8,[13][14][15][16][17][18][19].…”

Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

“…Rao and Pawar utilized optimization algorithms, namely artificial bee colony (ABC), particle swarm optimization (PSO) and simulated annealing (SA) to assess optimal cutting parameters with respect to production time (i.e., productivity) [4]. Other population-based optimization algorithms include the dragonfly algorithm and the Harmony Search algorithm that were also employed to study the relationship between cutting parameters and machining performances [5,6]. Pardo et al used artificial neural networks in conjunction with genetic algorithm (GA) to investigate cutting parameters resulting in optimum cutting insert and cutting parameters during turning operation [7].…”