Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy

Zainal, Nurezayana

Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy

dc.contributor.author	Zainal, Nurezayana
dc.date.accessioned	2024-02-18T04:12:45Z
dc.date.available	2024-02-18T04:12:45Z
dc.date.issued	2018
dc.description	Thesis (PhD. (Computer Science))
dc.description.abstract	Electrical discharge machining (EDM) is a non-traditional machining process widely used to machine geometrically complex and hard materials. In EDM, selection of optimal EDM parameters is important to have high quality products and increase productivity. However, one of the major issues is to obtain better machining performance at optimal value of these machining parameters. Modelling and optimization of EDM parameters have been considered to identify optimal EDM parameters that would lead to better EDM performance. Due to the complexity and uncertainty of the machining process, computational approaches have been implemented to solve the EDM problem. Thus, this study conducted a comprehensive investigation concerning the influence of EDM parameters on material removal rate (MRR), surface roughness (Ra) and dimensional accuracy (DA) through an experimental design. The experiment was performed based on full factorial design of experiment (DOE) with added center points of pulse on time (Ton), pulse off time (Toff), peak current (Ip) and servo voltage (Sv). In the EDM optimization, glowworm swarm optimization (GSO) algorithm was implemented. However, previous works indicated that GSO algorithm has always been trapped in the local optima solution and is slow in convergence. Therefore, this study developed a new hybrid artificial fish and glowworm swarm optimization (AF-GSO) algorithm to overcome the weaknesses of GSO algorithm in order to have a better EDM performance. For the modeling process, four types of regression models, namely multiple linear regression (MLR), two factor interaction (2FI), multiple polynomial regression (MPR) and stepwise regression (SR) were developed. These regression models were implemented in the optimization process as an objective function equation. Analysis of the optimization proved that AF-GSO algorithm has successfully outperformed the standard GSO algorithm. 2FI model of AF-GSO optimization for MRR and DA gave optimal solutions of 0.0042g/min and 0.00129%, respectively. On the other hand, the SR model for Ra of AF-GSO optimization gave the optimal solution of 1.8216p,s. Overall, it can be concluded that AF-GSO algorithm has successfully improved the quality and productivity of the EDM problems.
dc.description.sponsorship	Faculty of Engineering - School of Computing
dc.identifier.uri	http://openscience.utm.my/handle/123456789/1003
dc.language.iso	en
dc.publisher	Universiti Teknologi Malaysia
dc.subject	Electric metal-cutting
dc.subject	Machining—Materials
dc.subject	Machinery—Research
dc.title	Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
dc.type	Thesis
dc.type	Dataset