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

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...

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Main Author: Zainal, Nurezayana
Format: Thesis
Language:English
Published: 2018
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Online Access:http://eprints.utm.my/id/eprint/96190/1/NurezayanaZainalPSC2018.pdf.pdf
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spelling my-utm-ep.961902022-07-04T08:31:38Z Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy 2018 Zainal, Nurezayana QA75 Electronic computers. Computer science 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. 2018 Thesis http://eprints.utm.my/id/eprint/96190/ http://eprints.utm.my/id/eprint/96190/1/NurezayanaZainalPSC2018.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:142137 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic QA75 Electronic computers
Computer science
spellingShingle QA75 Electronic computers
Computer science
Zainal, Nurezayana
Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
description 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.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Zainal, Nurezayana
author_facet Zainal, Nurezayana
author_sort Zainal, Nurezayana
title Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
title_short Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
title_full Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
title_fullStr Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
title_full_unstemmed Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
title_sort hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy
granting_institution Universiti Teknologi Malaysia
granting_department Faculty of Engineering - School of Computing
publishDate 2018
url http://eprints.utm.my/id/eprint/96190/1/NurezayanaZainalPSC2018.pdf.pdf
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