Zainal, N. and Zain, A. M. and Radzi, N. H. M. and Othman, M. R. (2016) Glowworm swarm optimization (GSO) for optimization of machining parameters. Journal of Intelligent Manufacturing, 27 (4). pp. 797-804. ISSN 0956-5515
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Abstract
This study proposes glowworm swarm optimization (GSO) algorithm to estimate an improved value of machining performance measurement. GSO is a recent nature-inspired optimization algorithm that simulates the behavior of the lighting worms. To the best our knowledge, GSO algorithm has not yet been used for optimization practice particularly in machining process. Three cutting parameters of end milling that influence the machining performance measurement, minimum surface roughness, are cutting speed, feed rate and depth of cut. Taguchi method is performed for experimental design. The analysis of variance is applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. GSO has improved machining process by estimating a much lower value of minimum surface roughness compared to the results of experimental and particle swarm optimization.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Algorithms, Cutting, Machining, Machining centers, Particle swarm optimization (PSO), Surface roughness, Taguchi methods, Cutting parameters, Glowworm swarm optimizations, Investigate effects, Machining parameters, Machining performance, Machining Process, Minimum surface roughness, Optimization algorithms, Optimization |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computing |
| ID Code: | 72216 |
| Deposited By: | Fazli Masari |
| Deposited On: | 16 Nov 2017 05:16 |
| Last Modified: | 16 Nov 2017 05:16 |
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