Particle Swarm Optimisation Prediction Model for Surface Roughness

Acrylic sheet is a crystal clear (with transparency equal to optical glass), lightweight material having outstanding weather ability, high impact resistance, good chemical resistance, and excellent thermo-formability and machinability. This paper develops the artificial intelligent model using parti...

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Main Authors:	M. M., Noor, K., Kadirgama, M. M., Rahman
Format:	Article
Language:	English
Published:	Academic Journals 2011
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/2228/ http://umpir.ump.edu.my/id/eprint/2228/1/Particle_swarm_optimisation_prediction_model_for.pdf

id	ump-2228
recordtype	eprints
spelling	ump-22282018-01-25T04:05:19Z http://umpir.ump.edu.my/id/eprint/2228/ Particle Swarm Optimisation Prediction Model for Surface Roughness M. M., Noor K., Kadirgama M. M., Rahman TS Manufactures Acrylic sheet is a crystal clear (with transparency equal to optical glass), lightweight material having outstanding weather ability, high impact resistance, good chemical resistance, and excellent thermo-formability and machinability. This paper develops the artificial intelligent model using partial swarm optimization (PSO) to predict the optimum surface roughness when cutting acrylic sheets with laser beam cutting (LBC). Response surface method (RSM) was used to minimize the number of experiments. The effect of cutting speed, material thickness, gap of tip and power towards surface roughness were investigated. It was found that the surface roughness is significantly affected by the tip distance followed by the power requirement, cutting speed and material thickness. Surface roughness becomes larger when using low power, tip distance and material thickness. Combination of low cutting speed, high power, tip distance and material distance produce fine surface roughness. Some defects were found in microstructure such as burning, melting and wavy surface. The optimized parameters by PSO are cutting speed (2600 pulse/s), tip distance (9.70 mm), power (95%) and material thickness (9 mm) which produce roughness around 0.0129 µm. Academic Journals 2011-07-04 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2228/1/Particle_swarm_optimisation_prediction_model_for.pdf M. M., Noor and K., Kadirgama and M. M., Rahman (2011) Particle Swarm Optimisation Prediction Model for Surface Roughness. International Journal of Physical Sciences, 6 (13). pp. 3082-3090. ISSN 1992-1950
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures M. M., Noor K., Kadirgama M. M., Rahman Particle Swarm Optimisation Prediction Model for Surface Roughness
description	Acrylic sheet is a crystal clear (with transparency equal to optical glass), lightweight material having outstanding weather ability, high impact resistance, good chemical resistance, and excellent thermo-formability and machinability. This paper develops the artificial intelligent model using partial swarm optimization (PSO) to predict the optimum surface roughness when cutting acrylic sheets with laser beam cutting (LBC). Response surface method (RSM) was used to minimize the number of experiments. The effect of cutting speed, material thickness, gap of tip and power towards surface roughness were investigated. It was found that the surface roughness is significantly affected by the tip distance followed by the power requirement, cutting speed and material thickness. Surface roughness becomes larger when using low power, tip distance and material thickness. Combination of low cutting speed, high power, tip distance and material distance produce fine surface roughness. Some defects were found in microstructure such as burning, melting and wavy surface. The optimized parameters by PSO are cutting speed (2600 pulse/s), tip distance (9.70 mm), power (95%) and material thickness (9 mm) which produce roughness around 0.0129 µm.
format	Article
author	M. M., Noor K., Kadirgama M. M., Rahman
author_facet	M. M., Noor K., Kadirgama M. M., Rahman
author_sort	M. M., Noor
title	Particle Swarm Optimisation Prediction Model for Surface Roughness
title_short	Particle Swarm Optimisation Prediction Model for Surface Roughness
title_full	Particle Swarm Optimisation Prediction Model for Surface Roughness
title_fullStr	Particle Swarm Optimisation Prediction Model for Surface Roughness
title_full_unstemmed	Particle Swarm Optimisation Prediction Model for Surface Roughness
title_sort	particle swarm optimisation prediction model for surface roughness
publisher	Academic Journals
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/2228/ http://umpir.ump.edu.my/id/eprint/2228/1/Particle_swarm_optimisation_prediction_model_for.pdf
first_indexed	2023-09-18T21:55:50Z
last_indexed	2023-09-18T21:55:50Z
_version_	1777414072386453504

Particle Swarm Optimisation Prediction Model for Surface Roughness

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