Artificial Intelligent Model to Predict Surface Roughness in Laser Machining

Light Amplification Stimulation Emission of Radiation or the common name is Laser. The laser light differs from ordinary light due to it has the photons of same frequency, wavelength and phase. Advantages of using laser beam cutting (LBC) are materials with complex figures can easily be cut by i...

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Main Authors:	M. M., Noor, K., Kadirgama, M. R. M., Rejab, M. M., Rahman, R. A., Bakar
Format:	Conference or Workshop Item
Language:	English
Published:	2009
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1738/ http://umpir.ump.edu.my/id/eprint/1738/1/Artificial_Intelligent_Model_to_Predict_Surface_Roughness_in_Laser.pdf

id	oai:umpir.ump.edu.my:1738
recordtype	eprints
spelling	oai:umpir.ump.edu.my:17382018-01-25T05:58:29Z http://umpir.ump.edu.my/id/eprint/1738/ Artificial Intelligent Model to Predict Surface Roughness in Laser Machining M. M., Noor K., Kadirgama M. R. M., Rejab M. M., Rahman R. A., Bakar TJ Mechanical engineering and machinery Light Amplification Stimulation Emission of Radiation or the common name is Laser. The laser light differs from ordinary light due to it has the photons of same frequency, wavelength and phase. Advantages of using laser beam cutting (LBC) are materials with complex figures can easily be cut by incorporating computer numerical control (CNC) motion equipment, LBC has high cutting speed, Low distortion, very high edge quality and most important thing is LBC has a minimal heat affected zone (HAZ).This paper discussed the development of Radian Basis Function Network (RBFN) to predict surface roughness when laser cutting acrylic sheet. The main objectives of this paper are to find the optimum laser parameters (power, material thickness, tip distance and laser speed) and the effect of these parameters on surface roughness. The network was trained until it predict closer to the experimental values. It observed that some of good surface roughness specimen fail in terms of structure when investigate under microscope. 2009 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1738/1/Artificial_Intelligent_Model_to_Predict_Surface_Roughness_in_Laser.pdf M. M., Noor and K., Kadirgama and M. R. M., Rejab and M. M., Rahman and R. A., Bakar (2009) Artificial Intelligent Model to Predict Surface Roughness in Laser Machining. In: International Conference on Recent Advances in Materials, Minerals & Environment (RAMM’09), , 1-3 June 2009 , Bayview Beach Resort, Batu Ferringhi, Penang, Malaysia,. .
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery M. M., Noor K., Kadirgama M. R. M., Rejab M. M., Rahman R. A., Bakar Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
description	Light Amplification Stimulation Emission of Radiation or the common name is Laser. The laser light differs from ordinary light due to it has the photons of same frequency, wavelength and phase. Advantages of using laser beam cutting (LBC) are materials with complex figures can easily be cut by incorporating computer numerical control (CNC) motion equipment, LBC has high cutting speed, Low distortion, very high edge quality and most important thing is LBC has a minimal heat affected zone (HAZ).This paper discussed the development of Radian Basis Function Network (RBFN) to predict surface roughness when laser cutting acrylic sheet. The main objectives of this paper are to find the optimum laser parameters (power, material thickness, tip distance and laser speed) and the effect of these parameters on surface roughness. The network was trained until it predict closer to the experimental values. It observed that some of good surface roughness specimen fail in terms of structure when investigate under microscope.
format	Conference or Workshop Item
author	M. M., Noor K., Kadirgama M. R. M., Rejab M. M., Rahman R. A., Bakar
author_facet	M. M., Noor K., Kadirgama M. R. M., Rejab M. M., Rahman R. A., Bakar
author_sort	M. M., Noor
title	Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
title_short	Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
title_full	Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
title_fullStr	Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
title_full_unstemmed	Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
title_sort	artificial intelligent model to predict surface roughness in laser machining
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/1738/ http://umpir.ump.edu.my/id/eprint/1738/1/Artificial_Intelligent_Model_to_Predict_Surface_Roughness_in_Laser.pdf
first_indexed	2018-09-07T00:24:34Z
last_indexed	2018-09-07T00:24:34Z
_version_	1610906205526425600

Artificial Intelligent Model to Predict Surface Roughness in Laser Machining

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