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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/1738/1/Artificial_Intelligent_Model_to_Predict_Surface_Roughness_in_Laser.pdf
http://umpir.ump.edu.my/id/eprint/1738/
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spelling my.ump.umpir.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,. .
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/1738/1/Artificial_Intelligent_Model_to_Predict_Surface_Roughness_in_Laser.pdf
http://umpir.ump.edu.my/id/eprint/1738/
_version_ 1643664451645210624
score 13.211869

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