Artificial Intelligent Model to Predict Surface Roughness in Laser Machining

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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,. .



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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Proceeding ISBN 978-983-3986-57-6 Prof. Dr. Md Mustafizur Rahman (M. M. Rahman) Prof. Dato’ Dr. Rosli Abu Bakar (R. A. Bakar) Dr. Mohd Ruzaimi Mat Rejab (M. R. M. Rejab) Dr. Kumaran Kadirgama (K. Kadirgama) Muhamad Mat Noor (M. M. Noor)
Uncontrolled Keywords:Laser beam cutting, Radian Basis Function Network (RBFN), surface roughness, Power requirement
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Faculty of Mechanical Engineering
ID Code:1738
Deposited By: Pn. Hazlinda Abd Rahman
Deposited On:25 Aug 2011 11:35
Last Modified:25 Jan 2018 13:58

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