Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets

This paper presents the use of response surface method (RSM) and neural network to study surface roughness for laser beam cutting on acrylic sheets. Box-Behnken design based on response surface method and multilayer perceptions neural network were used to predict the effect of laser cutting paramete...

Full description

Bibliographic Details
Main Authors:	M. M., Noor, K., Kadirgama
Format:	Conference or Workshop Item
Language:	English
Published:	2009
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1410/ http://umpir.ump.edu.my/id/eprint/1410/1/2009_P_CIRP_M.M.Noor-Conference-.pdf

id	ump-1410
recordtype	eprints
spelling	ump-14102018-01-31T01:37:03Z http://umpir.ump.edu.my/id/eprint/1410/ Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets M. M., Noor K., Kadirgama TJ Mechanical engineering and machinery This paper presents the use of response surface method (RSM) and neural network to study surface roughness for laser beam cutting on acrylic sheets. Box-Behnken design based on response surface method and multilayer perceptions neural network were used to predict the effect of laser cutting parameters. These parameters include power requirement, cutting speed and tips distance on surface roughness during the machining of acrylic sheets. It is found out that the predictive models are able to predict the longitudinal component of the surface roughness close to those readings recorded experimentally with a 95% confident interval. The result obtained from the predictive model was also compared using multilayer perceptions with back–propagation learning rule artificial neural network. The first order equation revealed that power requirement was the dominant factor which was followed by tip distance, and cutting speed. The cutting parameter predicted by using neural network was in good agreement with that obtained by RSM. This observation indicates the potential of using response surface method in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting condition to enhance the surface roughness. 2009 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1410/1/2009_P_CIRP_M.M.Noor-Conference-.pdf M. M., Noor and K., Kadirgama (2009) Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets. In: 12th Cirp Conference On Modelling Of Machining Operations, 7-8 May 2009 , San Sebastian (Spain). . (Unpublished)
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 Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets
description	This paper presents the use of response surface method (RSM) and neural network to study surface roughness for laser beam cutting on acrylic sheets. Box-Behnken design based on response surface method and multilayer perceptions neural network were used to predict the effect of laser cutting parameters. These parameters include power requirement, cutting speed and tips distance on surface roughness during the machining of acrylic sheets. It is found out that the predictive models are able to predict the longitudinal component of the surface roughness close to those readings recorded experimentally with a 95% confident interval. The result obtained from the predictive model was also compared using multilayer perceptions with back–propagation learning rule artificial neural network. The first order equation revealed that power requirement was the dominant factor which was followed by tip distance, and cutting speed. The cutting parameter predicted by using neural network was in good agreement with that obtained by RSM. This observation indicates the potential of using response surface method in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting condition to enhance the surface roughness.
format	Conference or Workshop Item
author	M. M., Noor K., Kadirgama
author_facet	M. M., Noor K., Kadirgama
author_sort	M. M., Noor
title	Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets
title_short	Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets
title_full	Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets
title_fullStr	Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets
title_full_unstemmed	Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets
title_sort	response surface method and neural network to determine surface roughness for laser cutting on acrylic sheets
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/1410/ http://umpir.ump.edu.my/id/eprint/1410/1/2009_P_CIRP_M.M.Noor-Conference-.pdf
first_indexed	2023-09-18T21:54:31Z
last_indexed	2023-09-18T21:54:31Z
_version_	1777413990015565824

Response Surface Method and Neural Network to Determine Surface Roughness for Laser Cutting on Acrylic Sheets

Similar Items