Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network

In CNC milling process, proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artific...

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Main Authors:	M. F. F., Ab Rashid, Mohd Rizal, Abdul Lani
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/5278/ http://umpir.ump.edu.my/id/eprint/5278/1/WCE2010_pp2219-2224.pdf

id	ump-5278
recordtype	eprints
spelling	ump-52782015-03-03T09:24:02Z http://umpir.ump.edu.my/id/eprint/5278/ Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network M. F. F., Ab Rashid Mohd Rizal, Abdul Lani TS Manufactures In CNC milling process, proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5278/1/WCE2010_pp2219-2224.pdf M. F. F., Ab Rashid and Mohd Rizal, Abdul Lani (2010) Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network. In: Proceedings of the World Congress on Engineering 2010, 30 June - 2 July 2010 , London, UK. pp. 2219-2224..
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. F. F., Ab Rashid Mohd Rizal, Abdul Lani Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
description	In CNC milling process, proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction.
format	Conference or Workshop Item
author	M. F. F., Ab Rashid Mohd Rizal, Abdul Lani
author_facet	M. F. F., Ab Rashid Mohd Rizal, Abdul Lani
author_sort	M. F. F., Ab Rashid
title	Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
title_short	Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
title_full	Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
title_fullStr	Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
title_full_unstemmed	Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
title_sort	surface roughness prediction for cnc milling process using artificial neural network
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/5278/ http://umpir.ump.edu.my/id/eprint/5278/1/WCE2010_pp2219-2224.pdf
first_indexed	2023-09-18T22:00:32Z
last_indexed	2023-09-18T22:00:32Z
_version_	1777414368029310976

Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network

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