Prediction of Torque in Milling by Response Surface Method and Neural Network

The present paper discusses the development of the ﬁrst-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The ﬁrst-order equation was developed using response surface method (RSM). The input cutting parameters wer...

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Main Author:	K., Kadirgama
Format:	Article
Language:	English
Published:	2008
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1314/ http://umpir.ump.edu.my/id/eprint/1314/1/Prediction_of__Torque_in_Milling_by_Response_Surface_Method_and_Neural_Network.pdf

id	ump-1314
recordtype	eprints
spelling	ump-13142018-01-31T02:08:50Z http://umpir.ump.edu.my/id/eprint/1314/ Prediction of Torque in Milling by Response Surface Method and Neural Network K., Kadirgama TJ Mechanical engineering and machinery The present paper discusses the development of the ﬁrst-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The ﬁrst-order equation was developed using response surface method (RSM). The input cutting parameters were the cutting speed, feed rate, radial depth and axial depth of cut. The study found that the predictive model was able to predict torque values close to those readings recorded experimentally with a 95% conﬁdent interval. The results obtained from the predictive model were also compared by using multilayer perceptron with back-propagation learning rule artiﬁcial neural network. The ﬁrst-order equation revealed that the feed rate was the most dominant factor which was followed by axial depth, radial depth and cutting speed. The cutting torque value predicted by using Neural Network was in good agreement with that obtained by RSM. This observation indicates the potential use of RSM in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting conditions in terms of torque. 2008 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1314/1/Prediction_of__Torque_in_Milling_by_Response_Surface_Method_and_Neural_Network.pdf K., Kadirgama (2008) Prediction of Torque in Milling by Response Surface Method and Neural Network. International Journal of Modelling and Simulation, 28 (4).
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 K., Kadirgama Prediction of Torque in Milling by Response Surface Method and Neural Network
description	The present paper discusses the development of the ﬁrst-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The ﬁrst-order equation was developed using response surface method (RSM). The input cutting parameters were the cutting speed, feed rate, radial depth and axial depth of cut. The study found that the predictive model was able to predict torque values close to those readings recorded experimentally with a 95% conﬁdent interval. The results obtained from the predictive model were also compared by using multilayer perceptron with back-propagation learning rule artiﬁcial neural network. The ﬁrst-order equation revealed that the feed rate was the most dominant factor which was followed by axial depth, radial depth and cutting speed. The cutting torque value predicted by using Neural Network was in good agreement with that obtained by RSM. This observation indicates the potential use of RSM in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting conditions in terms of torque.
format	Article
author	K., Kadirgama
author_facet	K., Kadirgama
author_sort	K., Kadirgama
title	Prediction of Torque in Milling by Response Surface Method and Neural Network
title_short	Prediction of Torque in Milling by Response Surface Method and Neural Network
title_full	Prediction of Torque in Milling by Response Surface Method and Neural Network
title_fullStr	Prediction of Torque in Milling by Response Surface Method and Neural Network
title_full_unstemmed	Prediction of Torque in Milling by Response Surface Method and Neural Network
title_sort	prediction of torque in milling by response surface method and neural network
publishDate	2008
url	http://umpir.ump.edu.my/id/eprint/1314/ http://umpir.ump.edu.my/id/eprint/1314/1/Prediction_of__Torque_in_Milling_by_Response_Surface_Method_and_Neural_Network.pdf
first_indexed	2023-09-18T21:54:20Z
last_indexed	2023-09-18T21:54:20Z
_version_	1777413978538901504

Prediction of Torque in Milling by Response Surface Method and Neural Network

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