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
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Online Access:http://umpir.ump.edu.my/id/eprint/1314/1/Prediction_of__Torque_in_Milling_by_Response_Surface_Method_and_Neural_Network.pdf
http://umpir.ump.edu.my/id/eprint/1314/
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Institution: Universiti Malaysia Pahang
Language: English
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spelling my.ump.umpir.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).
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
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/1314/1/Prediction_of__Torque_in_Milling_by_Response_Surface_Method_and_Neural_Network.pdf
http://umpir.ump.edu.my/id/eprint/1314/
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