Development of machinability data model for end milling using artificial neural networks

Machinability data is a crucial factor affecting manufacturing cost and quality. Two artificial neural network machinability data models have been developed for the recommendation of proper cutting speed and feed rate for the peripheral end milling process. The first model is for single tool of high...

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Main Author:	Chu, Bee Wang
Format:	Thesis
Language:	English
Published:	2009
Subjects:	Information storage and retrieval systems - Mechanical engineering Neural networks (Computer science) Artificial intelligence
Online Access:	http://psasir.upm.edu.my/id/eprint/51547/1/FK%202009%20115RR.pdf
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id	my-upm-ir.51547
record_format	uketd_dc
spelling	my-upm-ir.515472017-03-30T03:19:51Z Development of machinability data model for end milling using artificial neural networks 2009-06 Chu, Bee Wang Machinability data is a crucial factor affecting manufacturing cost and quality. Two artificial neural network machinability data models have been developed for the recommendation of proper cutting speed and feed rate for the peripheral end milling process. The first model is for single tool of high speeds steel with inputs of material hardness, cutter diameter and ration of radial depth of cut to cutter radius. An identical model is developed with an additional input of cutter tool type has shown to be are able give appropriate recommendation of cutting speed and feed rate. The models were trained and tested with data from the most general and widely used Machining Data Handbook by Metcut and Associates. Model A and B results in the best least MSE of 4.91 x 10-5 and 1.61 x 10-4 respectively, after being trained for 3 x 10-8 iterations. The development aspects of the models, the mapping ability of hyperbolic tangent functions in perspective of summation neurons used to develop the neural network model are discussed. The minimum number of hidden neurons needed for mapping stepped pattern using hyperbolic tangent function was analysed. Two hidden layer networks are able to represent the nonlinearity of the machinability data to be modelled. The evaluation of the network is enhanced with the inclusion of standard deviation. Information storage and retrieval systems - Mechanical engineering Neural networks (Computer science) Artificial intelligence 2009-06 Thesis http://psasir.upm.edu.my/id/eprint/51547/ http://psasir.upm.edu.my/id/eprint/51547/1/FK%202009%20115RR.pdf application/pdf en public masters Universiti Putra Malaysia Information storage and retrieval systems - Mechanical engineering Neural networks (Computer science) Artificial intelligence
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Information storage and retrieval systems - Mechanical engineering Neural networks (Computer science) Artificial intelligence
spellingShingle	Information storage and retrieval systems - Mechanical engineering Neural networks (Computer science) Artificial intelligence Chu, Bee Wang Development of machinability data model for end milling using artificial neural networks
description	Machinability data is a crucial factor affecting manufacturing cost and quality. Two artificial neural network machinability data models have been developed for the recommendation of proper cutting speed and feed rate for the peripheral end milling process. The first model is for single tool of high speeds steel with inputs of material hardness, cutter diameter and ration of radial depth of cut to cutter radius. An identical model is developed with an additional input of cutter tool type has shown to be are able give appropriate recommendation of cutting speed and feed rate. The models were trained and tested with data from the most general and widely used Machining Data Handbook by Metcut and Associates. Model A and B results in the best least MSE of 4.91 x 10-5 and 1.61 x 10-4 respectively, after being trained for 3 x 10-8 iterations. The development aspects of the models, the mapping ability of hyperbolic tangent functions in perspective of summation neurons used to develop the neural network model are discussed. The minimum number of hidden neurons needed for mapping stepped pattern using hyperbolic tangent function was analysed. Two hidden layer networks are able to represent the nonlinearity of the machinability data to be modelled. The evaluation of the network is enhanced with the inclusion of standard deviation.
format	Thesis
qualification_level	Master's degree
author	Chu, Bee Wang
author_facet	Chu, Bee Wang
author_sort	Chu, Bee Wang
title	Development of machinability data model for end milling using artificial neural networks
title_short	Development of machinability data model for end milling using artificial neural networks
title_full	Development of machinability data model for end milling using artificial neural networks
title_fullStr	Development of machinability data model for end milling using artificial neural networks
title_full_unstemmed	Development of machinability data model for end milling using artificial neural networks
title_sort	development of machinability data model for end milling using artificial neural networks
granting_institution	Universiti Putra Malaysia
publishDate	2009
url	http://psasir.upm.edu.my/id/eprint/51547/1/FK%202009%20115RR.pdf
_version_	1747812062040424448

Development of machinability data model for end milling using artificial neural networks

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