Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

Benardos, PG; Vosniakos, GC

dc.contributor.author	Benardos, PG	en
dc.contributor.author	Vosniakos, GC	en
dc.date.accessioned	2014-03-01T01:18:14Z
dc.date.available	2014-03-01T01:18:14Z
dc.date.issued	2002	en
dc.identifier.issn	0736-5845	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14882
dc.subject	Design of experiments	en
dc.subject	Face milling	en
dc.subject	Neural networks	en
dc.subject	Surface roughness	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.classification	Robotics	en
dc.subject.other	Cutting tools	en
dc.subject.other	Neural networks	en
dc.subject.other	Surface roughness	en
dc.subject.other	Wear of materials	en
dc.subject.other	Face milling	en
dc.subject.other	Milling (machining)	en
dc.title	Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0736-5845(02)00005-4	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0736-5845(02)00005-4	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	In this paper, a neural network modeling approach is presented for the prediction of surface roughness (R-a) in CNC face milling. The data used for the training and checking of the networks' performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method. The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force. Using feedforward artificial neural networks (ANNs) trained with the Levenberg-Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5 x 3 x 1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 1.86% and to be consistent throughout the entire range of values. (C) 2002 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Robotics and Computer-Integrated Manufacturing	en
dc.identifier.doi	10.1016/S0736-5845(02)00005-4	en
dc.identifier.isi	ISI:000179721700002	en
dc.identifier.volume	18	en
dc.identifier.issue	5-6	en
dc.identifier.spage	343	en
dc.identifier.epage	354	en