Fault detection in a cold forging process through feature extraction with a neural network Rolfe, Bernard, Frayman, Yakov, Hodgson, Peter, Webb, G.I. and Kelly, Georgina 2002, Fault detection in a cold forging process through feature extraction with a neural network, in Artificial intelligence and applications: proceedings of the second IASTED Conference, ACTA Press, New York, N.Y, pp. 155-159. Attached Files Name Description MIMEType Size Downloads Title Fault detection in a cold forging process through feature extraction with a neural network Author(s) Rolfe, Bernard orcid.org/0000-0001-8516-6170 Frayman, Yakov Hodgson, Peter orcid.org/0000-0002-6985-1319 Webb, G.I. Kelly, Georgina Conference name IASTED International Conference (2nd : 2002 : Malaga, Spain) Conference location Malaga, Spain Conference dates 9-12 September 2002 Title of proceedings Artificial intelligence and applications: proceedings of the second IASTED Conference Editor(s) Hamza, M.H. Publication date 2002 Start page 155 End page 159 Publisher ACTA Press Place of publication New York, N.Y Keyword(s) Engineering Applications Summary This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition. ISBN 0889863520 9780889863521 Language eng Field of Research 091099 Manufacturing Engineering not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2005, Monash University Persistent URL http://hdl.handle.net/10536/DRO/DU:30004827 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 18 Sep 2008, 12:25:52 EST Fri, 03 Apr 2009, 09:09:57 EST Fri, 17 Oct 2014, 08:34:27 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 1212 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 07 Jul 2008, 09:41:58 EST