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.

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Title Fault detection in a cold forging process through feature extraction with a neural network
Author(s) Rolfe, BernardORCID iD for Rolfe, Bernard orcid.org/0000-0001-8516-6170
Frayman, Yakov
Hodgson, Peter
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

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