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Fault detection in a cold forging process through feature extraction with a neural network
conference contribution
posted on 2002-01-01, 00:00 authored by Bernard RolfeBernard Rolfe, Yakov Frayman, Peter HodgsonPeter Hodgson, G Webb, Georgina KellyThis 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.
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Title of proceedings
Artificial intelligence and applications: proceedings of the second IASTED ConferenceEvent
IASTED International Conference (2nd : 2002 : Malaga, Spain)Pagination
155 - 159Publisher
ACTA PressLocation
Malaga, SpainPlace of publication
New York, N.YStart date
2002-09-09End date
2002-09-12ISBN-13
9780889863521ISBN-10
0889863520Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2005, Monash UniversityEditor/Contributor(s)
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