Fault Detection in a Cold Forging Process through Feature Extraction with a Neural Network

B.F. Rolfe, Y. Frayman, P.D. Hodgson, G.I. Webb, and G.L. Kelly (Australia)


Lubrication Defects, Fasteners Manufacturing, Cold Forging, Neural Networks, Feature Extraction


This paper investigates the application of neural net works 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 mate rial 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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