NDLI: Feature signature prediction of a boring process using neural network modeling with confidence bounds

Content Provider	Springer Nature Link
Author	Yu, Gang Qiu, Hai Djurdjavic, Dragan Lee, Jay
Copyright Year	2005
Abstract	Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.
Starting Page	614
Ending Page	621
Page Count	8
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	30
Issue Number	7-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-11-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Prediction Neural networks Prediction confidence bounds Boring process Degradation Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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