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Proceedings Paper

Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters
Author(s): Anthony Chukwujekwu Okafor; O. Adetona
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Paper Abstract

This paper presents a systematic evaluation of the individual effects of training parameters: learning rate, momentum rate, number of hidden layer nodes, and processing element's transfer function, on the performance of back propagation networks in predicting quality characteristics of end milled parts. Multi-sensor signatures (acoustic emission, spindle vibration, and cutting force components) acquired during circular end-milling of 4140 steel and the corresponding measured quality characteristics (surface roughness and bore tolerance) were used to train the networks. The network is part of a proposed Intelligent Machining Monitoring and Diagnostic System for Quality Assurance of Machined Parts. The network performances were evaluated using four different criteria: maximum error, RMS error, mean error and number of training cycles. One of the results obtained shows that hyperbolic tangent transfer function gave a better performance than the sigmoid and sine functions respectively. Optimum combinations of training parameters have been observed. The effects of various combinations of training parameters are presented.

Paper Details

Date Published: 2 March 1994
PDF: 13 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169958
Show Author Affiliations
Anthony Chukwujekwu Okafor, Univ. of Missouri/Rolla (United States)
O. Adetona, Univ. of Missouri/Rolla (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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