Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters

Anthony Chukwujekwu Okafor; O. Adetona

doi:10.1117/12.169958

2 March 1994 Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters

Anthony Chukwujekwu Okafor, O. Adetona

Author Affiliations +

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169958
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Anthony Chukwujekwu Okafor and O. Adetona "Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169958

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