Multi-Sensor Integration using Neural Networks for Predicting Quality Characteristics of End-Milled Parts-Part I: Individual Effects of Training Parameters
Abstract
Artificial neural networks have been shown to have a lot of potential as a means of integrating multi-sensor signals for on-line real time monitoring of machining processes. However, a lot of questions still remain to be answered on how to optimize the training parameters during the training phase to optimize their subsequent performance, especially in view of the fact that the few published literature have made conificting recommendations. This paper presents a systematic evaluation of the individual effects of training parameters: learning rate, momentum rate, number of bidden 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.
Recommended Citation
A. C. 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," Proceedings of SPIE - The International Society for Optical Engineering, vol. 2243, pp. 119 - 131, Society of Photo-optical Instrumentation Engineers, Mar 1994.
The definitive version is available at https://doi.org/10.1117/12.169958
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
1996-756X; 0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
02 Mar 1994
Comments
National Science Foundation, Grant None