Multi-Sensor Integration using Neural Networks for Predicting Quality Characteristics of End-Milled Parts-Part I: Individual Effects of Training Parameters


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.


Mechanical and Aerospace Engineering


National Science Foundation, Grant None

International Standard Serial Number (ISSN)

1996-756X; 0277-786X

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





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Publication Date

02 Mar 1994