Predicting Quality Characteristics of End-Milled Parts based on Multi-Sensor Integration using Neural Networks: Individual Effects of Learning Parameters and Rules

Abstract

Artificial neural networks have been shown to have a lot of potential as a means of integrating multi-sensor signals for real-time monitoring of machining processes. However, many 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 articles have made conflicting recommendations. This paper presents a systematic evaluation of the individual effects of training parameters - learning rate, momentum rate, number of hidden layer nodes, transfer function and learning rule-on the performance of back-propagation networks used for predicting quality characteristics of end-milled parts. Multi-sensor signatures (acoustic emission, spindle vibration, cutting force components and machining time) 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 the hyperbolic tangent transfer function gives 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. © 1995 Chapman & Hall.

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

machine learning; Neural networks; quality engineering; sensor integration

International Standard Serial Number (ISSN)

1572-8145; 0956-5515

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Dec 1995

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