Process-Monitoring Using Part Shape-Scales with Neural Networks: A Circular-Component Case

Existing approaches for conducting a control task for components machining generally include three methods: dimensional measurement, tolerance verification and equipment monitoring. However, spatial parameters from direct measurement present limited information about geometric features. Thus, many p...

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Bibliographic Details
Published in:International Journal of Applied Science and Engineering 2003-03, Vol.1 (1), p.030-044
Main Authors: Kuang-Han Hsieh, C. Alec Chang
Format: Article
Language:Chinese
Subjects:
error pattern identification
feedforward neural networks
Fourier descriptor
process monitoring
Scopus
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Summary:Existing approaches for conducting a control task for components machining generally include three methods: dimensional measurement, tolerance verification and equipment monitoring. However, spatial parameters from direct measurement present limited information about geometric features. Thus, many process monitoring systems must rely on acoustic information, torque and force sensors, vibration sensors, or an analysis of collected chips from a production process. These sensors can only detect problems caused by abnormal contact conditions between process tools and workpieces but not geometric deformations of industrial components produced in normal machining.＜BR＞Frequency parameters that directly utilize coordinate data from an object can identify more detailed geometric features for the purpose of industrial process monitoring. Accordingly, many more process anomalies about manufacturing facilities can be revealed using neural networks to map geometric anomalies. This paper develops shape-scales extracted fro
ISSN:1727-2394