A course in fuzzy systems and control
A course in fuzzy systems and control
IEEE Transactions on Neural Networks
Neural networks and statistical based models for surface roughness prediction
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
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Industrial CNC-milling centers demand adaptive control systems for better product quality. Surface roughness of machined parts is a key indicator of product quality, as it is closely related to functional features of parts such as fatigue life, friction, wear, etc. However, on-line control systems for surface roughness are not yet ready for industrial use. One of the main reasons is the absence of sensors that provide measurements reliably and effectively in a hostile machining environment. One potential solution is to combine readings from several different kinds of sensors in an intelligent sensor-fusion monitoring system. We implemented such a system and compared three modelling approaches for sensor-fusion: multiple regression, artificial neural networks (ANNs), and a new probabilistic approach. Probabilistic approaches are desirable because they can be extended beyond simple prediction to provide confidence estimates and diagnostic information as to probable causes. While our early experimental results with aluminum show that the ANN approach has the greatest predictive power over a variety of operating conditions, our probabilistic approach performs well enough to justify continued research given its many additional benefits.