Abstract
The demand on better returns by investors and the quest for continued customer satisfaction through assured quality products, have culminated in a rapid increase in factory automation processes within the last 20 years. This paper outlines an investigative study, where Artificial Neural Networks are applied to automatically detect the state of a cutting tool. The ensuing wear on the tool is chosen as the parameter to be detected, and process parameters from the cutting environment are measured on-line, fused through the network and its output gives an indication of tool wear level. The implemented Single and Multiple Layer Perceptron Neural Networks achieved success accuracy rates of > 75% and > 95% respectively.
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