• 한국정밀공학회지
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• 제15권4호
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• pp.105-110
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• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• 한국정밀공학회지
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• 제14권10호
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• pp.135-140
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• 1997

The detection of cutting tool states in machining is important for the automation. The information of cutting tool states in metal cutting process is uncertain. Hence a industry needs the system which can detect the cutting tool states in real time and control the feed motion. Cutting signal features must be sifted before the classification. In this paper the Fisher's linear discriminant function was applied to the pattern recognition of the cutting tool states successfully. Cutting conditions and cutting force para- meters have shown to be sensitive to tool states, so these cutting conditions and cutting force paramenters can be used as features for tool state detection.