Abstract
In today’s manufacturing industries several methods are available to monitor the tool conditions. This paper presents condition monitoring of face milling cutting tool with the help of Artificial Neural Network (ANN) based multilayer perceptron (MLP) approach. The vibration signals are acquired in good and faulty conditions of tool during machining of cast iron rectangular block. Statistical features have been extracted from these vibration signals. Additionally, features are selected with the help of decision tree and these features are given to classifier input. To classify the tool conditions, artificial neural network (ANN) based multilayer perceptron (MLP) classifier is used. The classification accuracy of MLP classifier is 97.33%. Result confirmed that MLP gives more classification accuracy. From these results it was found that combination of statistical feature extraction plus ANN based MLP are most suitable for supervision of face milling tool.
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