Condition Monitoring of Cutting Tools Using Artificial Neural Networks
The paper presents a methodology for using neural network techniques and simple data processing algorithms for monitoring the condition of milling cutters during peripheral milling . The learning algorithms considered in this research utilise artificial neural networks to map some machining parameters to sensory signals. Cutting force and acceleration signals recorded during machining are first simplified and then fed into the input layer of the neural network. Using the back-propagation method, the output of the neural network is used to recognise “normal” as well as “faulty” milling cutters and the depth of cut used. The experimental results show that the proposed approach of using simple data processing algorithms with neural networks is capable of successfully identifying common fault conditions in milling cutters in peripheral operations.
Keywordscondition monitoring artificial neural networks milling tool breakage pattern recognition
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