Tool Wear Monitoring by Means of Artificial Neural Networks (original) (raw)
The paper presents the application of multi layer perceptron artificial neural network for the tool wear monitoring in turning. To simulate factory floor conditions, six sets of cutting parameters were selected and applied in sequence. Six configurations of input parameter were tested to reveal their usability. Subsequently, the network's structure was optimised by means of an original pruning method, which makes possible an automatic network configuration. The obtained results prove the effectiveness of the studied BP neural networks for the purposes of tool wear monitoring.
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