Abstract
This paper presents recent development in acoustic emission (AE) technique for grinding process monitoring. It demonstrated the similarity of thermal acoustic emission feature existing in grinding processes and laser irradiation tests. An innovative concept that grinding process can be monitored by using thermal AE signatures from laser irradiation tests has been proposed. Based on such idea, an artificial neural network (ANN) was built and the results showed that grinding performance variation due to wheel wear can be identified by using the ANN. This development could bring great benefits by reducing experimental works in the preparation of an ANN for grinding monitoring.
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