Abstract
This paper presents recent investigation of acoustic emission (AE) behaviours in grinding processes. It demonstrated the acoustic emission features characterized in time and frequency domain are influenced by thermal behaviours of materials. By control laser conditions, the temperature elevation under laser irradiation can be similar to that in a grinding process. Therefore, an innovative concept that grinding process can be monitored by using thermal AE signatures from laser irradiation tests has been proposed. Accordingly, an artificial neural network (ANN), built on laser irradiation tests, was applied to monitor grinding thermal performance. 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.