Method for fault detection of aluminum oxide grinding wheel cutting surfaces using a piezoelectric diaphragm and digital signal processing techniques

Abstract

A novel strategy to quantify evenness and prevent faults on the cutting surface of conventional aluminum oxide grinding wheels during the dressing operation was proposed in this research work. The method is based on the use of low-cost piezoelectric diaphragms and new signal processing parameters based on time-frequency and ratio of power metric. Dressing tests were performed on two structurally distinct aluminum oxide grinding wheels. An acoustic emission (AE) sensor was used as a reference for comparative purposes. Subsequently, AE and piezoelectric diaphragm signals were collected and processed through the proposed approach. The results obtained for both grinding wheels by the piezoelectric diaphragm reveal a strong correlation with those obtained by the AE sensor. This indicates that the piezoelectric diaphragm was as efficient as the AE sensor and can be used to monitor the dressing operation of conventional grinding wheels using the proposed method, thus contributing to optimize the grinding process.