Fault diagnosis of single point cutting tool using spectrum, cepstrum and wavelet analysis

Abstract

Tool fault diagnosis is essential in modern machining process to commemorate automation and precise manufacturing with limited human intervention. Automation increases productivity and efficient job handling ability. Online tool condition monitoring enables the fault diagnosis of cutting tool. A sensor is employed to acquire the information of the tool condition. The sensor data will be in raw form, which need to be processed using signal processing technique to derive the useful information about the tool fault. In the present work, a single point cutting tool of carbide tip used to machine oil hardened nickel steel. Various tool conditions are considered namely healthy, extended overhang, worn flank and broken tool. Vibration signals corresponding to each tool conditions are acquired using accelerometer to monitor tool condition. The time domain signals are transformed to frequency domain by employing fast Fourier transform (FFT). Other signal processing techniques such as cepstrum analysis and wavelet analysis used to understand the ailment of tool. The study also addresses limitations of signal processing tools and the advantage of one over the other.