Abstract
A non-contact vision-based method to detect the occurrence of fracture in ceramic cutting tool inserts using the workpiece profile signature is proposed. Machining experiments were carried out to turn stainless steel using ceramic cutting tool inserts. The images of the workpiece profile were captured after each turning pass using a high-resolution DSLR camera. The edge profiles of the workpiece were extracted to sub-pixel accuracy using the invariant moment method. The extracted profiles were transformed from the spatial domain to the frequency domain using Fast Fourier Transform (FFT). From the Fourier spectrum the amplitude of the fundamental feed frequency was observed to increase steadily with the cutting duration during gradual wear of the tool edge. However, significant fluctuations in the amplitude of the fundamental feed frequency were observed after the onset of chipping and fracture due to the continuous deterioration of the cutting edge. This was caused by the irregular peak-to-valley heights in the workpiece surface profile resulting from the fractured tool at the end of the cutting time of 84.8s.
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