Application of image and sound analysis techniques to monitor the condition of cutting tools
Tool wear dramatically affects the texture of the machined surface and the sound generated by the cutting process. This paper discusses our work on texture analysis of machined surfaces and signal processing of sound generated by machining process and investigates the correlation between tool wear and quantities characterizing machined surfaces and sound pattern. Our results clearly indicate that tool condition monitoring which is defined as the ability to distinguish between a sharp, a semi-dull, or a dull tool can be successfully accomplished by combining sensory data from a CCD camera (image analysis) and a microphone (sound analysis).
tool condition monitoring surface texture analysis wavelets neural networks run length statistics automated visual inspection