Tool condition monitoring using laser scatter pattern

Abstract

Tool condition monitoring is crucial to the efficient operation of any machining processes where the cutting tool is in constant or intermittent contact with the workpiece material and is subject to continuous wear. It is an important function for unattended operation of CNC machine tools and satisfactory operation of flexible machining cells. Most tool condition monitoring techniques employ force, spindle motor torque, current or power, and acoustic emission signals, or a combination of these signals, during machining. There are also commercially available CNC systems that provide optional tool condition monitoring features using signals from the motor current or power. Most of these systems are suitable for machining in the roughing range. This paper presents an optical method using the scatter pattern of reflected laser light for the monitoring of tool condition in the roughing to near-finishing range. The scatter pattern is formed by a low-power laser beam that is reflected from the surface of the workpiece. It is captured with a digital camera and processed using suitable image processing techniques. Two vision-based optical parameters are used to characterize the scatter pattern: the mean and standard deviation of the intensity distribution in the region of interest (ROI) captured by the digital camera. The initial attempt at monitoring the tool condition is to first correlate the surface finish of the workpiece with the optical parameters and then correlate the surface finish with the tool condition. However, from the analysis of the intensity distribution of the scatter patterns, surface finish and tool wear results, it has been found that the surface finish of the machined components does not have consistent correlations with both the optical parameters nor tool wear. On the other hand, there is quite good correlation between the standard deviation parameter and tool wear. The above results and the possible approach to use the scatter pattern for tool condition monitoring are discussed.