A Model Approach for In-Process Tool Condition Monitoring in CNC Turning Using Machine Vision

Tool wear monitoring and real-time predicting tool life during the machining process is becoming a crucial element in modern manufacturing to properly determine the ideal point to replace tool, remains a challenge currently. In this paper, the model approach for in-process monitoring and predicting progressive tool wear by using machine vision is proposed. The developed method adopts machine vision to acquire tool wear images from a CCD camera. The emerged wear analysis is conducted based on the in-progress of signal processing on captured tool wear images, received throughout the cutting process. This automated analysis is carried out with programming to assess and compare a number of pixels of cutting edge images between cutting tools before machining and during the machining process. The developed system is evaluated through experiments of actual cutting conducted on the CNC turning machine with the proposed system installed to evaluate progressive wear during the machining process. Experimental results are capable of indicating the emerged wear at the current state by comparing the number of pixels between the new and used tools. Average flank wear (VB) is also evaluated linked to tool rejection criteria. The developed system is validated by the 3D microscope measuring actual wear on the used tool after cutting experiments. Comparative wear analysis is then performed by finding the correlation equation of pixels examined by a developed system and SMr2 value measured by the microscope. The results showed that the relationship between the number of pixels and SMr2 is a strong correlation.