Intelligent tool wear identification based on optical scattering image andhybrid artificial intelligence techniques

Tool wear monitoring is crucial for an automated machining system to mainta in consistent quality of machined parts and prevent damage to the parts dur ing the machining operation. A vision-based approach is presented for tool wear identification in finish turning using an adaptive resonance theory (A RT2) neural network embedded with fuzzy classifiers. The proposed approach is established upon the fact that the optical scattering image of a turned surface is related to the wear of the cutting tool. By applying the techniq ue of the ART2 neural network embedded with fuzzy classifiers, the state of wear of the turning tool is determined from captured images obtained by la ser scattering from the machined surfaces of the workpiece. This approach i s not unlike the visual inspection of the surface of a machined workpiece t o determine the state of wear of a cutting tool by an expert machinist. How ever, experimental results indicate that the conventional technique of meas uring surface finish does not give values that correlate well with tool wea r. On the other hand, the laser scattering image provides a good indication of the tool wear as it is not readily affected by buildup edge or cold-wel ded material, scratches and other disruptive defects on the turned surface as the tool wears. In this paper, the theory on the laser scattering image and the principle of tool wear identification are described. Based on the s cattering images, the proposed approach can correctly identify the conditio n of 'significant wear' prior to the rapid tool wear stage for the cutting tool.