On-line tool wear estimation in CNC turning operations

In order to prevent tool breakage and resultant decrease in productivity in unmanned turning operations, many researchers have attempted to develop tool wear estimation and classification models. These include neural network models, fuzzy logic models and working scenario for quantitative models. The worn tools need to be replaced before their wear exceeds the allowed limits. Normally, cutting forces, AErms and cutting conditions including cutting speed, feed rate, rake angle and depth of cut are employed as inputs in these models. In the recent past, however, many researches have focused on flank wear prediction and off-line tool wear prediction systems. Additionally, the accuracy of tool wear prediction for these models needs to be increased. Therefore, in this research, a new on-line tool wear estimation system having higher accuracy for estimating the length of flank wear and the maximum depth of crater wear in CNC turning operations is developed.