Abstract
In order to assure precision and quality of boring operations, on-line tool condition monitoring is essential. In this research, Counterpropagation Neural Networks (CPN’s) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are used in conjunction with features extracted from 3-axis cutting force data for the on-line measurement and detection of tool wear for precision boring. Force data was collected for carbide inserts during the boring of titanium parts. At the end of each boring operation, the average flank wear width was measured to determine the cutting tool conditions. Measurements were accomplished with the aid of a toolmaker’s microscope. Fourteen features were extracted from the cutting force data. In order to determine which features showed the best indication of cutting tool conditions, a Sequential Forward Search (SFS) algorithm was utilized to reduce the number of features for on-line measurements and detection of precision boring of titanium parts. The selected two most prominent features were kurtosis of longitudinal force and average of the ratio between tangential force and radial force. On-line classification showed excellent results, using both a 2x30x1 CPN and a 2x2 ANFIS, of being able to predict tool conditions on-line with 100% accuracy. On-line measurements also produced exceedingly successful results with a minimum error for a 1x10 ANFIS of 0.87% and a minimum error for a 3x69x1 CPN of 8.46%.