On-line monitoring of boring tools for control of boring operations

In order to carry out precision and quality control of boring operations, on-line monitoring of boring tools is essential. Fourteen features were extracted by processing cutting force signals using virtual instrumentation. A Sequential Forward Search (SFS) algorithm was employed to select the best combination of features. Backpropagation neural networks (BPNs) and adaptive neuro-fuzzy inference systems (ANFIS) were used for on-line classification and measurement of tool wear. The input vectors consist of selected features. For the on-line classification, the outputs are boring tool conditions, which are either usable or worn out. For the on-line measurement, the outputs are estimated value of the tool wear. Using BPN, five features were needed for the on-line classification of boring tools. They are the average longitudinal force, average value of the ratio between the tangential and radial forces, skewness of the longitudinal force, skewness of the tangential force, and kurtosis of the longitudinal force. Three features, the average longitudinal force, average of the ratio between the tangential and radial forces, and kurtosis of the longitudinal force, were needed for on-line measurement of tool wear. Using ANFIS, three features were needed for the on-line classification of boring tools. They are the average longitudinal force, average of the ratio between the tangential and radial forces, and kurtosis of the longitudinal force. Only one feature, kurtosis of the longitudinal force, was needed for the on-line measurement of tool wear using ANFIS. Both 5×20×1 BPN and 3×5 ANFIS can achieve a 100% success rate for the on-line classification of boring tool conditions. Using a 3×20×1 BPN for neural computing, the minimum flank wear estimation error is 0.29% while the minimum flank wear estimation error is 2.04% using a 1×5 ANFIS.