Online detection and measurements of drill wear for the drilling of stainless steel parts

Abstract

Thrust and torque have been selected in this research for online detection and measurements of drill wear for the drilling of stainless steel parts because cutting forces are closely related to the drilling process and give the best indirect indication of drill conditions. Using thrust and torque information to monitor the drill conditions for control of the drilling process can decrease the operation cost and enhance the product quality. To find the most important feature(s), the feature selection technique is used in this research. Sequential forward search algorithm is used for feature selection. To reduce the dimension of the measurement vector, it is necessary to retain only those components of the extracted features which show a high sensitivity to drill wear and low sensitivity to process parameters. The best feature selected is the peak of torque in the drilling process. Adaptive neuro-fuzzy inference system (ANFIS) is a neuro-fuzzy system. It includes input layer, output layer, and layers between them. ANFIS can construct fuzzy rules with membership functions to generate an input–output pair. A 1 × 6 ANFIS architecture with generalized bell membership function can achieve a success rate of 100 % for the online detection of drill states. A 1 × 6 ANFIS architecture with product of sigmoid membership functions can measure the drill wear online with an error as low as 0.15 %. Furthermore, the detection and measurement of drill wear is performed under different drilling conditions as compared with the training process. This shows that ANFIS has the capability of generalization.