A Mechatronics Approach for the On-line Assessment of Drill Wear

George Liu1, Tien-I Liu2 and Zhiyu Gao

1Department of Electrical Engineering, California State University, Long Beach, USA.

2Department of Mechanical Engineering, California State University, Sacramento, USA.


Using thrust and torque information to assess 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 (SFS) 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 using SFS is the peak of torque in the drilling process.

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. A1x6 ANFIS architecture with generalized bell membership function can achieve a success rate of 100% for the on-line assessment of drill states. This is extremely important for control of the drilling process. Furthermore, the assessment of drill wear is performed under different drilling conditions as compared with the training process. This shows that ANFIS has the capability of generalization.

Keywords: Feature selection, Neuro-fuzzy systems.

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