In-process prediction of corner wear in drilling operations

The paper presents an in-process prediction of corner wear in drilling operations by means of a polynomial network. The polynomial network is composed of a number of functional nodes and well organized to form an optimal network architecture using an algorithm for synthesis of polynomial networks (ASPNs). Thrust force or torque in drilling operations has been correlated with corner wear in this study. It has been shown that the thrust force is better than the torque as the sensing signal for the in-process prediction of corner wear. Experimental results have shown that the corner wear over a wide range of drilling conditions can be predicted with a reasonable accuracy if the cutting speed, feed rate, drill diameter, and thrust force are given.