APPLICATION OF ABDUCTIVE POLYNOMIAL NETWORK AND GREY THEORY TO DRILL FLANK WEAR PREDICTION

An abductive polynomial network for drill flank wear prediction was established, in which grey relational analysis was incorporated to explore the effect of various drilling parameters on flank wear. An abductive polynomial network usually includes multiple layers, each of which contains different polynomial functional nodes. It can automatically synthesize the optimal network structure, including the optimal number of layers and the optimal form of functional nodes. The correlation between the drilling input parameters, including the average thrust force, torque, cutting speed, feed and drill diameter, and drill flank wear can be achieved through this network model.

Based on experimental data, the developed network of this paper attained better accuracy in predicting drill flank wear, given the CPM of 0.1. The findings prove that the network is feasible and accurate in predicting flank wear.

In addition, grey relational analysis was used in this paper to investigate the effect of the aforementioned five drilling parameters on flank wear. According to the analytical results, the most influential factor on flank wear is drill diameter, followed by the average thrust force.