Strategies for intelligent drill wear prediction using multiple sensor signals

Abstract

In this work, strategies for artificial intelligence (AI)-based drill flank wear prediction systems have been presented. Four AI-based models, namely, back propagation neural network (BPNN), radial basis function network (RBF), normalised radial basis function network (NRBF) and fuzzy radial basis function network (FRBF) have been used. Signals have been acquired from dynamometer, current sensor and accelerometer during drilling under different cutting conditions. Wear sensitive features in time-domain and frequency domain have been extracted. Different strategies, i.e., various combinations of the selected features and process parameters used as inputs to the AI models, are formulated. For most of the strategies, BPNN model gives better wear prediction results followed by NRBF model. But for noisy features such as features from vibration signal can be better related to drill wear using FRBF model.