Monitoring drill conditions with wavelet based encoding and neural networks

Abstract

Encoding of thrust force signals of microdrilling operations with wavelet transformations and classification of estimated coefficients with adaptive resonance theory (ART2)-type neural networks are proposed for detection of severe tool damage just before complete tip breakage occurs. The coefficients of the wavelets were classified both directly and after a secondary encoding to reduce the humber of inputs. Direct classification of the wavelets was found to be more reliable in the sixty-one cases studied. The proposed approach was also tested with two sampling intervals. Large sampling intervals were used to inspect complete drilling cycles. Smaller sampling intervals were used to focus on thrust force variations during the motion of the machine tool table when it is driven by a stepping motor. It was found that the data collected at smaller sampling intervals were easier to classify to detect severe damage to the tool.