INSPECTION OF MICRO-TOOLS AT HIGH ROTATIONAL SPEEDS

Micro-tools have been widely used in industry, primarily by biomedical and electronic equipment manufacturers. The life of these cutting too ls is extremely unpredictable and much shorter than conventional tools . Also, these miniature tools, with a diameter of less than 1 mm, cann ot be inspected by an operator without the aid of a magnifying glass. In this paper, evaluation of the intensity variation of a reflected la ser light beam from the cutting tool surfaces is proposed as a method of estimating cutting tool surface conditions. Various encoding method s, including wavelet transformations, were proposed to obtain a small and meaningful set of data from the intensity variation readings of on e tool rotation. The encoded data were classified using a simple thres hold method, Restricted Coulomb Energy (RCE), and Adaptive Resonance T heory (ART2)-type neural networks. The proposed encoding and classific ation approaches were tested with over one hundred sets of data. The t hreshold method detects only severe tool damage. The RCE neural networ ks and graphical presentation of the encoded sets demonstrated the fea sibility of the proposed monitoring technique and encoding methods. Th e ART2-type neural networks were found to be the best candidate for to ol condition monitoring because of their self learning capability. Wav elet transformation-based encoding and ART2-type neural networks were found to be sensitive enough to recognize wear at the cutting edge.