Inspection of micro-tools at high rotational speeds

Abstract

Micro-tools have been widely used in industry, primarily by biomedical and electronic equipment manufacturers. The life of these cutting tools is extremely unrpedictable and much shorter than conventional tools. Also, these miniature tools, with a diameter of less than 1 mm, cannot be inspected by an operator without the aid of magnifying glass. In this paper, evaluation of the intensity variation of a reflected laser light beam from the cutting tool surfaces is proposed as a method of estimating cutting tool surface conditions. Various encoding methods, including wavelet transformations, were proposed to obtain a small and meaningful set of data from the intensity variation readings of one tool rotation. The encoded data are classified using a simple threshold method, Restricted Coulomb Energy (RCE), and Adaptive Resonance Theory (ART2)-type neural networks. The proposed encoding and classification approaches were tested with over one hundred sets of data. The threshold method detects only severe tool damage. The RCE neural networks and graphical presentation of the encoded sets demonstrated the feasibility of the proposed monitoring technique and encoding methods. The ART2-type neural networks were found to be the best candidate for tool condition monitoring because of their self learning capability. Wavelet transformation-based encoding and ART2-type neural networks were found to be sensitive enough to recognize wear at the cutting edge.