Automated visual inspection for microdrills in printed circuit board production

Abstract

The rapid growth in printed circuit board production has led to the growing importance of microdrilling. Owing to the trend for high-density circuits on printed circuit boards, the diameter of a microdrill has to be much finer than ever before. The inspection for microdrills becomes more difficult and the detection of defects is harder. An automated visual inspection scheme for detecting the major defects of microdrills is proposed. The colour images of microdrills are first derived, and the boundary of the lip relief plane is extracted. A robust K-curvature corner detection algorithm is then used to detect the corners on the blade boundaries and to split boundaries into segments. The least-squares linear regression method is used to fit the segments into linear equations. With the fitted equations, the intersections on the boundaries of two facets are located and their distances (gap or overlap) are measured. In addition, the defects of taper and flare are identified by comparing the angle measurements with predefined specifications. Two types of microdrills are used to verify the proposed inspection process. Experimental results show that the proposed scheme reliably achieves precise inspection.