As a part of the HDD manufacturing process, HDD stamped base, an exterior container, is one of the most essential components in which various parts become assembled to compose a hard disk drive (HDD). Height errors that are caused by pressing, breaking or cracking can occur on the base, because it is designed by a stamping method. In order to detect the height errors, the inspection process is essential in the production fields. In the current industry, CMM (Coordinate Measurement Machine) is one of the representative machines that inspect certain regions on the product. The machine probes a designated point by an operator and judges the defect by comparing the height of the point to the originally designed height. However, the method takes much time to inspect each designated point resulting in a total of 17 minutes. In order to reduce the total inspection time, we propose an inspection method using 3D point cloud data acquired from a holographic sensor. To compare the height from acquired 3D point cloud data with the one from the originally designed CAD data, the exact point cloud registration is important. There are differences between 2D image registration and 3D point cloud registration, such as translation on each plane, rotation, tilt, and nonlinear transformations. The relationship between the acquired 3D point cloud data and the originally designed CAD data can be obtained by projective transformation. If the projective transformation matrix between the two is obtained, 3D point cloud data registration can be performed. In order to calculate 3D projective transformation matrix, corresponding points between 3D point cloud data and CAD data are required. To find the corresponding points, we use the height map which is projected from 3D point cloud data onto XY plane. The height map has pixel intensity from the height value of each point. If the height maps from 3D point cloud data and CAD data are matched, corresponding points can be estimated. As one of the features of the HDD stamped base, there are multiple circles on the base. In this paper, we find the corresponding points between 3D point cloud data and CAD data using circle fitting, and obtain 2D Affine transformation matrix from the corresponding points. By applying 2D Affine transformation matrix to height map, the corresponding points on the 3D coordinate can be obtained. Using such points, we propose the method designed to achieve 3D projective transformation matrix. To find the proper 3D projective transformation matrix, we formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D projective transformation matrix can be calculated by minimizing the cost function. Then the 3D point cloud data can be matched to CAD data and the height values of each point of 3D point cloud can be compared to the CAD data.
In this paper we propose a novel template matching algorithm for visual inspection of bare printed circuit board (PCB).1 In the conventional template matching for PCB inspection, the matching score and its relevant offsets are acquired by calculating the maximum value among the convolutions of template image and camera image. While the method is fast, the robustness and accuracy of matching are not guaranteed due to the gap between a design and an implementation resulting from defects and process variations. To resolve this problem, we suggest a new method which uses run-length encoding (RLE). For the template image to be matched, we accumulate data of foreground and background, and RLE data for each row and column in the template image. Using the data, we can find the x and y offsets which minimize the optimization function. The efficiency and robustness of the proposed algorithm are verified through a series of experiments. By comparing the proposed algorithm with the conventional approach, we could realize that the proposed algorithm is not only fast but also more robust and reliable in matching results.