A Method for Void Inspection in X-ray CT Images Based on Golden Part Generated from a Set of Accepted Parts

X-ray Computer Tomography systems (XCT) which can visualize internal structures of objects are very useful tool for detecting defects of parts. Their most common application is inspection of parts made by metal casting or by resin molding. These parts often contain voids, which are small empty regions inside the parts. XCT can scan these parts to generate the 3D CT images of their internal structure involving voids. The parts usually include both unacceptable and acceptable voids. What is practically needed for inspection is to detect unacceptable voids. In this paper we propose our unique classification method based on a concept so called golden part. We compute a golden part by averaging images of the parts containing only acceptable voids. Then the inspection of a test part can be made by comparing the CT image of the test part and the golden part. If the voids of the test part exist only in the void region of the golden part, the test part is passed while if some voids exist elsewhere, it is failed. We implemented a prototype inspection system and apply it to a test case. Its examples demonstrate the usefulness of our method.