SHARP FEATURE DETECTION FOR INSPECTION

Abstract

The manufacturing industry constantly needs to verify machined objects against their original CAD models. Inspection applied directly on scanned points is desirable. Typical scan data, however, is very large-scale, unorganized and noisy, and usually misses information about the sampled object. Therefore, direct processing of scanned points is problematic. This paper formulates the concept of diverse scan data which may significantly facilitate the direct treatment of scanned points. The paper focuses on the sharp features of a scanned object as a type of diverse scan data, and proposes a new method for sharp feature detection. The proposed Sharp Feature Detection (SFD) method is applied directly on the scanned points and is completely automatic, fast and straightforward to implement. Finally, the paper demonstrates how the proposed SFD method can be integrated into the general framework of utilizing diverse scan data for inspection.