A novel approach to the inspection of deformable bodies by adapting the coherent point drift algorithm and using a clustering methodology

The dimensional and geometric inspection of non-rigid mechanical parts still presents a real challenge to the automotive and aerospace industry. Much research has been devoted to automating the inspection of these parts in order to eliminate the cost of specialized fixtures that cause productivity problems for manufacturers. A registration step between the part’31s nominal CAD model and its corresponding scan is required for an automatic inspection. The coherent point drift (CPD) is one of the existing algorithms for registration and is widely used in imaging applications. It has been recently adapted with regard to the mechanical field for the fixtureless dimensional inspection problems of deformable bodies, notably the IDB-ACPD algorithm. In this paper, an improvement of the optimization phase by reformulating the objective function of the algorithm is put forward. A correction matrix associated with the importance of each measurement point, and therefore with the local rigidity of the part during the registration operation, was introduced and incorporated. In addition, the measurement points representing the same rigidity have been clustered based on mechanical metrics using a fuzzy c-mean algorithm. The effectiveness of the proposed approach is demonstrated on different case studies from the transport industry and the results provide an improved registration of non-rigid parts leading to better dimensional and geometric errors detection.