Machine vision for 3D mechanical part recognition in intelligent manufacturing environments

A 3D machine vision method used in intelligent manufacturing environments is presented. In this method, neural network technology is used to provide effective methodologies for solving difficult computational problems in 3D recognition processes. The recognition processes can be divided into two parts. First, a 3D reconstruction approach based on wavelet analysis is presented. The stereo matching problem is solved with wavelet analysis. Dyadic discrete wavelet analysis is adopted in this process and stereo matching process is realized with global optimization. A coherent hierarchical matching strategy is constructed, so that the stereo matching process can be accomplished with coarse to fine techniques. A 3D reconstruction neural network is constructed by using BP neural network, With the results of stereo matching, the 3D shape of part can be reconstructed. Second, the feature vectors of 3D parts tire constructed by using 3D moment and its invariant. With the results obtained in the first part, ART2 neural network is adopted for neural network classifier. With the ART2 neural network classifier, the 3D parts can be recognized and classified. The method is tested with both synthetic and real mechanical parts in intelligent assembly system. Good results are obtained. It is proved through the experiments and actual applications that the method presented in this paper is correct and reliable. It is very suitable for intelligent assembly system.