3D Measurement for Mechanical Parts in Neuro-vision System

In this paper, a new on-line measurement and accuracy analysis method for part configuration and surface is presented by combining computer vision and neural networks. Different from conventional contact measurement, it is non-contact measurement method, and it can operate on-line. In this method, the 3D configuration and surface of part are reconstructed from stereo image pair taken by computer vision system. The architecture for parallel implementation of part measurement system is developed using neural networks. Several relevant approaches including system calibration, stereo matching, and 3D reconstruction are constructed using neural networks. Instead of conventional system calibration method that needs complicated iteration calculation process, the new system calibration approach is presented using BP neural network. The 3D coordinates of part surface are obtained from 2D points on images by several BP neural networks. Based on the above architecture and the approaches, the part measurement and accuracy analysis system for intelligent manufacturing is developed by making fall use of the advantages of neural networks. The experiments and application research for this system is also presented in this paper. It is proved through the actual application that the method presented in this paper can meet the needs of on-line measurement for parts in intelligent manufacturing. It has important value especially for on-line measurement of parts that have complicated surface. Key-Words: neural networks, on-line measurement, computer vision, 3D reconstruction, camera calibration.