| 英文摘要 | NdFeB has the highest performance price ratio of all magnetic materials. The NdFeB magnets, made of NdFeB magnetic materials, are the leading elements of rare-earth permanent magnetic materials industry. It’s not only used in household appliances, and is widely used in aviation, aerospace, electronics, automobile industry, petrochemical industry, instrumentation and other installations and equipment in need of permanent magnetic field. NdFeB industry is a sunrise industry, the new application and growing point of which are coming forth. NdFeB materials industry’s market prospect is very broad. However, because of limited equipment and technology of NdFeB magnet processing at present and surface damage caused by electroplating, the yield of NdFeB is very low. Using magnets with surface defects, will not only seriously affect the performance and life of the whole machine, and lead to difficult to estimate economic losses. Now, the surface defects detection in the field is mainly by manual, which is unable to meet the requirements of both product quality and production efficiency. In this paper, based on analysis of the NdFeB surface defects type, we proposed an efficient detection algorithm and designed a set of NdFeB surface defects detection system based on machine vision. Specifically, in the paper, the work that has been done and research achievements are as follows: (1) According to the modular design, our system is divided into the following modules: automatic feeding module, workpiece’s transmission, turn-over and rotation module, image acquisition module, I/O control module, in which, we designed the process of camera soft trigger control and sorting control, man-machine interface module and image processing module, which includes defective object segmentation, defective feature extraction and classification and so on. (2) Defective object segmentation: we compared 3 kinds of adaptive threshold segmentation algorithm, and in view of our image, proposed an improved OTSU segmentation algorithm. First, adopt projection method to get the defect object region (the number of pixels is n) and compute the histogram h (x) of this region; second, add equal proportion pixel number at u, which is the mean gray level of background, in the histogram, that is, h (u) =h (u) + n; finally, based on the new histogram, segment the image with OTSU method, and the result is better than general OTSU algorithm. (3) Defective feature extraction: first, in view of abno... |
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