Two approaches to detect defects on printed circuit board had been evaluated. One is the direct comparison of the tested image with a template image. Before the comparison, we adopted an interpolation method to reconstruct the test image such that the orientation and position of components shown on the test image are the same as those on the template image. The second approach is using image features to detect and classify defects. We proposed a two steps inspection scheme. The inspection system is divided into the screening stage and the classification stage. The object of the screen stage is to quickly screen out most normal components to reduce overall processing time. Only one image feature is used as the screen index. At the classification stage, the neural networks were adopted to integrate all image feature information available to more precisely classify those fail to pass the screening test.
The industrial application of an automated inspection system that aims to enhance the efficiency and flexibility of a computer integrated manufacturing system (CIMS) is proposed in this paper. A machine-vision-based approach is adopted to utilise its advantages of measurement flexibility, high resolution, and non-destruction. With a closed-loop feedback 1, IntroductionComputer integrated manufacturing (CIM) is widely accepted as a fundamental strategy employed for enterprises competing in the world market. The implementation of CIM systems, despite a considerable number of successful cases having been reported in many countries, remains a difficult task. A CIM system is typically a highly computerised system that consolidates all production and management functions in a company to form a more efficient unit, generally in an untended mode. The product quality and productivity can consequently be increased. In order to ensure a high quality product, it is essential to integrate an automated inspection system with manufacturing. Most current inspection systems achieve this quality control function by detecting deviations from specifications and relaying this information to a central computer. Fully automating the manufacturing system, however, requires that the inspection systems should possess the Correspondence and offprint requests to: Fei-Long Chert, Department of Industrial Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China. capability both to determine the possible causes of failures in manufacturing and to recommend solutions for these failures.The industrial application of an automated inspection system aimed to elevate the efficiency and flexibility of an automated manufacturing system is reported in this paper. A machinevision-based approach is adopted here which utilises its advantages of measurement flexibility, high resolution, and non-destruction. A closed-loop feedback control architecture is formed by fully integrating the inspection procedures with the production process. A defective part, once detected, is collected by a material handling device. An intelligent diagnostic algorithm based on statistics and heuristics is simultaneously applied to analyse the consecutive inspection results in order to deduce possible causes of defects. This system therefore not only eliminates defects but also recovers normal operations in a real-time mode.This proposed automated inspection system has been implemented on a physical shop floor for automatic inspection of wrench sockets of varied types and sizes. The machine vision system uses two cameras that perform multiple functions:1. The first camera recognises the shape of the socket, and measures its dimensions and concentricity. 2. The second camera determines the amount of die wear.This system, with other automated devices, assists in the formation of a truly automated manufacturing system. The experimental results show that the machine vision system has satisfactory performance and flexibility in the factory environment. Even thoug...
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