The Development of Adaptive Gray Level Mapping Combined Partical Wwarm Optimization for Measuring the Dimeter Size of Automotive Nut

In line assembly process, it is necessary to use the accurate tools to inspect the work piece. In order to measure the width, thickness or depth of the nut used in the commercial car, previously in Thai Steel Cable Company used the caliper to measure size of the automotive nut operated by human as an sample test inspection. Even the result on this technique is high accuracy, however; there are some disadvantages based on this method such as human error, idle time and also fatigue by the human. Therefore, this research focus on the development of the new measurement technique that utilized industrial camera together with the image processing algorithm to measure the entire nut with 100% inspection test. Circular Hough Transform (CHT) is applied to be the basic concept used for finding the circle position and measuring nut diameter. Although the CHT technique can measure diameter of the interested nut, but the result's accuracy is not acceptable due to measuring error from the various range of light condition. This research proposed the new technique, Adaptive Gray Level Mapping algorithm (AGLM) to increase the quality of the input picture before measuring the radius by CHT technique. Moreover, the beta in AGLM is optimized by particle swarm optimization (PSO) technique that applies 50% of nut data and other is used for validate. The results show the effectiveness of the proposed AGLM combined PSO that increase the accuracy of the visual measuring method via compare to the conventional threshold with CHT technique.