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Title |
The implementation of automated optical inspection in printed circuit boards |
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Author |
Arash Aravand |
| Citation |
Vol. 17 No. 6 pp. 137-146 |
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Abstract |
Different machines are used in the production of electronic boards to control the elements, the correct amounts and their directions. The automated optical inspection systems are used to solve the excessive complexity of printed circuit boards and increase the production with high accuracy as well as low errors. In cases where the quick inspection of the board is impossible, the inspection is done visually with eyes. Since, the eyes get tired of continuous work, automating this process increases the production and quality of products. Therefore, a lot of visual devices are presented in the industry. The automated optical inspection systems can be used at any stage of production. AOI is one of these systems. In this technique, AOI machine is able to get a picture of the board. The automated optical inspection system uses the registered image to compare the information of images with the information of the machines to detect errors and make decisions. Using this comparison, the AOI system can detect and identify any error or suspicious area. In the present article, we have attempted to build an AOI device to cover all the errors happening in the printed circuit boards at any stage of the production line. The traveling salesman problem is solved to control the direction of movement of the camera on the conveyor. To introduce any printed circuit board to the system, a software is designed that uses CAD file to obtain the type of elements on the printed circuit board and their conditions, finds the optimal route for the movement of X-Y table, detects the errors arising from lack of elements, elements' directions, the connection of two bases, the lack of soldering, cold soldering, added soldering, etc. in three stages of feature extraction, feature selection and decision-making. The results show that the device is efficient in the detection of the glue error before mounting of the element and the detection of SMD and DIP elements after bath. |
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Keywords |
Evolutionary algorithms, artificial intelligence, feature selection, feature extraction, error detection |
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