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Special Graduate Schools
Graduate School of Science and Technology
Department of Industrial Engineering
3. Theses(Master)
뉴럴 네트워크 기반 연성 인쇄 회로 기판 검사 시스템
- Alternative Title
- An Inspection System for Flexible Printed Circuit Board based on Neural Network
- Author(s)
- Cho, Kang Hoon
- Advisor
- 박상철
- Department
- 일반대학원 산업공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2015-08
- Language
- eng
- Alternative Abstract
- This thesis judges the excessive images among images which are objects to inspection in visual inspection, and suggests the inspection system based on neural network to minimize the images which was regarded as faulty image from image of fair quality. F-PCB (Flexible Printed Circuit Board) operates to inspection quality firstly by the inspection device, the image regarded as faulty should be the object to inspection in macrograph. The objects of visual inspection need to take second inspection by professional people, but they shall be wasted and shall decrease their unity of qualities because the number of images are too much. For these matters, there need to have the inspection system for figuring out whether something would be fair quality or faulty. To structure the proposed inspection system, using the neural network can solve these matters. Neural network have been known as the level of studying or the level of testing. In the stage of studying, it can be far different depending on the data; consistency of data is important. Visual inspection images involve noisy due to the place or position of inspection device. This paper suggest ‘Sweep Searching Filtering,’ ‘Island Filtering’ to find a key from these matters because images involving noisy let down the consistent data. Moreover, it also suggests four different types of methods to extract a parameter, characteristic of images for studying. The suggested inspection system in this studying will judge the excessive images, have an objective to minimize the faulty image from image of fair quality, and demonstrate the efficiency of inspection system through the studied neural network.
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