Intelligent LI-On Battery Polarity Pad Image Inspection System
| Autor: | Chen Tsung Sheng, 陳宗聖 |
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| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 Throughout the changing times, automation not only replaced people's work but also created many products. Machine vision inspection can replace the human to detect all kinds of products.It not only can increase production speed, but also improve the quality of the products. The purpose of this thesis is the use of automated machine vision to detect the flatness of the lithium battery pole piece that meets the criteria. If the battery pole piece height is too high or too low, it might cause battery short and affect product and user safety. This thesis uses two cameras to capture positive (aluminum pole) and negative (copper electrode) pole piece of lithium batteries. It uses median filter to decrease noise and then do the binary, image edge enhancement and image growth. The key parameters include four features that are image area ratio, width ratio, highest position ratios and lowest position ratios of the pole pieces. Then, the calculated data are input to the back-propagation neural network to do the training process and image classification. This neural network has four inputs, four hidden units and three outputs. A hundred positive and a hundred negative pole piece images are used for experiments in this study. The classification outputs include normal, inside and outside bending. The purpose of classification is to locate bending pole piece, because these kinds of images are unable to measure properly. Finally, the examined product which is good or not good can be determined by comparing with the standard value. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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