碩士 / 元智大學 / 工業工程研究所 / 87 / Gneraly, therer are several statistical methods used to classify surface defects of a manufactured part:the Bayesian classifier, the linear discriminant function classifier, the minimum distance classifier, and the nearest neighborhood classifier. Each classifier has its characteristics and restrictions, thus makes the classification results unreliable.
This research starts from getting the color image of the golden fingers of a PCB, and use the raw RGB values for defects classification.The Logistic Regression Tree Classifier was applied to classify four defects of golden fingers: scuffing, blotted tin,exposed nickel, and unplating. The results showed that the proposed classification method obtained 89.33% accuracy rate, as opposed to other classifiers of about 56 to 80%. The advantages and limitations of the proposed method were also discussed.
| Identifer | oai:union.ndltd.org:TW/087YZU00030001 |
| Creators | Pei-Ling Chen, 陳佩鈴 |
| Contributors | Dr.B.C. Jiang, 江行全 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 88 |
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