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研究生:蔡典霖
研究生(外文):Dan-Lin Tsai
論文名稱:應用機械視覺於印刷電路板表面元件之檢測
論文名稱(外文):The Automatic Optical Inspection of Surface Mount PCBs Using Machine Vision
指導教授:林士傑林士傑引用關係
學位類別:碩士
校院名稱:國立清華大學
系所名稱:動力機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:77
中文關鍵詞:電腦視覺表面黏著元件類神經網路
外文關鍵詞:Computer VisionSMDNeural Network
相關次數:
  • 被引用被引用:21
  • 點閱點閱:518
  • 評分評分:
  • 下載下載:155
  • 收藏至我的研究室書目清單書目收藏:2
在印刷電路版的組裝業中,雖然技術不斷的成熟、提升,但製程當中仍存有許多瑕疵,大致上分為四大類,包括有缺件、歪斜、反向、錫誤四種。本研究針對片狀電阻、片狀電容等元件做瑕疵檢測。
本研究應用文獻中之灰階分區統計法、相關係數法、白點統計法,以及吾人所提出之高灰階差值像素比例法(一)、高灰階差值像素比例法(二)等五種演算法,以實際生產線上的表面黏著元件(SMD)為樣本做檢測並分析各個演算法的優缺點。由於單一演算法並無法完全有效的檢測出所有的元件是否合格,且在分類缺陷時亦相當困難,因此本研究藉由類神經網路來整合各個演算法的優點,並將檢測的結果以誤判率、漏失率、正確率、檢測時間四個項目來評估其檢測效果。
由檢測的結果可得知,有5個輸入參數(Input)的神經網路會有較佳的檢測結果,其誤判率以及漏失率皆為0,所以可以有效的辨別元件是否合格。因此藉由類神經網路的輔助,不但能正確的辨別元件是否合格,且在分類不合格元件的效果也比單一指標來的佳。缺點在於仍無法將不合格元件的缺陷種類完全分辨出來,但這個問題是可以忽略的。
The SMT Machine in PCB Assembly has been improving rapidly in the past decades. However, there are still defects occurring during the working process. In general, the defects can be classified into four categories, such as component missing, misalignment, counter polarity, and solder defect.
This paper focuses on chip resistor and chip capacity inspection. We apply the algorithms in the technical literatures, such as Gray statistics method, Pattern matching method, Extreme point method and two algorithms that we proposed to inspect the on-line SMD samples.
Due to the fact that single algorithm cannot differentiate completely to determine whether the SMD samples are qualified and it is difficult to classify the defects of SMD samples; therefore, we integrate the advantages of these five algorithms by Neural Network. Then we estimate the inspection results in four indexes, which are False alarm rate, Fault missing rate, Incorrect flaw classification and inspection time. The goal is to design a complete inspection procedure in order to detect the defects more correctly and conveniently.
After being verified by numerical simulations and experiments, there are five inputs of Neural Network that have better inspection result. With these five inputs, the False alarm rate and Fault missing rate are both equal to zero. Therefore, Neutral Network is better than single algorithm in inspection and classification of the defects of SMD sample.
目 錄

中文摘要 Ⅰ
英文摘要 Ⅱ
誌謝 Ⅲ
目錄 IV
表目錄 VI
圖目錄 Ⅶ

第一章 緒論 01
1-1.前言 01
1-2.研究目的 02
1-3.元件缺陷種類及表面黏著元件之介紹 03

第二章 文獻回顧 08
2-1.光學檢測系統的評估指標 08
2-2.電路板元件檢測演算法 09

第三章 研究方法與步驟 19
3-1.元件資料格式及元件尺寸資料庫 20
3-2.新的檢測指標 20
3-3.類神經網路 22

第四章 實驗規劃與設備 34
4-1.實驗規劃 34
4-2.測試樣本的規劃 35

第五章 實驗結果與討論 39
5-1.演算法的測試結果 39
5-2.類神經網路訓練結果 41
5-3.類神經網路檢測結果 43
5-3.結論 45

第六章 結論與建議 56
6-1.結論 56
6-2.建議 57
參考文獻 58
附錄A 61
附錄B 72
參考文獻

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