自動化光學檢測之印刷電路板除膠雛型機之開發

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：53

、訪客IP：3.149.249.154

姓名

施庭宇(Ting-Yu Shih) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

自動化光學檢測之印刷電路板除膠雛型機之開發
(Development of a Prototype Machine for Removing Glue on Printed Circuit Boards by Automated Optical Inspection)

相關論文

★ 自動平衡裝置在吊扇上之運用	★ 以USB通訊界面實現X-Y Table之位置控制
★ 液體平衡環在立式轉動機械上之運用	★ 液流阻尼裝置設計與特性之研究
★ 液晶電視喇叭結構共振異音研究	★ 液態自動平衡環之研究
★ 抑制牙叉式機械臂移載時產生振幅之設計	★ 立體拼圖式組合音箱共振雜音消除之設計
★ 電梯纜繩振動抑制設計研究	★ 以機器學習導入電梯生產結果預測之研究
★ 新環保冷媒R454取代R410A冷媒迴轉式單缸壓縮機效能分析與可靠性驗證	★ 高速銑削Al7475-T7351的銑削參數與基因演算法研究
★ 自動化鞋型切削機之設計與實現	★ 以FPGA為基礎之精密位置控制IC
★ CNC三維圓弧插補器	★ PID與模糊控制在營建工程自動化的探討

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

在工業 4.0 的潮流下，工業自動化勢不可擋。自動化光學檢測(AOI)是現
代工業品管自動化的重點技術，本研究將以印刷電路板(PCB)除膠機為題，
整合一套具自主整合技術的 AOI 自動化 PCB 除膠機，協助國產自動化產業
的自主技術的發展。本研究整合三軸精密平台、線性掃描相機、同軸視覺系
統等三大設備，以 OpenCV 函式庫做為影像處理的核心。運用線性掃描相
機完成 PCB 表面殘膠的粗定位，確定所有殘膠座標後，依序將待消除的殘
膠移至同軸視覺系統的振鏡下方執行精定位除膠。精定位流程將辨識殘膠
的輪廓並規劃雷射除膠路徑，由振鏡快速移動雷射光束去除殘膠。實驗結果
表明該套雛型機已能完成 PCB 表面殘膠的除膠任務，但處理速度仍有待加
強。未來持續優化程式表現後，即可上線運作，提升生產效率。

摘要(英)

Under the Industry 4.0, automated optical inspection (AOI) is the key technology of modern industrial quality on control automation. This research will focus on the system integration technology of printed circuit board (PCB) residual glue removing by AOI system. This technology is autonomous, so it can promote the development of this country’s autonomous technology. This research can be divided three major equipments : precision three-axis granite gantry platform, linear scan camera and coaxial laser vision system. The program of this research uses OpenCV library as the core of image processing. The linear scanning camera is used to detect the rough position of the residual glues on the PCB surface. After all the residual glues coordinates are determined by linear scan camera, the
residual glues will be moved to the central of the coaxial laser vision system sequentially. Then the residual glues will be fine positioning and removed by laser. The precise positioning process will identify the contour of the residual glue firstly. Then it will plan the laser glue removal path, and control the galvanometer quickly
move the laser beam to remove the residual glue. The experimental results show that the prototype machine can complete the goal of removing the glue on the PCB surface, but the processing speed still needs to be strengthened. After the program performance is continuously optimized in the future, it can be put into operation to improve production efficiency.

關鍵字(中)

★ 自動化光學檢測
★ 除膠
★ 印刷電路板
★ 雷射

關鍵字(英)

★ AOI
★ PCB
★ Laser

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 xi
符號說明 xii
一、緒論 1
1-1 研究背景與動機 1
1-2 文獻回顧 2
1-3 研究架構與方法 6
二、雷射除膠機之系統架構介紹 9
2-1 精密移動平台 9
2-2 線性掃描系統 16
2-3 同軸視覺系統 21
2-3-1 雷射源 21
2-3-2 掃描振鏡 25
2-3-3 同軸成像系統 28
2-4 機器視覺函式庫 31
三、系統運作原理 33
3-1 粗定位流程之 PCB 殘膠辨識原理 33
3-1-1 HSV 色彩空間 33
3-1-2 灰階化 35
3-1-3 中值濾波 36
3-1-4 二值化 37
3-1-5 Canny 邊緣辨識 38
3-1-6 邊緣面積與周長挑選 44
3-2 掃描相機與掃描振鏡之間的座標轉換 45
3-3 精定位流程之 PCB 殘膠辨識原理 47
四、系統程式架構與運行結果 52
4-1 系統程式架構 52
4-1-1 粗定位程式架構 53
4-1-2 精定位程式架構 57
4-2 運行結果 59
4-2-1 粗定位程式運行結果 59
4-2-1-1 ROI 遮罩 59
4-2-1-2 中值濾波與 HSV 顏色過濾 61
4-2-1-3 灰階化與二值化 63
4-2-1-4 Canny 邊緣辨識與邊緣篩選 64
4-2-1-5 座標轉換 65
4-2-2 精定位程式運行結果 66
五、結論與未來展望 79
5-1 結論 79
5-2 未來展望 80
六、參考文獻 81

參考文獻

[1] F. Guo and S. Guan, “Research of the Machine Vision Based PCB Defect Inspection System ”, 2011 International Conference on Intelligence Science and Information Engineering, Vol 20-21, pp. 472-475, August 2011, doi: 10.1109/ISIE.2011.47.
[2] B. M, K. M, S. A, and A. E, “Machine vision based defect detection approach using image processing”, 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Vol 16-17, pp. 1-5, September 2017, doi: 10.1109/IDAP.2017.8090292.
[3] W. Wang, S. Chen, L. Chen, and W. Chang, “A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards”, IEEE Access, Vol 5, pp. 10817-10833, November 2016, doi: 10.1109/ACCESS.2016.2631658.
[4] A. Raj and A. Sajeena, “Defects Detection in PCB Using Image Processing for Industrial Applications”, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Vol 20-21, pp. 1077-1079, April 2018, doi: 10.1109/ICICCT.2018.8473285.
[5] T. J. M. Sanguino and M. Smolčić-Rodríguez, “Computer-aided system for defect inspection in the PCB manufacturing process”, 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), Vol 13-15, pp. 151-156, June 2012 2012, doi: 10.1109/INES.2012.6249821.
[6] X. Li, Z. Zeng, M. Chen, and S. Che, “A new method of resistor′s color rings detection based on machine vision”, 2017 Chinese Automation Congress (CAC), Vol 20-22, pp. 241 -245, October 2017, doi: 10.1109/CAC.2017.8242770.
[7] W. Fupei, Z. Xianmin, K. Yongcong, and H. Zhenzhen, “An AOI algorithm for PCB based on feature extraction”, 2008 7th World Congress on Intelligent Control and Automation, Vol 25-27, pp. 240-247, June 2008 2008, doi: 10.1109/WCICA.2008.4592931.
[8] Y. T. Li, P. Kuo, and J. I. Guo, “Automatic Industry PCB Board DIP Process Defect Detection System Based on Deep Ensemble SelfAdaption Method”, IEEE Transactions Components Packaging and Manufacturing Technology, Vol 11, pp. 312-323, December 2020, doi: 10.1109/TCPMT.2020.3047089.
[9] L. H. d. S. Silva, G. O. d. A. Azevedo, B. J. T. Fernandes, B. L. D. Bezerra, E. B. Lima, and S. C. Oliveira, “Automatic Optical Inspection for Defective PCB Detection Using Transfer Learning”, 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Vol 11-15, pp. 1-6, November 2019 ,doi: 10.1109/LACCI47412.2019.9037036.
[10]Galil Motion Control Inc., “GALIL (2010/2011 Motion Control Product Catalog)”, pp.57, 2010.
[11]S. S. GmbH：AcquisitionApplets User Documentation, June 16, 2021, from https://docs.baslerweb.com/framegrabbers/files/en/AdvancedAcquisitionApplets/mE5-MAACL/html/Acq_SingleMediumLineRGB.html#FG_SHAFTENCOD
ERON.
[12]THORLABS ： F-Theta Lenses Tutorial, June 16, 2021, from https://www.thorlabs.com/newgrouppage9.cfm?objectgroup_id=10766.
[13]A. Kaehler，G. Bradski，OpenCV 3 學習手冊，賴屹民，2020 年
05 月初刷四版，碁峰資訊，台北市，2017 年 11 月。
[14]Wiki Team ： HSL 和 HSV 色彩空間 , June 16, 2021, from https://zh.wikipedia.org/wiki/HSL%E5%92%8CHSV%E8%89%B2%E5%BD%A9%E7%A9%BA%E9%97%B4.
[15]I. Sobel, “An Isotropic 3x3 Image Gradient Operator”, Stanford A.I. Project, February 2014.

指導教授

董必正(Pi-Cheng Tung)

審核日期

2021-10-12

推文