簡易檢索 / 詳目顯示

研究生: 林世為
Shi-Wei Lin
論文名稱: 基於機器視覺之錫波高度量測
The Height Measurement of Soldering Wave Based on Machine Vision
指導教授: 林淵翔
Yuan-Hsiang Lin
口試委員: 郭重顯
Chung-Hsien Kuo
郭景明
Jing-Ming Guo
黃文正
Wun-Jheng Huang
學位類別: 碩士
Master
系所名稱: 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 53
中文關鍵詞: 樹莓派錫波高度高度量測動態偵測影像處理波峰焊
外文關鍵詞: height detect
相關次數: 點閱:337下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

  PCB元件焊接對於電子工廠生產線非常重要,波峰焊 (Soldering wave) 品質的好壞會直接影響PCB的良率,其中波峰焊的高度更會直接影響到PCB元件焊接的狀況,高度過高可能會使電路短路,高度過低則可能造成空焊,降低焊接的品質。
  然而目前在工廠中使用的錫爐大部分只有溫度監控的功能,並沒有錫波高度的監測,目前是請廠務人員利用目測的方式去調整錫波的高度,這個方法過於依賴廠務人員過往對於錫波高度觀測的經驗,缺少科學化的數據,而且也無法即時監控錫波高度。市面上雖有專用的錫波高度量測商用感測器,但它的架設受到嚴格限制,且量測的高度和範圍有限。
  本論文開發了一套基於樹莓派的影像量測方法,使用Webcam以非接觸式量測錫波高度,主要以動態偵測的方式抓取錫波移動的軌跡,藉由錫波移動的軌跡來計算出當前高度。
  本論文的實驗分為實驗室環境與工廠環境。在實驗室環境中與商用感測器比較的MAE±SD為0.171±0.115 mm,RMSE為0.217;在工廠環境中與商用感測器比較的MAE±SD為0.213±0.119 mm,RMSE為0.283。


  PCB components soldering is an important part of the electronics factory production line, and the quality of the wave soldering directly affects the production quality. If the height of soldering wave is too high, the circuit will be shorted. If the height of soldering wave is too low, the circuit will be not connected.
  However, currently in electronic components factory, the solder machine in the factory only have the temperature monitor, and there is no monitoring of the height of soldering wave. And the height of wave soldering usually estimated by the operator or by using high-temperature resistant glass plate. However, in this situation, it's difficult to control the status of solder machine and keep stability of wave soldering height.
  Therefore, this paper proposed a method that uses a camera with dynamic detection to monitor the height of soldering wave, which can measure without affecting the operation of the solder machine.
  The experiments in this paper were divided into laboratory and factory environment. Compared with commercial sensor, the average error was 0.171±0.115 mm(MAE±SD) and RMSE was 0.217 in laboratory environment, and the average error is 0.213±0.119 mm(MAE±SD) and RMSE is 0.283 in factory environment.

摘要 I ABSTRACT II 致謝 III 目錄 IV 圖目錄 VI 表目錄 VII 第一章、 緒論 1 1.1 動機與目的 1 1.2 文獻探討 2 1.3 相關論文比較 3 1.4 論文架構 4 第二章、 背景與原理 5 2.1 背景 5 2.2 波峰焊 6 2.3 雙列直插封裝 8 2.4 渦電流距離感測器 8 2.5 三幀差法 9 2.6 背景減法 11 2.7 Otsu自適應閥值 12 第三章、 研究方法 14 3.1 系統介紹 14 3.2 攝影機校正分析 16 3.2.1 徑向變形探討 16 3.2.2 像素轉換公式建立 18 3.3 影像處理流程 19 3.3.1 前處理(Image Pre-processing) 20 3.3.2 動態偵測(Dynamic Detect) 21 3.3.2.1 三幀差法改良 21 3.3.2.2 背景減法 22 3.3.3 形態學處理(Morphology) 26 3.4 特徵分析 29 3.5 錫波高度計算 31 第四章、 實驗方法與結果討論 33 4.1 實驗方法 33 4.1.1 實驗概述 33 4.1.2 實驗設備介紹 33 4.2 實驗一(實驗室) 34 4.2.1 實驗設計 36 4.2.2 實驗數據 35 4.3 實驗二(工廠) 42 4.2.1 實驗設計 42 4.2.2 實驗數據 44 4.4 量測經度探討 47 第五章、 結論與未來展望 50 參考文獻 51

[1] The printed circuit report. Accessed: Jan. 22, 2019. [Online]. Available: https://www.prismark.com/printed-circuit-report-pcb.
[2] Taiwan PCB market. Accessed: Jan. 22, 2019. [Online]. Available: http://www.tpca.org.tw/Message?mid=102&itemid=11.
[3] Y. Wang, B. Wang, J. Cai, and T. Wang, “Impact of soldering terminal solderability of component and PCB on solder joint interface,” in Proc. Int. Conf. ICEPT-HDP 2012, Mar. 2012, pp. 877–883.
[4] D. Barbini and J. Bath, “Lead-free wave sodering,” in Springer US, 2011, ch. 3, pp. 45–69.
[5] E. Guene, “Solderability and reliability evolution of no clean solder fluxes for selective soldering,” in Proc. EMPC 2017 - 21st Eur. Microelectron. Packag. Conf. Exhib., Apr. 2018, pp. 1–10.
[6] Q. He, Z. Su, Z. Xie, Z. Zhong, and Q. Yao, “A novel principle for molten steel level measurement in tundish by using temperature gradient,” IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1809–1819, Mar. 2017.
[7] C. Gaber, K. Chetehouna, H. Laurent, C. Rosenberger, and S. Baron, “Optical sensor system using computer vision for the level measurement in oil tankers,” IEEE Int. Symp. Ind. Electron., no. 1, pp. 1120–1124, Nov. 2008.
[8] S. Eppel, “Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach,” arXiv Prepr. arXiv:1501.04691, 2015.
[9] P. Bistak, “Identification and control of hydraulic system using visual feedback,” in Proc. 2016 Int. Conf. Emerg. eLearning Technol. Appl., Jan. 2017, pp. 29–34.
[10] M. Kim, J. Jang, K. Jeong, D. Kim, and J. Paik, “Liquid-level estimation using region-based segmentation for automatic beverage refilling service,” in Proc. Int. Symp. Consum. Electron(ISCE), Aug. 2015, pp. 2–3.
[11] Z. Q. Su, Q. He, Z. Xie, “Molten steel level measurement based on optical flow analysis,” Journal of Northeastern University. Sci., vol. 39, no. 2, pp. 158–161, Feb. 2018.
[12] B. Du, Y. Sun, S. Cai, C. Wu, and Q. Du, “Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm,” IEEE Geosci. Remote Sens. Lett., vol. 15, no. 2, pp. 168–172, 2018.
[13] S. Y. Chen and S. X. Wang, “Moving object tracking based on five frame difference and improved meanshift algorithm,” Comput. Sci., vol. 43, no. 6A, pp. 203–206, Jun. 2016.
[14] Z. Xu, D. Zhang and L. Du, “Moving object detection based on improved three frame difference and background subtraction,” in Proc. Int. Conf. Ind. Informatics - Comput. Technol. Intell. Technol. Ind. Inf. Integr., 2017, pp. 79-82.
[15] X. J. Zhang and H. Xu, “Moving vehicle detection algorithm based on video processing,” Chinese J. Liq. Cryst. Displays, vol. 27, no.1, pp. 108-113, Feb. 2012.
[16] W. A. Kaminski and G. M. Wojcik, “Liquid state machine built of hodgkin – huxley neurons,” Neurocomputing, vol. 239, pp. 245-251, 2004.
[17] D. S. Suresh and M. P. Lavanya, “Motion detection and tracking using background subtraction and consecutive frames difference method” Int. J. Res. Stud. Sci. Eng. Technol., vol. 1, no. 5, pp. 16-22, Aug. 2014.
[18] H. Wang, S. K. Nguang, and J. Wen, “Robust video tracking algorithm: a multi-feature fusion approach,” IET Comput. Vis., vol. 12, no. 5, pp. 640–650, Jul. 2018.
[19] J. Guo, J. Wang, R. Bai, Y. Zhang, and Y. Li, “A new moving object detection method based on frame-difference and background subtraction,” in Proc. IOP Conf. Ser. Mater. Sci. Eng., pp. 1–4, 2017.
[20] T. Bouwmans, “Traditional and recent approaches in background modeling for foreground detection: An overview,” Comput. Sci. Rev., vol. 11–12, pp. 31–66, May 2014.
[21] J. R. Evans and W. M. Lindsay, “Managing for quality and performance excellence, ” in Cengage Learning, Jan. 2007, pp. 574.
[22] Wave soldering defects. Accessed: Apr. 11, 2019. [Online]. Available: https://www.epectec.com/pcb/wave-soldering-defects/.
[23] Soldering wave wikipedia. Accessed: Apr. 11, 2019. [Online]. Available: https://en.wikipedia.org/wiki/Wave_soldering.
[24] R. H. Todd, D. K. Allen and L. Alting, “Manufacturing processes reference guide,” in Industrial Press, 1994, pp. 393-394.
[25] K. H. J. Buschow et al., “Encyclopedia of materials: science and technology,” in Elsevier, 2001, pp. 2708-2709.
[26] Dual in-line package wikipedia. Accessed: Apr. 18, 2019. [Online]. Available: https://en.wikipedia.org/wiki/Dual_in-line_package.
[27] Eddy current wikipedia. Accessed: Apr. 18, 2019. [Online]. Available: https://en.wikipedia.org/wiki/Eddy_current.
[28] Y. Wang et al, “CDnet 2014: An expanded change detection benchmark dataset,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, Jun. 2014, pp. 387–394.
[29] Y. Zhang, X. Wang, and B. Qu, “Three-frame difference algorithm research based on mathematical morphology,” Procedia Engineering, vol. 29, no. 4, pp. 2705–2709, 2012.
[30] N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Trans. Syst. Man Cybern., vol. 9, pp. 62–66, Jan. 1979.
[31] “Raspberry pi 3 Model B plus website” Accessed: Jul. 9, 2019. [Online]. Available: https://www.raspberrypi.com.tw/19429/57/
[32] “Matlab calibration” Accessed: Jun. 13, 2019. [Online]. Available: https://www.mathworks.com/help/vision/ug/camera-calibration.html.
[33] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 11, pp. 1330–1334, Nov. 2000.
[34] J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.
[35] “OpenCV canny edge detector” Accessed: Jul. 10, 2019. [Online]. Available: https://docs.opencv.org/3.4/da/d5c/tutorial_canny_detector.html
[36] “Sobel operator wikipedia” Accessed: Jul. 10, 2019. [Online]. Available: https://en.wikipedia.org/wiki/Sobel_operator
[37] “Mean absolute error wikipedia” Accessed: Aug. 7, 2019. [Online]. Available: https://en.wikipedia.org/wiki/Mean_absolute_error
[38] “Root mean square error wikipedia” Accessed: Aug. 7, 2019. [Online]. https://en.wikipedia.org/wiki/Root-mean-square_deviation

無法下載圖示 全文公開日期 2024/08/20 (校內網路)
全文公開日期 2024/08/20 (校外網路)
全文公開日期 2024/08/20 (國家圖書館:臺灣博碩士論文系統)
QR CODE