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研究生: 廖志偉
Chih-Wei Liao
論文名稱: 燒結生料粒徑與水分量測系統之研究
A Study of Measurement System for Sinter Raw Mix Size and Moisture
指導教授: 唐永新
Yeong-Shin Tarng
口試委員: 牟金祿
Jin-Luh Mou
郭中豐
Chung-Feng Kuo
鍾國亮
K.L. chung
邱奕契
Yih-Chih Chiou
學位類別: 博士
Doctor
系所名稱: 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年: 2009
畢業學年度: 97
語文別: 中文
論文頁數: 104
中文關鍵詞: 燒結生料粒徑分佈粒徑性質粒子水分量測自動化檢測線掃瞄影像擷取影像處理與分析.
外文關鍵詞: Pseudo-Particle Size Distribution, Electric Conductivity, Moisture.
相關次數: 點閱:326下載:3
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本研究旨在研發一燒結生料(Sinter Raw Mix)粒徑與水分量測系統,藉由非接觸式光學影像技術與導電度量測技術開發出一套全世界首創之粒徑與水分分析儀。系統檢測之粒徑大小範圍0.1mm ~ 20mm之間,利用Line Scan CCD來做影像擷取,取像率可達100%,且不會有重複和遺失的缺失,解決過去Area Scan CCD影像遺漏的問題,達到燒結生料粒子全檢功能。此自動化機械包括粒徑整列機構模組、影像擷取模組、影像分析處理模組、水分偵測模組及電控驅動模組,燒結生料經此分析儀將可自動計算出粒徑分佈狀況、粒徑數目、重量百分比與累積重量百分比、燒結生料粒子的形狀係數如:圓形度(Roundness)、球形度(Sphericity)與均勻係數(Uniformity),且具有良好之重複精度,無論那一個等級其差值皆小於2%,透過水分迴歸分析式可計算出燒結生料的水分含量,提高量測結果之可靠性與準確度,縮短作業時間、減少人為誤差與有效地降低成本,使功能更臻完善。現場操作人員可藉此設備所提供之資訊瞭解到燒結機原料的造粒與水分狀況,並即時做出相關調整,以確保生產之順暢性與提升產能。


An on-line optical scanning system together with conductivity measurement device has been developed for Sinter Raw Mix size analysis and moisture determination. Sinter Raw Mix particle image is obtained by optical Line Scan technology and analyzed by digital image processing. The developed system is composed of pseudo-particle separation module, image acquisition module, image processing module, conductivity measurement module, and electric control module. The main advantages of this system include full inspection (100%) without overlapping nor missing of any particles, which improves the Area Scan Charge Coupled Device (CCD) acquisition problems. The particle size distribution, roundness, sphericity, and uniformity can be obtained by image processing software. The deviation of repeated precision is around ±1%. And converting the conductivity with regression equation to get particle moisture. It has been shown that the developed system has a high accuracy and precision, convenience, and versatile for any kind of particle size, shape, and moisture analysis for the academic and industrial users. Currently two sets of the developed system have been run in Chain Steel Corporation successfully.

中文摘要 I 英文摘要(ABSTRACT) II 目 錄 III 圖表索引 VI 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻探討 2 1.3 研究方法與本文架構 4 第二章 燒結製程與燒結品質分析 5 2.1 燒結製程 5 2.2 粒徑與水分對燒結品質的影響 9 第三章 機構設計與系統模組 12 3.1 系統機構設計目的 16 3.2 整列機構模組 20 3.3 視覺取像模組 22 3.3.1 CCD取像模式 23 3.3.2 光源與照明技術 29 3.3.3 鏡頭與影像擷取卡的選用 32 3.4 水分偵測模組 34 3.4.1 水分偵測原理 34 3.4.2 水分偵測電路與偵測盒 36 3.4.3 溫度感測器 38 3.5 電控模組 41 3.5.1 系統電控架構 41 3.5.2 電控通訊控制 42 3.6 校正系統 47 3.6.1 CCD與光源水平位置校正 47 3.6.2 系統之像素與物理量校正 48 第四章 影像處理與粒徑分析 50 4.1 數位影像處理介紹 50 4.2 影像處理技術 53 4.2.1 影像二值化(Threshold Processing) 54 4.2.2 影像反轉(Invert image) 56 4.2.3 邊界抽取(Boundary Extraction) 56 4.2.4 影像區域填充(Region Filling) 57 4.2.5 去除邊界影像 (Remove Borders Objects) 58 4.2.6 保留邊界影像 (Remain Border Objects) 59 4.2.7 影像選取與合併(Image Mask and Merge) 59 4.3 粒徑分析原理 60 第五章 系統整合 64 5.1 系統整合架構 64 5.2 系統開發工具與環境 71 5.3 人機介面 72 第六章 實驗結果與討論 79 6.1 粒徑分析結果 79 6.2 LINE SCAN 全檢與重複精度測試 82 6.3 水分偵測實驗 84 6.3.1 水分分析關係式建立 84 6.3.2 實驗因子探討 87 6.4 討論(DISCUSSION) 89 第七章 結論與展望 90 7.1 結論 90 7.2 未來研究方向建議 92 參考文獻 94 附 錄 98 附錄一 SONY XC-55的規格表 98 附錄二 DALSA P2-2X-1K30的規格表 99 附錄三 NI IMAQ PCI-1428規格表 100 附錄四 NI-6014規格表 101 附錄五 NI-6014 PINOUT 102 論文登錄 103 作者簡介 104

參考文獻
[1] L. Banta, K. Cheng, and J. Zaniewski, “Estimation of limestone particle mass from 2D images,” Powder Technology, Vol.132, pp.184-189 (2003).
[2] C.F. Mora, A.K.H. Kwan, and H.C. Chan, “Particle size distribution analysis of coarse aggregate using digital image processing,” Cement and Concrete Research, Vol.28, No.6, pp.921-932 (1998).
[3] R. Duda and P. Hart, “Use of Hough transformation to detect lines and curves in pictures,” ACM, Vol.15, pp.204 (1972).
[4] K. Sakaue and M. Takagi, “Separation of Overlapping Particles by Iterative Method,” Jouhou Syori Gakkai Ronbunsyuu, Vol.24, No.5, pp.561 (1983).
[5] X. Song, F. Yamamoto, M. Iguchi, L. Shen, X. Ruan, and K. Ishii, “A method for measuring particle size in overlapped images,” ISIJ Int., Vol.38, No.9, pp.971-976 (1998).
[6] B. Tom and R. John, “An on-line vision system for measuring particle size of sinter and coke for No. 6 blast furnace at port kembla steel work,” BHP project report, No. ITD OP REP 98003 (1998).
[7] Y.K. Yen, C.L. Lin, and J.D. Miller, “Particle overlap and segregation problems in on-line coarse particle size measurement,” Powder Technology, Vol.98, pp.1-12 (1998).
[8] S. Biryukov, D. Faiman, and A. Goldfeld, “An optical system for the quantitative study of particulate contamination on solar collector surfaces,” Solar Energy, Vol.66, pp.371-378 (1999).
[9] G.G Gordon, “Automated glass fragmentation analysis,” Machine Vision Applications in Industrial Inspection IV, Procedings of the SPIE, San Jose, CA, pp.2665-2675 (1996).
[10] J. Caron, L. Duvieubourg, and J.G. Postaire, “A hyperbolic filter for defect detection in packaging industry,” In Int. Conf. on Quality Control and artificial Vision, Le Creusot, French, pp.207-211 (1997).
[11] D. Brzakovic and N. Vujovic, “Designing defect classification system: A cause study,” Pattern Recognition, Vol.29, pp.1401-1419 (1992).
[12] C. Fernandze, C. Platero, P. Campany, and R. Aracil, “Vision system for online surface inspection in aluminum casting process,” Proceedings of the IEEE International Conference on Industrial Electrics, Control, Instrumentation and Automation (IECON’93), pp.1854-1859 (1993).
[13] J. Caron, L.J. Duvieubourg, J. Orteu, and J.G. Revolte, “Automatic inspection system for strip of preweathered zinc,” In Int. Conf. on Applications of photonic Technology, Montréal, Canada, pp.571-576 (1997).
[14] P.B. Chou, A.R. Rao, and F.Y. Wu, “Automatic defect classification for semiconductor manufacturing,” Machine Vision and Applications, pp.201-214 (1997).
[15] H. Li, and J.C. Lin, “Using fuzzy logic to detect dimple defects of polished wafer surfaces,” IEEE Transactions on Industry Applications 30, pp.1530–1543 (1994).
[16] R.W. Conners, C.W. Mcmillin, K. Lin, and R.E. Vasquez-Espinosa, “Identifying and locating surface defects in wood: Part of an Automated Lumber Processing System,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.PAMI-5, pp.573-583 (1983).
[17] T. Ojala, M. Pietikäinen, and O. Silven, “Edge-based texture measures for surface inspection. Processing of the 11th International Conference on Pattern Recognition,” pp.594-598 (1992).
[18] N. Zuech, “Machine Vision and Lighting,” President, Vision Systems International, Consultancy (2003).
[19] 王國華,鐵礦燒結製程的參數最佳化之研究,國立台灣科技大學機械研究所碩士論文,1999年。
[20] 中國鋼鐵股份有限公司網頁,http://www.csc.com.tw/index.asp (2008).
[21] W.K. Lu and M.G. Ranade, “Recent Advances in Blast Furnace Ironmaking in North America,” ISIJ International, 31/5, PP.395 ~ 402, 1991.
[22] S. Sato, T. Kawaguchi, M. Ichidate and M. Yoshinaga : Tetsu-to-Hagane, Vol.73, PP. 964,1987.
[23] 牟金祿,“燒結礦品質與其化學成份及礦物相之關係”,技術與訓練,4/6,PP.54 ~ 70,民國七十八年。
[24] 常致泰,“決定燒結礦的因素對高爐操作影響的工場試驗”,技術與訓練,10/11,PP.42 ~ 50,民國七十四年。
[25] 趙宏欽,王吉昌,“中鋼燒結礦還原粉化率(R.D.I)之改進研究”,技術與訓練,10/8,PP.43 ~ 47,民國七十四。
[26] E. Sarigul, A.L. Abbott, and D.L. Schmoldt, ”Rule-driven defect detection in CT images of hardwood logs,” Computers and Electronics in Agriculture, Vol.41, pp.101-119 (2003).
[27] 汪光夏,“機器視覺運用”,電路版會刊,第二期,P.8-23,2000。
[28] 葉倪,“機械視覺系統的發展趨勢”,機電整合雜誌,P120-P123,2003年10月。
[29] 王新,“線性掃描攝影機之基本入門”,機電整合雜誌,P.123-130,2004年12月。
[30] 王新,“PC-based工業控制利器泰洛Line scan視覺系統”,機電整合雜誌,P23-P25,2001年9月。
[31] 王瑞陽,“機器視覺系統的光源與照明”,機械工業雜誌,P.185-200,1988年9月。
[32] 鍾國亮,“影像處理與電腦視覺“,台北,東華書局,九十三年二月。
[33] National Instruments Corporation, IMAQ Vision Concepts Manual, Texas (2000).
[34] R.C. Gonzales and R.E. Wood, Digital Image Processing, Prentice-Hall, New Jersey, pp.534-539 (2002).
[35] W.C. Krumbein and L.L. Sloss, “Stratigraphy and sedimentation (2nd ed.),” W. H. Freeman and Company, San Francisco, (1963).
[36] R.M Carter and Y. Yan, “Journal of Physics,” Conference Series 15, pp.177 (2005).

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