請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66149完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 馬劍清(Chien-Ching Ma) | |
| dc.contributor.author | Yu-Lun Lee | en |
| dc.contributor.author | 李宇倫 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:23:34Z | - |
| dc.date.available | 2020-02-18 | |
| dc.date.copyright | 2020-02-18 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-02-11 | |
| dc.identifier.citation | Reference
[1] J. Xavier, A. M. R. Sousa, J. J. L. Morais, V. M. J. Filipe, and M. Vaz, “Measuring Displacement Fields by Cross-Correlation and a Differential Technique: Experimental Validation,” Optical Engineering, vol. 51, no. 4, pp. 043602–1–043602–12, 2012. [2] Z. X. Chen, J. Liang, C. Guo, and H. Hu, “Application of the Speckle Technique for Three-Dimensional Deformation Measurement,” Optical Engineering, vol. 51, no. 1, pp. 013604–1–013604–7, 2012. [3] M. M. Frocht, Photoelasticity, vol. 1. J. Wiley, 1941. [4] A. J. Durelli and V. J. Parks, Moiré Analysis of Strain. Prentice-Hall Englewood Cliffs, New Jersey, 1970. [5] T. Kreis, Handbook of Holographic Interferometry: Optical and Digital Methods. Wiley-VCH, 2005. [6] M. A. Sutton, J. J. Orteu, and H. W. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. Springer Verlag, 2009. [7] C. C. Ma and K. M. Hung, “Exact Full-Field Analysis of Strain and Displacement for Circular Disks Subjected to Partially Distributed Compressions,” International Journal of Mechanical Sciences, vol. 50, no. 2, pp. 275–292, 2008. [8] J. N. Butters and J. A. Leendertz, “Speckle Pattern and Holographic Techniques in Engineering Metrology,” Optics and Laser Technology, vol. 3, no. 1, pp. 26–30, 1971. [9] O. Løkberg and K. Høgmoen, “Use of Modulated Reference Wave in Electronic Speckle Pattern Interferometry,” Journal of Physics E: Scientific Instruments, vol. 9, no. 10, pp. 847–851, 1976. [10] Y. H. Huang and C. C. Ma, “Experimental Measurements and Finite Element Analysis of the Coupled Vibrational Characteristics of Piezoelectric Shells,” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 59, no. 4, pp. 785–798, 2012. [11] G. Wang, Y. J. Li, and H. C. Zhou, “Application of the Radial Basis Function Interpolation to Phase Extraction from a Single Electronic Speckle Pattern Interferometric Fringe,” Applied Optics, vol. 50, no. 19, pp. 3110-3117, 2011. [12] C. Y. Chang, S. H. Lin, and C. C. Ma, “High-Resolution Electronic Interferometry for the Measurement of In-Plane Vibration,” Applied Optics, vol. 51, no. 24, pp. 5773–5779, 2012. [13] T. Siebert, H. R. Schubach, and K. Splitthof, “Recent Developments and Applications for Optical Full Field Strain Measurement Using ESPI and DIC,” Proceedings of SPIE, vol. 7997, pp. 79972B, 2010. [14] D. Zhang, X. Zhang, and G. Cheng, “Compression Strain Measurement by Digital Speckle Correction,” Exp. Mech., pp. 39 62-65, 1999. [15] B. K. Bay, “Texture Correlation—a Method for the Measurement of Detailed Strain Distributions within Trabecular Bone,” Journal of Orthopaedic Research, Vol. 13, pp. 258–267, 1995. [16] D. J. Chen, F. P. Chiang, Y. S. Tan, and H. S. Don, “Digital Speckle-Displacement Measurement Using a Complex Spectrum Method,” Applied Optics, Vol. 32, pp. 1839–1849, 1993. [17] G. R. Gaudette, J. Todaro, I. B. Krukenkamp, and F. P. Chiang, “Computer Aided Speckle Interferometry: a Technique for Measuring Deformation of the Surface of the Heart,” Ann. Biomed. Eng., vol. 29, pp. 775–780, 2001. [18] M. Sjodahl, and L. R. Benckert, “Electronic Speckle Photography: Analysis of an Algorithm Giving the Displacement with Subpixel Accuracy,” Applied Optics, Vol. 32, pp. 2278–2284, 1993. [19] M. Sjodahl, “Accuracy in Electronic Speckle Photography,” Applied Optics, Vol. 36, pp. 2875–2885, 1997. [20] R. J. Adrian, “Twenty Years of Particle Image Velocimetry,” Experiments in Fluids, vol. 39, pp. 159–169, 2005. [21] D. J. White, W. A. Take, and M. D. Bolton “Soil Deformation Measurement Using Particle Image Velocimetry (PIV) and Photogrammetry,” Geotechnique, vol. 53, pp. 619–631, 2003. [22] B. Pan, H. M. Xie, L. H. Yang, and Z. Y. Wang, “Accurate Measurement of Satellite Antenna Surface Using Three-Dimensional Digital Image Correlation Technique,” Strain, vol. 45, pp. 194–200, 2009. [23] B. K. Bay, T. S. Smith, D. P. Fyhrie, and M. Saad, “Digital Volume Correlation: Three-Dimensional Strain Mapping Using X-ray Tomography,” Experimental Mechanics, vol. 39, no. 3, pp. 217–226, 1999. [24] Z. Sun, J. S. Lyons, and S. R. McNeill, “Measuring Microscopic Deformations with Digital Image Correlation,” Optics and Lasers in Engineering, vol. 27, no. 4, pp. 409–428, 1997. [25] M. A. Sutton, N. Li, D. Garcia, N. Cornille, J. J. Orteu, S. R. McNeill, H. W. Schreier, X. Li, and A. P. Reynolds, “Scanning Electron Microscopy for Quantitative Small and Llarge Deformation Measurements Part II: Experimental Validation for Magnifications from 200 to 10,000,” Experimental Mechanics, vol. 47, no. 6, pp. 789–804, 2007. [26] C. Franck, S. Hong, S. A. Maskarinec, D. A. Tirrell, and G. Ravichandran, “Three-Dimensional Full-Field Measurements of Large Deformations in Soft Materials Using Confocal Microscopy and Digital Volume Correlation,” Experimental Mechanics, vol. 47, no. 3, pp. 427–438, 2007. [27] X. Li, W. Xu, M. A. Sutton, and M. Mello, “Nanoscale Deformation and Cracking Studies of Advanced Metal Evaporated Magnetic Tapes Using Atomic Force Microscopy and Digital Image Correlation Techniques,” Materials Science and Technology, vol. 22, no. 7, pp. 835–844, 2006. [28] H. A. Bruck, S. R. McNeill, M. A. Sutton, and W. H. Peters, “Digital Image Correlation Using Newton-Raphson Method of Partial Differential Correction,” Experimental Mechanics, vol. 29, no. 3, pp. 261–267, 1989. [29] S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution Image Reconstruction: a Technical Overview,” IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 21–36, 2003. [30] C. Y. Chang, “Application of Image Processing and Computer Cluster in the Development of Full-Field Measurement for Electronic Speckle Pattern Interferometry and Digital Image Correlation,” Doctoral Dissertation, Department of mechanical engineering, National Taiwan University, 2012. [31] C. Y. Chang and C. C. Ma, “Increasing the Computational Efficient of Digital Cross Correlation by a Vectorization Method” Mechanical Systems and Signal Processing, vol. 92, pp. 293-314, 2017. [32] 張景媖，馬劍清,「數位影像相關法應用於跨尺度跨領域靜態及動態全域位移與應變精密量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2013。 [33] 周宛萱，馬劍清,「建構高精度數位影像相關法並應用於土木結構動態系統及奈米材料微系統的變形量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2014。 [34] 簡宸煜，馬劍清,「應用數位影像相關法於土木結構及碳纖維性質與電池表面變化之量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2015。 [35] 彭柏勳，馬劍清,「應用數位影像相關法於機械系統與土木結構之變形及動態特性量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2016。 [36] 陳亮至，馬劍清,「建構立體數位影像相關法之基礎理論並應用於結構靜態與動態三維變形精密量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2016。 [37] 王盛儀，馬劍清,「數位影像相關法於二維軌跡及變形量測和應用於建構立體形貌」, 碩士論文, 機械工程學研究所, 台灣大學, 2017。 [38] 黃右年，馬劍清,「建立即時立體數位影像相關法於三維工程問題的動態量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2017。 [39] 毛英澤，馬劍清,「數位影像相關法於高速主軸即時監測與機械系統動態行為及車輛追跡之跨領域量測」, 碩士論文, 機械工程學研究所, 台灣大學, 2018。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66149 | - |
| dc.description.abstract | 數位影像相關法（digital image correlation, DIC）是一種非接觸式的光學測量技術，可應用於跨尺度與跨領域的工程問題上。其原理為透過數位影像序列分析，追蹤目標表面的灰階特徵，取得位移場、速度、加速度、熱伸長、熱應變等物理量。二維DIC系統使用單相機進行面內量測；三維DIC系統使用雙相機量測三維空間之全場資訊。本文加強了本實驗室自行開發的DIC量測精度與計算速度，提升DIC技術於各項產業實際問題之泛用性，並將其應用於兩項工業界所面臨之議題：複雜交通場景之車輛追蹤與 ISO 9283 標準化機械手臂三維運動性能量測。
在車輛追蹤上，DIC在過往遇到兩個困難點：（1）複雜街景之背景干擾，例如柏油路上所印的交通標誌（2）追蹤目標形貌灰階值變化過大，例如車輛駛近、駛遠與轉彎造成特徵放大縮小或旋轉。以上兩點皆會導致過往DIC系統無法正確追蹤標的物而導致追丟目標。本文提出扣除「平均影像」的手法，去除背景對DIC特徵搜索過程之影響；以及運用「更新模板法」，有效克服特徵劇烈變化而產生的追蹤失效問題。 在三維 DIC的測量能力上，本文提出一種適合DIC三維追蹤的特殊標記，大幅提升影像深度方向的測量性能，避免因深度方向運動時特徵放大縮小變化所導致的追蹤失敗，大幅增加三維DIC技術的量測範圍。將DIC量測技術與三次元量床進行比較後，確立DIC沿影像高度、寬度和深度方向的誤差分別落在0.2%、0.45%和2%。本文也針對三維 DIC量測技術之系統架設和校正鏡頭畸變提出相關校正流程與規範。 在機械手臂的性能量測上，DIC同時滿足手臂大範圍與高精密量測的需求，本文遵循ISO 9283機械手臂性能量測規範，將三維 DIC量測結果與業界常用的雷射追蹤儀比對：絕對精度差異小於0.064 mm，重複性精度差異小於0.01 mm。此結果顯示相較於昂貴的雷射追蹤儀，DIC技術同樣可提供大範圍運動量測，甚至在重複精度上呈現更好的穩健性。 | zh_TW |
| dc.description.abstract | Digital Image Correlation (DIC) is a non-contact measuring technique for diverse engineering problems. By analyzing image series and tracking a region of interest (ROI) target, DIC can derive physical quantities such as displacement and velocity. In this thesis, the accuracy of our self-developed DIC system (in MATLAB) is improved; then it is applied to two different problems: vehicle tracking in complex traffic scenarios and 3D standardized robotic arm performance evaluation according to ISO 9283.
In vehicle tracking, two obstacles were identified and overcame in this thesis: (1) unwanted interference in image background (e.g. traffic markings on pavement) and (2) ROI features, cars in this case, change drastically as cars turn or drive near/away. Background interference is eliminated by utilizing an “average image”. ROI change is overcome by applying the “updating template method” proposed in this thesis. In terms of 3D DIC, measuring capability is improved greatly, especially along depth direction measurements. ROI change for motions in depth direction is overcome by using a special tracking marker (2×2 checkerboard). By comparing with a coordinate measuring machine, measuring error is found to be 0.2, 0.45 and 2% along height, width and depth direction of DIC camera system. This thesis also provides standardized guidelines for camera setup and lens calibration processes. In robotic arm performance evaluation, DIC results match well with laser tracker, a prominently used but expensive machine for robotic arm calibration. Absolute precision differences were less than 0.064 mm, and repeatability precision differences were less than 0.01 mm. Between experiments, DIC outperforms laser tracker in repeatability. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:23:34Z (GMT). No. of bitstreams: 1 ntu-109-R05522505-1.pdf: 21088688 bytes, checksum: 31d8e524c814eb14b0ada62d68b6133a (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Table of Contents
誌謝 I 摘要 III Abstract V Table of Contents VII List of Tables XI List of Figures XIII Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Review 4 1.3 Chapters Overview 7 Chapter 2 Digital Image Correlation 11 2.1 Introduction to DIC 11 2.1.1 Working Principles 11 2.1.2 CCPF: From Pixel to Subpixel accuracy 14 2.1.3 2D and 3D DIC Analysis 16 2.2 Equipment Introduction 21 2.2.1 Camera 22 2.2.2 Lens 23 Chapter 3 DIC Algorithm Improvements 37 3.1 Adapt to ROI Changes by Updating Template 38 3.1.1 Updating Template vs. Constant Template 38 3.1.2 Experiment 1: ROI Rotation 39 3.1.3 Experiment 2: ROI Size Change 41 3.2 Calculation Speedup by Utilizing GPU 44 3.2.1 Arithmetic Complexity in A General DIC Case 44 3.2.2 Sequential Computing vs. Parallel Computing 46 3.2.3 Experiment Results 47 3.3 Redesigning MATLAB Plotting Code 49 3.3.1 Original Code vs. Redesigned Code 49 3.3.2 Summary 51 Chapter 4 Vehicle Tracking with DIC 73 4.1 Difficulties in Vehicle Tracking 73 4.1.1 Background Interference 74 4.1.2 ROI change 74 4.2 Solutions 75 4.2.1 Eliminating Background Interference 75 4.2.2 Updating Template to Adapt to ROI Change 78 4.3 Successful Results in Complicated Traffic 78 4.3.1 Single Car Turning and Enlarging 80 4.3.2 Two Cars Driving Away Shrinking 81 4.3.3 Simultaneously Tracking 28 Vehicles 82 4.4 Summary 84 Chapter 5 Improving 3D DIC Accuracy 117 5.1 Standardize System Setup Workflow 117 5.2 Use 2×2 Checkerboard as Tracking Marker 120 5.3 Calibration Images 120 5.4 Verify Accuracy by Comparing with CMM 122 5.5 Summary 126 Chapter 6 Standardized Robotic Arm Performance Evaluation 147 6.1 Performance Evaluation and Testing Methods According to ISO 9283 147 6.1.1 Distance Accuracy 147 6.1.2 Absolute Precision and Repeatability Precision 148 6.2 HIWIN Robotic Arm 155 6.2.1 Distance Accuracy 155 6.2.2 Absolute Precision and Repeatability Precision 157 6.3 PMC Robotic Arm (DIC vs. Laser Tracker) 159 6.3.1 Distance Accuracy 160 6.3.2 Absolute Precision and Repeatability Precision 162 6.4 Summary 164 Chapter 7 Conclusion and Future Works 213 7.1 Conclusion 213 7.2 Future Works 216 Reference 219 | |
| dc.language.iso | en | |
| dc.subject | 鏡頭校正 | zh_TW |
| dc.subject | ISO 9283 | zh_TW |
| dc.subject | 數位影像相關法 | zh_TW |
| dc.subject | 車輛追蹤 | zh_TW |
| dc.subject | 三維測量 | zh_TW |
| dc.subject | 非接觸式測量 | zh_TW |
| dc.subject | 機械手臂校正 | zh_TW |
| dc.subject | vehicle tracking | en |
| dc.subject | robotic arm calibration | en |
| dc.subject | 3D measurement | en |
| dc.subject | ISO 9283 | en |
| dc.subject | lens calibration | en |
| dc.subject | non-contact measurement | en |
| dc.subject | digital image correlation | en |
| dc.title | 提升數位影像相關法的量測精度並應用於車輛追蹤與機械手臂的三維量測 | zh_TW |
| dc.title | Improving the Accuracy of Digital Image Correlation and Applying it to Vehicle Tracking and 3D Measurements of Robotic Arms | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳亮嘉(Chen Liang-Chia),蔡孟勳(Meng-Shiun Tsai),林沛群(Pei-Chun Lin),張敬源(Ching-Yuan Chang) | |
| dc.subject.keyword | 數位影像相關法,非接觸式測量,車輛追蹤,鏡頭校正,ISO 9283,三維測量,機械手臂校正, | zh_TW |
| dc.subject.keyword | digital image correlation,non-contact measurement,vehicle tracking,lens calibration,ISO 9283,3D measurement,robotic arm calibration, | en |
| dc.relation.page | 223 | |
| dc.identifier.doi | 10.6342/NTU202000280 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-02-12 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-109-1.pdf 未授權公開取用 | 20.59 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。