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研究生： 蘇榮智
Jung-Chic Su
論文名稱： 自動化光學檢測之研究
A Study of Automated Optical Inspection
指導教授： 唐永新
Yeong-Shin Tarng 　
口試委員: 楊宏智
Hong-Tsu Young
廖運炫
Yun-Shiuan Liao
許新添
Hsin-Teng Hsu
鍾國亮
Kuo-Liang Chung
林原慶
Yuan-Ching Lin
傅光華
Kuang-Hua Fuh
陳亮嘉
Liang-Chia Chen
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： 2006
畢業學年度： 94
語文別： 英文
論文頁數： 152
中文關鍵詞： 測直線 、測圓 、缺陷分類 、結構光 、刀腹磨耗 、砂輪 、微型鑽頭 、喇叭震模 、變阻器 、測邊 、自動化光學檢測 、機器視覺
外文關鍵詞： Machine vision, Automated optical inspection
相關次數： 點閱：323  下載：1 
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  •  上一筆

    • 本論文主要的目的是利用機器視覺發展一自動化檢測系統，以提昇製造廠品質管制能力。本研究達成三個項目(1)檢測元件的位置尺寸是否正確，(2)元件的外型是否在接受範圍內，(3)元件的外觀是否有瑕疵。自動化光學檢測包含下列步驟: 擷取影像，影像處理，特徵擷取和判決。本文使用四個應用實例來印證所提出的特徵擷取的方式及對產品瑕疵的辨識，這四個實例分別為(A)二維的輪廓檢測-主要的目的量測砂輪磨耗量的改變，藉此來判別砂輪是否需要再重磨，(B)使用測邊的方式量測微型鑽頭的刀腹磨耗，(C)三維的曲面檢測-使用結構光之照明方式檢測喇叭震模的高度及同心度是否符合要求，及(D)變阻器外觀檢測，主要從影像中擷取此元件特徵，再將這些特徵透過ANFIS訓練，訓練完畢後再對Varistors的缺陷加以分類。由這四個應用實例顯示，本研究所發展的機器視覺系統具有很良好重現精度，及優越瑕疵分類能力。

    This thesis presents a machine vision system for automated inspection of industrial parts for post-manufacturing quality control. The aims of the visual inspection process are to determine whether all components have correctly located dimensions, whether all components shaped within acceptable tolerances, to check for structural damage, and to inspect the surface quality of components for defects. Automated optical inspection processes involve the following sequence of steps: image acquisition, image processing, exaction feature, and decision-making. All this must be accomplished while ensuring that overall completion time is comparable to that of a human inspector. We describes, with applications: (1) a two-dimensional contour measurement algorithm using back lighting to solve the problem of measuring wear on a grinding wheel, (2) an automated flank wear measurement scheme with edge detection based on machine vision for a microdrill (3) three-dimensional shape measurement using a structured illumination method to measure the dimensions of a loudspeaker cone, and (4) an automated visual system for inspecting the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). The experimental results show that the machine vision system can inspect or classify these components in a highly consistent and accurate manner, and can be a valuable tool for ensuring product quality.

    Contents Abstract I 中文摘要 II Acknowledgements III List of Tables XIV Chapter 1 Introduction 1 1.1 Background 1 1.2 Related researches 2 1.3 The study of motivations and purposes 3 1.4 Contributions 4 1.5 Organization 5 Chapter 2 Machine Vision 6 2.1 A typical machine vision system 6 2.2 Illumination 7 2.2.1 Lighting technology 8 2.3 Image sensors 9 2.3.1 Point scanning 10 2.3.2 Line scanning 11 2.3.3 Area scanning 12 Chapter 3 Automated Visual Inspection Fundamentals 14 3.1 Four types of inspection on industrial vision 15 3.1.1 Inspection of dimensional quality 17 3.1.2 Inspection of surface quality 18 3.1.3 Inspection of structural quality 19 3.1.4 Inspection of accurate operational quality 20 3.2 Feature extraction 20 3.3 Edge detection 21 3.4 Two-dimensional edge detection 24 3.5 Line detection 25 3.5.1 Mean square distance 25 3.5.2 Least-squares method 26 3.6 Circle detection 26 Chapter 4 Fuzzy and Neural Network 30 4.1 Fuzzy logic fundamentals 30 4.1.1 Membership function 31 4.1.2 Fuzzy inference models 33 4.1.3 Sugeno fuzzy models (TSK fuzzy model) 34 4.1.4 Tsukamoto fuzzy models 36 4.2 Artificial neural networks (ANNs) 38 4.2.1 Backpropagation neural network 40 4.2.2 Error backpropagation 41 4.2.3 Hybrid learning rule: combining steepest descent and LSE 42 4.2.4 Training pattern modeling 44 Chapter 5 Case Study 1 ─ Measuring Wear of the Grinding Wheel Using Machine Vision 46 5.1 Introduction 46 5. 2 Principle of measurement 48 5.2.1 Experimental Set-up 49 5.2.2 Illumination-Backlighting 49 5.2.3 Calibration 50 5.3 Experimental procedures 52 5.4. Results and discussion 59 5.5. Conclusions 63 Chapter 6 Case Study 2 ─An Automated Flank Wear measurement of Microdrills Using Machine Vision 65 6.1 Introduction 65 6.2 Experimental set-up 67 6.3 Measuring method 69 6.3.1 Measuring procedures 70 6.3.2 Measuring the flank wear area 72 6.3.3 Measuring the average wear height VBave 73 6.3.4 Measuring maximum wear height VBmax 74 6.4 Measurement results and discussion 74 6.5 Conclusions 80 Chapter 7 Case Study 3 ─ Application of the Structured Illumination Method for Automated Optical Inspection of the Loudspeaker Cones 81 7.1 Introduction 81 7.2 Set-up of an automated visual inspection system 84 7.3 Measurement process 87 7.3.1 Image acquisition 88 7.3.2 Triangulation-based and calibration 88 7.3.3 Stripe identification and feature extraction using edge detection 90 7.3.4 Measuring the concentricity of the cone 90 7.3.3 Measuring the height of the cone 92 7.4 Results and discussion 96 7.5 Conclusions 103 Chapter 8 Cause Study 4 ─ Automated Visual Inspection for Surface Appearance Defects of Varistors Using an Adaptive Neuro- Fuzzy Inference System 104 8.1 Introduction 104 8.2. Adaptive Neuro-Fuzzy Inference System (ANFIS) 106 8.2.1 ANFIS Architecture 107 8.2.2 ANFIS learning algorithm 109 8.3 Automated visual inspection system 110 8.3.1 Structure 111 8.3.2 Feature extraction method 112 8.3.3 Image Mask 115 8.3.4 Unwrap 116 8.3.5 Feature types 117 8.4. Modeling using ANFIS 120 8.4.1 Establish a Sugeno fuzzy model 120 8.4.2 Training the ANFIS 121 8.4.3 Assignment using the minimum orthogonal distance method 122 8.5 Results and discussion 122 8.6 Conclusion 127 Chapter 9 Conclusions and Future Work 128 References 130 Appendix B 141 B.1 Training mode 141 B.2 Inspection mode 142 Appendix C 143 C.1 Mechanism 143 C.2 Image acquisition module 146 C.3 Controller 146 C.4 Hierarchical control 147 C.5 Communication 149 C.6 Light meter 150 About author 151 List of Figures Fig. 2.1 A typical machine vision system. 7 Fig. 2.2 Variability in appearance due to differences in illumination. 8 Fig. 2.3 Various illumination technologies (a) directed lighting (b) back lighting (c) vertical lighting (d) structured lighting. 9 Fig. 2.4 Point Scanner 10 Fig 2.5 Image acquisition using a linear sensor strip 12 Fig. 2.6 Area Scanning 13 Fig. 3.1 One-dimensional and two-dimensional edge detection. 24 Fig. 3.2 Determine search region of circular detector by six control parameters. 27 Fig. 4.1 Fuzzy set with three bell-shaped membership functions 33 Fig. 4.2 A two-input first-order Sugeno fuzzy model 35 Fig. 4.3 A first-order Sugeno fuzzy model with two nonlinear inputs and one linear output. 36 Fig. 4.4 The Tsukamoto fuzzy model. 37 Fig. 4.5 Single-input/output Tsukamoto fuzzy model (a) antecedent MFs; (b) consequent MFs; (c) each rule’s output cuve; (d) overall input-output curve. 38 Fig. 4.6 Feedforward neural network. 40 Fig. 4.7 Activation functions for backpropagation MLPs: (a) logistic function; (b) hyperbolic function; (c) identity function. 41 Fig. 5.1 A specimen is ground to yield a gap with the contour of the grinding wheel. 48 Fig. 5.2 Set-up of a measuring system 49 Fig. 5.3 System set-up based on a back lighting 50 Fig. 5.4 Standard circle with Dt=0.7mm to be calibrated using the least square method. 52 Fig. 5.5 The centers of the two arcs must align on the X-axis. 52 Fig. 5.6 The specimen image is captured using the back lighting method, (a)The angel between the straight edge of the two shoulder width perpendicular to the horizontal axis,θ0, is 85.2.(b) The image is rotated an angle of 4.8 such that the straight edge of the specimen becomes perpendicular to the horizontal axis. 54 Fig. 5.8 Calculate as the starting point for measurement. 56 Fig. 5.9 (a) 90 points obtained by circular edge detection in the upper quadrant,(b) 90 points obtained by circular edge detection in the lower quadrant. 59 Fig. 5.10 Repeatability of the measurement: (a) Radius R1, (b) Radius R2, (c) Radius , (d) Radius (e) Radius (f) Radius (g)Radius (h)Radius 62 Fig. 6.1 Schematic diagram of a microdrill 66 Fig. 6.2 Experimental set-up for the measuring flank wear using the toolmaker microscope. 69 Fig. 6.3 The images of cutting plane with flank wear. (a) Rotate the image to horizontal. (b) The search region is confined by the red rectangle to avoid noise effect in the measurement results. 71 Fig. 6.4 The contour of cutting plane (a) original contour of the cutting plane (b) the worn-out of cutting plane (c) original image with edge detection search lines (d) the image of flank wear with edge detection search lines. 72 Fig. 6.5 Flank wear measurement results. 77 Fig. 6.6 The height of the cutting plane change along X-axis in hole-drilling test. 78 Fig. 6.7 The curves of the flank area wear. 79 Fig. 6.8 The curves of the average wear height. 79 Fig. 6.9 The curves of the maximum wear height. 80 Fig. 7.1 The appearance of the loudspeaker cone, (a) the cone of loudspeaker consists of the internal and external two layers of elastic materials, (b) standard sample (c) the inner and outer two layers are bound with an angular deviation. 82 Fig. 7.2 A non-contact 3D measurement system for inspecting the loudspeaker cone. 85 Fig. 7.3 Structure of the measurement system 87 Fig. 7.4 Triangulation geometry and calibration 89 Fig. 7.5 Measuring the concentricity using three images 92 Fig. 7.6 Measuring the height using a laser stripe, (a) reference line is plotted in red line, search area is restricted in green frame, and edge points are plotted with red mark, and (b) tracking method uses two edge-detection lines to detect the change grey intensity. 95 Fig. 7.7 The profile of the loudspeaker cone 96 Fig. 7.8 Using a single image computes the internal and external centers of the circle to estimate the concerntricity by fitting the circle using the least square method. 100 Fig. 7.9 Measurement of the results uses triangulation with laser stripe to compute the height of the loudspeaker cone at different position with different slices of the range image. (a) Measuring the height of the loudspeaker cone on the right hand region, (c) on the middle region, (e) on the left region, and (b), (d), and (f) with a curve of the shape profile are piloted along the Y-axis. 101 Fig. 7.10 Repeatability of the height measurements 102 Fig. 8.1 Six types of the ring varistors 106 Fig. 8.2 ANFIS architecture for a two-input, two-rule Sugeno FIS (A square represents the adaptive note and a circle denotes the fixed note). 107 Fig. 8.3 The following chart of the defect classification. 111 Fig. 8.4 Fuzzy rule architecture of ANFIS model with feature extraction. 112 Fig. 8.5 Feature extraction (a) image mask, (b) unwrapped, (c) two-dimensional edge detection, (d) summing up the number edge points (red points) in rectangular region with d width. (Red points mean where the edge detected using two-dimensional edge detection). 113 Fig. 8.7 (c) Front qualify pattern (d) broken pattern are histogram of discrete feature values. 115 Fig. 8.8 (e) Back qualify pattern (f) cracked pattern are histogram of discrete feature values. 115 Fig. 8.9 (a) Circular edge detection (b) image mask (c) center of circle of the image mask and center overlap of circular detector (d) extract masked region. 116 Fig. 8.10 Discrete feature values are defined in a histogram. 118 Fig. 8.11 ANFIS architecture for a four-input Sugeno model with six rules. 120 Fig.8.12 Adaptation of step sizes from initial value 0.11 (right most) to final value 5.73 . 123 Fig. 8.13 RMSE curves for ANFIS 124 Fig. 8.14 (a),(b) MFs before learning; (c), (d) MFs after learning in input 1 ( ) and input 2( ). 125 Fig. 8.15 (a),(b) MFs before learning; (c), (d) MFs after learning in input 3 ( ) and input 4( ). 125 Fig C.1 Relationship chart of training mode and inspection mode 143 Fig. B-1 The structure of a varistors inspection system 145 Fig. B-3 Schematic diagram of the hierarchical control in the automated visual inspection 149 List of Tables Table 3.1 the features of inspected products 16 Table 5.1 The results of various factors cases errors test 63 Table 6.1 The relationship between the height of cutting plane and the number of hits. 75 Table 7.1 Measurement results using multi-image with local feature test repeatability of the concentricity. 97 Table 7.2 Measurement results using only a single image test repeatability of the concentricity 99

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