Master Thesis - Fachbereich Informatik

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1.2. PROBLEM STATEMENT 3 hazards, risk of explosion, x-rays, radioactive material, loud noise levels, etc. [7]. On the other hand, in applications that require aseptic conditions as in the food or pharmaceutical industry, a human operator can be a ‘polluter’ as a source of dirt particles (hair, danders, bacteria, etc.). In this case, a MV system is a clean alternative. Machines usually exceed humans in all kinds of accurate vision-based measurements. Human vision performs well in comparing objects and in detecting differences for example in shape, color or texture [27]. Large deviations can be detected quickly. As the difference is getting smaller, however, the time of inspection increases or the deviation can not be detected at all without technical tools. With respect to the task considered in this thesis, a human is not able to determine the length of an object at sub millimeter precision just by looking at it. Manual measurements are slow and not practicable in line production if 100% control is desired, and can thus be used only for random inspection of few objects. MV systems on the other hand can measure the length (or other features) of an object without contact up to nm precision - depending on the optical system and the size of the object [16]. Furthermore, humans soon reach limits, if the number of objects to inspect per minute increases significantly. Many manufacturing processes are so fast that the human eye has problems to even perceive the objects, not to mention the ability to accomplish any inspection task. MV systems, however, can handle several hundred objects per minute with high accuracy. Although MV systems have many advantages for manufacturers, there are also drawbacks. Usually, a MV system is designed and optimized for a specific task in a constraint environment. If the requirements of the application change, the system has to be adapted, which can be difficult and expensive [7]. Furthermore, the system can be sensitive to a lot of influences of the (industrial) environment like heat, humidity, dirt, dust, or ambient lighting. Respective precautions have to be taken to protect the system. Finally, like in general in automation, vision systems that exceed the power of a human at some specific task, replace human operators and will therefore supersede mostly low-skilled jobs in the future. Addressing this problem in more detail is outside the scope of this thesis. 1.2. Problem Statement A large variety of heat shrink tubes of different sizes, material and shrinking properties is available on the market. The focus in this thesis will be on the DSG- Canusa DERAY- SPLICEMELT series. These tubes are commonly used for insulation of cable splices in the automotive industry (see Figure 1.1 for an example). A film of hotmelt adhesive inside the heat shrink tubes provides a waterproof sealing around the splice after shrinking. In addition, the DERAY- SPLICEMELT series shows a strong resistance against thermal, chemical and mechanical strains. The easy and fast handling allows for an application in series production. Accordingly, if the heat shrinking is performed in an automated fashion, the accuracy demands increase. In production, the heat shrink tubes are cut into lengths from a continuous tube. During this process, however, deviations from a specific target length can occur. In terms of quality assurance, any deviations above a tolerable level must be detected so that failings can be sorted out.
4 CHAPTER 1. INTRODUCTION (a) (b) (c) Figure 1.1: Application of a transparent heat shrink tube of type DERAY- SPLICEMELT. After shrinking the heat shrink tube provides a robust, waterproof insulation of the cable splice. (Source: DSG-Canusa) Property Attributes Color transparent, black Length 20-100mm Diameter 6, 8, 12mm Table 1.1: Range of tube types considered in this thesis. Delivering defectives must be avoided at highest priority to satisfy the customer and to retain a good reputation. In this context, tolerable failure rates are specified in parts per million. Rejected goods can be very expensive. Up to now, length measurements have been performed manually by a human operator. This has several drawbacks. First, only random samples can be controlled by hand, since 10 parts per second and more considerably exceed the human capabilities. Furthermore, one operator is busy doing the monotone measuring task at one machine and can not be deployed to other tasks. This leads to a low effective productivity. In practice, more than one production line that cuts the heat shrink tubes into lengths is running in parallel, requiring even more human resources which is very expensive. In addition, there is always a non-negligible possibility of subjective errors when human operators carry out the inspections - they also show symptoms of fatigue over time in this highly repetitive task. The measuring quality varies detectable between morning and late shift. In this thesis work a machine vision inspection system is developed that is able to replace the human operator at this particular measuring task allowing for a reliable 100% control. 1.3. Requirements The system must cover a range of tube types, differing in diameter, length or material properties. An overview of the variety of tube types can be found in Table 1.1. ThetwomainclassesofDERAY-SPLICEMELT heat shrink tubes considered in this thesis are black or transparent in color - transparent tubes cover about 70% of the production. Unlike black tubes, the transparent ones are translucent and appear slightly yellowish or reddish due to a film of hotmelt adhesive inside the tube. Most tubes have a printing on the surface that can consist of both letters and numbers (e.g. DSG2). Since this printing is plotted onto the continuous tube before being cut into
Page 1 and 2: Fachbereich Informatik Department o
Page 4: Acknowledgments First of all, I wou
Page 8 and 9: Contents Acknowledgments iii Abstra
Page 10 and 11: Contents ix 5.3.7. TubeLength .....
Page 12 and 13: List of Tables 1.1. Rangeoftubetype
Page 14 and 15: List of Figures 2.1. AccuracyandPre
Page 16 and 17: 1. Introduction Heat shrinkable tub
Page 20 and 21: 1.4. RELATED WORK 5 Length [mm] Tol
Page 22 and 23: 1.5. THESIS OUTLINE 7 The vision pa
Page 24 and 25: 2. Technical Background 2.1. Visual
Page 26 and 27: 2.1. VISUAL MEASUREMENTS 11 (a) Fig
Page 28 and 29: 2.1. VISUAL MEASUREMENTS 13 Figure
Page 30 and 31: 2.1. VISUAL MEASUREMENTS 15 � xd
Page 32 and 33: 2.2. ILLUMINATION 17 tubes alternat
Page 34 and 35: 2.2. ILLUMINATION 19 effect of comp
Page 36 and 37: 2.3. EDGE DETECTION 21 i 2 i 1 0 (a
Page 38 and 39: 2.3. EDGE DETECTION 23 N × 1 kerne
Page 40 and 41: 2.3. EDGE DETECTION 25 These operat
Page 42 and 43: 2.3. EDGE DETECTION 27 (a) 0 ◦ (b
Page 44 and 45: 2.4. TEMPLATE MATCHING 29 accuracy
Page 46 and 47: 3. Hardware Configuration This chap
Page 48 and 49: 3.2. CAMERA SETUP 33 3.2. Camera se
Page 50 and 51: 3.2. CAMERA SETUP 35 eachcolorchann
Page 52 and 53: 3.2. CAMERA SETUP 37 Figure 3.4: Co
Page 54 and 55: 3.2. CAMERA SETUP 39 adjusting scre
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Page 58 and 59: 3.3. ILLUMINATION 43 f V 12mm 129mm
Page 60 and 61: 3.3. ILLUMINATION 45 (a) (b) (c) Fi
Page 62 and 63: 3.3. ILLUMINATION 47 (a) (b) Figure
Page 64 and 65: 3.4. BLOW OUT MECHANISM 49 A light
Page 66 and 67: 4. Length Measurement Approach Whil
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4.2. MODEL KNOWLEDGE AND ASSUMPTION
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4.3. CAMERA CALIBRATION 59 4.2.7. B
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4.3. CAMERA CALIBRATION 61 Figure 4
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4.3. CAMERA CALIBRATION 63 is compu
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4.4. TUBE LOCALIZATION 65 (a) (b) F
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4.4. TUBE LOCALIZATION 67 � 1 Psm
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4.4. TUBE LOCALIZATION 69 Global Th
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4.4. TUBE LOCALIZATION 71 but also
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4.4. TUBE LOCALIZATION 73 Global Re
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4.5. MEASURING POINT DETECTION 75 f
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4.5. MEASURING POINT DETECTION 77 T
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4.5. MEASURING POINT DETECTION 79 -
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4.5. MEASURING POINT DETECTION 81 0
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4.5. MEASURING POINT DETECTION 83 o
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4.5. MEASURING POINT DETECTION 85 (
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4.5. MEASURING POINT DETECTION 87 w
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4.6. MEASURING 89 side of the tube,
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4.6. MEASURING 91 fcor(x) =−(c1x
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4.7. TEACH-IN 93 inthemeasuringplan
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4.7. TEACH-IN 95 The pair of a pixe
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5. Results and Evaluation 5.1. Expe
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5.1. EXPERIMENTAL DESIGN 99 between
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5.1. EXPERIMENTAL DESIGN 101 where
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5.1. EXPERIMENTAL DESIGN 103 Ground
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5.2. TEST SCENARIOS 105 enough, the
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5.3. EXPERIMENTAL RESULTS 107 The t
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5.3. EXPERIMENTAL RESULTS 109 Detec
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5.3. EXPERIMENTAL RESULTS 111 Lengt
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5.3. EXPERIMENTAL RESULTS 113 GTD [
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5.3. EXPERIMENTAL RESULTS 115 gray
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5.3. EXPERIMENTAL RESULTS 119 (a) (
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5.3. EXPERIMENTAL RESULTS 121 (a) (
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5.3. EXPERIMENTAL RESULTS 125 Total
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5.3. EXPERIMENTAL RESULTS 127 Color
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5.3. EXPERIMENTAL RESULTS 129 Task
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5.4. DISCUSSION AND FUTURE WORK 131
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6. Conclusion In this thesis a func
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Appendix 135
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138 APPENDIX A. PROFILE ANALYSIS IM
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140 APPENDIX A. PROFILE ANALYSIS IM
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142 APPENDIX B. HARDWARE COMPONENTS
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144 APPENDIX B. HARDWARE COMPONENTS
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146 Bibliography [18] C. Demant, B.
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148 Bibliography [51] K.K. Pingle.
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Master Thesis - Fachbereich Informatik

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