Automated Quality Inspection of Bottles Crates Through Image Processing

Automated Qual i ty Inspect ion

of Bott les Crates through Image Process ing

Graduat ion Pro ject Documentat ion

Advisor Dr. Nathir Rawashdeh Author: Ali K. Tillawi - 2008102013 School of Applied Technical Sciences / Mechatronics Engineering

Summer, 2013

Abstract

As they say a picture equals thousand words, and for computer based vision systems it is much more.

In a trail to improve the Industrial Production Index at Coca Colas factory, the outcome should be improved, which includes decreasing the outputs defects. This project is to inspect the outcome of the production line of the Coca Cola glass bottles using image processing to detect if the crates have all their bottles from the same beverage, no bottles are missing, no extra bottles are added, and that all the bottles are sitting in their places.

This project was done with two approaches, using an open-source program to program the algorithm and using LABVIEW and Vision builder to program the algorithm.

The camera is to grab the frames and do the processing needed to detect the defects by applying these methods: Color processing, Blob tracking, images segmentation and contours analysis.

After the processing is done the algorithm was able to count the number of bottles and give a signal if a defect was detected.

The project was built on real-life reading and is to be implemented on a real-life application.

Ded icat ion !I dedicate this project to my family for providing all the help and the support to finish this work. Also for Dr. Nathir Al-Rawashdeh for bearing with me and being supportive for all my actions. And last but not least to Eng. Fadi Al Madi and the Coca Cola factory for providing me with the data, means, help, knowledge and all that I needed since the day they allowed me to visit their factory.!

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Tab le o f Contents

1 CHAPTER 1 : INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1 .1 OBJECTIVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1 .2 APPROACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 CHAPTER TWO: L ITERATURE REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 .1 DEFIN IT IONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 .2 D IG ITAL IMAGE PROCESSING TECHNIQUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 IMAGE REPRESENTATION ................................................................................................................................... 8 2.2.2 IMAGE PREPROCESSING ..................................................................................................................................... 8 2.2.2.1 Scaling ...................................................................................................................................................... 8 Magnification ............................................................................................................................................................. 8 Reduction .................................................................................................................................................................. 9 2.2.2.2 Rotation .................................................................................................................................................... 9 2.2.2.3 Mosaic ...................................................................................................................................................... 9 2.2.3 IMAGE ENHANCEMENT .................................................................................................................................... 10 Point wise spatial filter ........................................................................................................................................... 10 2.2.3.1 Contrast Stretching ................................................................................................................................ 10 2.2.3.2 Noise Filtering ....................................................................................................................................... 11 2.2.3.3 Histogram Modification .......................................................................................................................... 11 2.2.3.4 Edge Detection and edge enhancement ............................................................................................... 11 2.2.4 IMAGE ANALYSIS ........................................................................................................................................... 12 2.2.4.1 Image Segmentation ............................................................................................................................. 12 2.2.4.2 Classification .......................................................................................................................................... 12 2.2.5 IMAGE RESTORATION ..................................................................................................................................... 13 2.2.6 IMAGE COMPRESSION ..................................................................................................................................... 13 2.2.7 IMAGE RECONSTRUCTION FROM PROJECTIONS .................................................................................................... 14 2 .3 USED-ALGORITHM SPECIF ICAT ION DETAILS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.1 COLOR CONVERSION ...................................................................................................................................... 15 2.3.2 FINDING CONTOURS ....................................................................................................................................... 16 2.3.3 MORPHOLOGICAL TRANSFORMATIONS ............................................................................................................... 16 2.3.4 IMAGE LOGICAL AND ARITHMETIC OPERATIONS .................................................................................................... 18 2.3.4.1 Logical operations: ................................................................................................................................ 18 2.3.4.2 Arithmetic operations ............................................................................................................................ 19

3 CHAPTER THREE: METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 .1 CASE STUDY: PACKAGING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3 .2 ALGORITHM DESCRIPT ION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2.1 OPENCV DETAILED ALGORITHM EXPLANATION/TRACE ......................................................................................... 21 3.2.1.1 Model Assembly: .................................................................................................................................... 22 3.2.2 NI VISION BUILDER PROGRAM EXPLANATION ...................................................................................................... 23 3.2.2.1 Model Assembly ..................................................................................................................................... 23

4 CHAPTER FOUR: RESULTS AND D ISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4 .1 OPENCV RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.1 THE BEVERAGE COCA COLA ............................................................................................................................ 24 4.1.1.1 Case 1: A Normal full crate .................................................................................................................... 25 4.1.1.2 Case 2 ................................................................................................................................................... 26

4.1.1.3 Case 3 ................................................................................................................................................... 27 4.1.2 THE OTHER BEVERAGES (FANTA, SPRITE AND PROMOTIONS) .............................................................................. 28 4.1.3 PERFORMANCE AND PRECESSION ..................................................................................................................... 29 4 .2 V IS ION BUILDER RESULTS (SECONDARY METHOD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.1 THE BEVERAGE COCA COLA ............................................................................................................................ 30 4.2.1.1 Case 1 ................................................................................................................................................... 30 4.2.1.2 Case 2 ................................................................................................................................................... 31 4.2.1.3 Case 3 ................................................................................................................................................... 32 4.2.2 THE OTHER BEVERAGES (FANTA, SPRITE AND PROMOTION) ................................................................................ 33

5 CHAPTER F IVE : CONCLUSIONS AND RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6 CHAPTER S IX : REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

7 APPENDENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

7 .1 APPENDIX A : MY EXPERIENCE AT COCA-COLA FACTORY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 7.1.1 CO2 PRODUCTION FACILITY ............................................................................................................................. 36 7.1.1.1 ABSORPTION AND DESORPTION ........................................................................................................... 36 7.1.1.2 CO2-COMPRESSION, GAS PURIFICATION AND GAS DRYING ................................................................... 36 7.1.1.3 CO2-LIQUEFACTION AND STORAGE ..................................................................................................... 36 7.1.2 PLASTIC BOTTLES MANUFACTURING .................................................................................................................. 37 7.1.3 BEVERAGE SODA CANS ................................................................................................................................... 38 7.1.4 BEVERAGE PLASTIC BOTTLES (FILLING SEALING, LABELING AND PACKAGING) ........................................................... 38 7.1.5 BEVERAGE GLASS BOTTLES (CLEANING, INSPECTING, SEALING, PACKAGING): ........................................................... 38 7 .2 APPENDIX B : GUIDE TO INSTALL OPENCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 7 .3 APPENDIX C : COMPARISON BETWEEN USED AND OTHER OPTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 7.3.1 HARDWARE .................................................................................................................................................. 47 7.3.1.1 Hardware Solution 1 .............................................................................................................................. 47 7.3.1.2 Hardware Solution 2 .............................................................................................................................. 51 7.3.2 SOFTWARE ................................................................................................................................................... 55 7.3.2.1 LABVIEW ................................................................................................................................................ 55 LABVIEW Features .................................................................................................................................................. 57 7.3.2.2 VISION BUILDER for Automated Inspection ........................................................................................... 57 7.3.2.3 OPENCV ................................................................................................................................................. 58 7.3.2.4 A Comparison between LABVIEW, Vision Builder & OPENCV ................................................................. 59 7 .4 APPENDIX D : SOURCE CODE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 7 .5 APPENDIX E : FLOW CHART D IAGRAM (OPENCV ALGORITHIM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Automated Quality Inspection of Bottles Crates through Image Processing Ali K. Tillawi 2008102013 Year: 2013

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Tab le o f F igures

Figure 1: Image Representation Example: a part of the gray level image is taken, ................................................. 8 Figure 2: Image Magnification ................................................................................................................................... 8 Figure 3: Image size Reduction ................................................................................................................................. 9 Figure 4: Rotation Transforms .................................................................................................................................. 9 Figure 5: Point wise spatial filter example .............................................................................................................. 10 Figure 6: Noise removal ......................................................................................................................................... 11 Figure 7: Histogram equalization example ............................................................................................................. 11 Figure 8: Edge detection methods Example: Original - Laplacian - Sobel ............................................................. 12 Figure 9: Image classification example .................................................................................................................. 13 Figure 10: image de-blurring example ................................................................................................................... 13 Figure 11: Image compression example ................................................................................................................ 13 Figure 12: Reconstruction from projections ........................................................................................................... 14 Figure 13: The multiple thresholding methods in OpenCV ..................................................................................... 15 Figure 14: The HSV color mode representation. ................................................................................................... 16 Figure 15: Morphological transforms effect on a gray level image ........................................................................ 17 Figure 16: Morphological transforms effect on a binary image ............................................................................. 18 Figure 17: Effect of logical operations on images. ................................................................................................. 18 Figure 18: The most frequent errors on the production line. Where; .................................................................... 20 Figure 19: The crate on the production line (good product) ................................................................................. 21 Figure 20: System assembly model to be utilized with OpenCV ............................................................................. 22 Figure 21: A CAD drawing of the adjustable arm where the camera should be mounted on. ............................... 22 Figure 22: A model assembly of the system to be utilized with NI vision builder .................................................. 23 Figure 23: first scenario after applying the algorithm (OpenCV) ........................................................................... 25 Figure 24: second scenario after applying the algorithm (OpenCV) ...................................................................... 26 Figure 25: Third scenario after applying the algorithm (OpenCV) ......................................................................... 27 Figure 26: Second product Analysis after applying the Open CV algorithm .......................................................... 28 Figure 27: Our target to obtain 24 circles in a binary image ................................................................................ 28 Figure 28: Connectivity in color (Motion Blur) ....................................................................................................... 29 Figure 29: connected pixels which they create unwanted objects ...................................................................... 29 Figure 30: The mast which is used to disconnect unwanted large objects ............................................................ 29 Figure 31: color intensity differences ..................................................................................................................... 29 Figure 32: first scenario after applying the algorithm (Vision Builder) .................................................................. 30 Figure 33: Second scenario after applying the Vision Builder Algorithm ............................................................... 31 Figure 34: Third scenario after applying the Vision Builder Algorithm .................................................................. 32 Figure 35: Second product analysis after applying the Vision Builder Algorithm .................................................. 33 Figure 36: CO2 Production process flow diagram ................................................................................................... 37 Figure 37: Plastic bottles manufacturing ............................................................................................................... 37 Figure 38 ................................................................................................................................................................ 38 Figure 39 ................................................................................................................................................................ 39 Figure 40: ............................................................................................................................................................... 39 Figure 41: Caustic Detection .................................................................................................................................. 39 Figure 42: inner wall of bottle inspection (OmniVision II) ...................................................................................... 40 Figure 43: outer wall inspection, including ROIs .................................................................................................... 41 Figure 45: Installing CMake and MVSC++ 2010 ................................................................................................... 43


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Figure 46: C:\opencv\build\x86\vc10\bin .............................................................................................................. 43 Figure 47: defining System and Environment variables ......................................................................................... 44 Figure 48 ................................................................................................................................................................ 44 Figure 49: Debug Symbols (for *.dll s) .................................................................................................................. 45 Figure 50: Project properties ................................................................................................................................. 45 Figure 51: Project properties ................................................................................................................................. 46 Figure 52: solution properties ................................................................................................................................ 46 Figure 53: Pike F-505 ............................................................................................................................................ 47 Figure 54: Quantum efficiency of the Pike F-505 .................................................................................................. 48 Figure 55: The EVS-1464 RT ................................................................................................................................. 48 Figure 56: The EVS-1464RT ports and connections flexibility ............................................................................... 49 Figure 57 Schott A20960 Light Source ................................................................................................................. 51 Figure 58: Detailed Diagram of the Beagle Board ................................................................................................. 52 Figure 59: Detailed Diagram of the Panda Board .................................................................................................. 52 Figure 60: Raspberry PI ......................................................................................................................................... 53 Figure 61: Arduino Uno .......................................................................................................................................... 54 Figure 62: front panel example LabView ................................................................................................................ 55 Figure 63: Examples: (Push Button, Program stop button, Selector panel, gauge indicator ............................... 55 Figure 64: Block Diagram window LabView ............................................................................................................ 56 Figure 65: Connectors panel LabView .................................................................................................................... 56 Figure 66: VISION Builder programing Environment .............................................................................................. 58

L is t o f Tab les

Table 1: List of the most used arithmetic operations on images ........................................................................... 19 Table 2: Technical specifications of the PIKE F-505 .............................................................................................. 47 Table 3: Technical specifications of the EVS-1464 RT ........................................................................................... 50 Table 4: Comparison between the Low-Cost imbedded chips ................................................................................ 53 Table 5: Arduino Uno Technical specifications & data sheet ................................................................................ 54 Table 6: Comparison between 3 solutions to build the system ............................................................................. 59


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1 Chapter 1: Introduct ion

Quality assurance and quality control in the production are important factors to increase the efficiency of the production line and decrease its defects. There are many ways to do quality control but it usually requires a worker to inspect periodically the outcome of the production line. This is considered to be tiring and limiting for the humans brain capability. Automating this procedure would be the optimum solution to invest with something else in the workers. This project is based on the output of the production line of the soda beverages in Coca Colas factory, where 24 bottles are to be placed in a plastic crate and sometimes the output could have some defects such as:

- Missing bottles - Extra bottles - Bottles are misaligned - Bottle(s) from different beverage type

And the inspection of these defects is done manually using a worker inspecting the outcome of the production line, to utilize the worker with something to more useful; it was decided to automate this process using modern techniques to make an artificial system that is capable of making decisions. So to count the bottles and check if there is any fault in the output beverage crate, it was decided to use D ig i ta l Image Process ing to check for the defects using a camera.

1 .1 Object ives

1- Build an Inspection System 2- Program the system and its algorithms using different programs

i. LABVIEW ii. Vision Builder iii. OpenCV

3- Study the software and the hardware Alternatives to choose from them to minimize the cost without affecting the processing speed.

4- Build a portable to take sample images and videos and be able to do some processing.

1 .2 Approach The project is divided to the following phases: Invest iga t ion Phase : Finding the application where the Computer Vision System is to be applied. Research Phase : Reviewing tutorials, scientific papers and books concerning digital image processing, LABVIEW programming, Vision Builder and OpenCV programming. Programming Phase : An algorithm was programmed on OpenCV and Vision Builder to execute the desired inspection. Imp lementa t ion Phase : Implementing the solution in the factory as a Standalone system including a CAD design to an adjustable arm to mount the camera, also using and programming a Microcontroller to get a useful output.


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2 Chapter Two: L i terature Rev iew

2.1 Def in i t ions

Image: In imaging science an Image is a two-dimensional signal, where X and Y determine the position of the pixel and the pixels color value represents the third independent variable. The Value of the pixel could be represented in 8-bit representation (Gray scale), 3x8bit planes (colored image) and 1-bit image (binary images). Images are treated as digital signals which consist of discrete values. So the best way to represent an image is in a matrix form to ease their understanding and processing. Computer V is ion : Computer vision is the transformation of data from a still or video camera into either a decision or a new representation. All such transformations are done for achieving some particular goal. The input data may include some contextual information such as the camera is mounted in a car or laser range finder indicates an object is 1 meter away. The decision might be there is a person in this scene or there are 14 tumor cells on this slide. A new representation might mean turning a color image into a gray scale image or removing camera motion from an image sequence. In a machine vision system, however, a computer receives a grid of numbers from the camera or from disk, and thats it. For the most part, theres no built-in pattern recognition, no automatic control of focus and aperture. What the computer sees is just a grid of numbers. The problem, as we have posed it thus far, is worse than hard; it is formally impossible to solve. Given a two-dimensional (2D) view of a 3D world, there is no unique way to reconstruct the 3D signal. Formally, such an ill-posed problem has no unique or definitive solution. The same 2D image could represent any of an infinite combination of 3D scenes, even if the data were perfect.

2 .2 Dig i ta l Image Process ing techniques From the definition of an Image, digital image processing is based on matrices mathematical operations to manipulate the images und get useful data out of them.

- Image representation - Image preprocessing - Image enhancement - Image restoration - Image analysis - Image reconstruction - Image data compression


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2.2 .1 Image Representa t ion An image defined in the "real world" is considered to be a function of two real variables, for example, f(x, y) with f as the amplitude (e.g. brightness) of the image at the real coordinate position (x, y). The effect of digitization is shown in the figure below.

Figure 1: Image Representation Example: a part of the gray level image is taken, and represented in the way the computer sees it. As a matrix

The 2D continuous image f(x, y) is divided into N rows and M columns. The intersection of a row and a column is called as pixel. The value assigned to the integer coordinates [m, n] with {m=0,1, 2,...,M-1} and {n=0,1,2,...,N-1} is f[m, n]. In fact, in most cases f(x, y), which we might consider to be the physical signal that impinges on the face of a sensor. Typically an image file such as BMP, JPEG, TIFF etc., has some header and picture information. A header usually includes details like format identifier (typically first information), resolution, number of bits/pixel, compression type, etc.

2 .2 .2 Image Preprocess ing

2 .2 .2 .1 Scal ing The theme of the technique of magnification is to have a closer view by magnifying or zooming the interested part in the imagery. By reduction, we can bring the unmanageable size of data to a manageable limit. For re-sampling an image Nearest Neighborhood, Linear, or cubic convolution techniques are used.

Magn i f i ca t ion

This is usually done to improve the scale of display for visual interpretation or sometimes to match the scale of one image to another. To magnify an image by a factor of 2, each pixel of the original image is replaced by a block of 2x2 pixels, all with the same brightness value as the original pixel.

Figure 2: Image Magnification


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6 Chapter S ix : References

1. Bradski G. and Kaehler A., (2008), Learning OpenCV, OReilly Media, Inc. 2. Campos M., Ferreira M, Martins T, Santos C., (2010), Inspection of Bottles Crates in the Beer Industry through

Computer Vision, Department of Industrial Electronics, University of Minho. 3. Gonzalez R. and Woods R., (2008), Digital Image Processing 3rd edition, Pearson Education Inc. 4. http://En.Wikipedia.com/, multiple subjects concerning digital image processing. 5. http://www.ni.com/vision/, for the products information and tutorials 6. http://beagleboard.org/Products/BeagleBoard-xM 7. http://pandaboard.org/content/platform 8. http://opencv.org/about.html 9. http://arduino.cc/en/Main/arduinoBoardUno 10. http://ee.lamar.edu/gleb/dip/10-1%20-%20Morphological%20Image%20Processing.pdf


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7 Appendences

7.1 Appendix A: My Exper ience at Coca-Cola Factory

The Coca Cola factor is relatively advanced, and the factory produces all the beverages related to Coca Cola (Soda drinks and Water Arwa). The company in Jordan is a subsidiary of Coca Cola turkey, the Madaba factory has 300 employees operating different production facilities, and they sum up to reach 500 employees in Jordan between the factory, storage and logistics facilities Product ion fac i l i t ies :

- CO2 production facility - Plastic bottles manufacturing - Beverage soda cans (Filling, sealing and packaging) - Beverage plastic bottles (Filling sealing, labeling and packaging) - Beverage glass bottles (Cleaning, inspecting, sealing, packaging)

7 .1 .1 CO 2 Product ion Fac i l i t y In the CO2-production plants the CO2-gas is generated by combustion of natural gas or fuel oil and separated from the flue gas by means of a special absorption agent (monoethanolamine), processed and subsequently liquefied. These plants are provided with all necessary components to ensure a safe and economical production of purest carbon dioxide. CO2-gas is produced by combustion of natural gas, diesel oil or kerosene. In a special burner, combustible and air are combusted under almost stoichiometric conditions, and generate a low-emission flue gas, which is rich in CO2.

7 .1 .1 .1 ABSORPTION AND DESORPTION The flue gas gets into the absorber where the CO2 is almost completely absorbed by means of an aqueous solution of monoethanolamine (MEA). The MEA-solution charged with CO2 is preheated and then pumped to the stainless steel disrober. In the disrober, the CO2 is released as a humid gas due to the heating of the MEA-solution. After leaving the disrober, the humid CO2 is cooled in a stainless steel gas cooler and led to the CO2-compressor. After desorption, the MEA- is chilled and purified in a special MEA-filter station before entering again the absorber.

7 .1 .1 .2 CO2-COMPRESSION, GAS PURIFICATION AND GAS DRYING In dry-running piston compressors, the humid CO2is compressed absolutely free of oil to approx. 16 to 17 bars. In a potassium permanganate-scrubber (stainless steel), impurities are removed by oxidation. The CO2-gas is then dried and remaining impurities are finally removed in a special activated carbon filter.

7 .1 .1 .3 CO2-LIQUEFACTION AND STORAGE In the CO2-liquefaction plant, the dried and purified CO2is cooled down to below -25C and then liquefied. In refrigeration plants screw or piston compressors are used for different refrigerants. Finally, the liquid and food grade pure CO2is stored in an insulated CO2-storage tank.


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Figure 36: CO2 Production process flow diagram

7 .1 .2 Plas t ic bot t les manufac tur ing Plastic bottles are manufactured from plastics usually High-density polyethylene (HDPE); they are usually used to store all types of liquids. The manufacturing process of these bottles consists of pressuring hot air in a small thick test-tube shaped plastic part. This operation occurs in mold having the desired bottle shape. There are some important factors that should be taken in account such as the air pressure, air temperature and the right position of the bottle.

Figure 37: Plastic bottles manufacturing


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7.1 .3 Beverage soda cans The cans are filled with the beverage and then pressurized CO2 is added (A pressure gage will determine the when to stop pumping gas in to the cans); where it is important that the can is well clamped with pressuring system and that the edges are note worn-out, so that the liquid will not flow outside the that can causing a huge mess at the line. After that the cans are sealed (the end part is well pressed preventing any of the beverage contents to flow out).

Figure 38 The production line produces 600 cans/min, then every 9 cans are packed together (using Impudence film) where they should be aligned, misalignment will cause some problems to the logistics department while packing the can packages.

7 .1 .4 Beverage p las t i c bot t les (F i l l ing sea l ing , labe l ing and packag ing) After the bottles are manufactured, as mentioned before, they are filled with the beverage and CO2 gas; they are sealed with imported plastic caps. Labeling: the production line produces three different products (Cola, Fanta and Sprite), and then they have to be labeled according to the product type. For the labeling process there are printed rolls containing and they are enough for more than 1000 bottle. After placing one label, the label has to be cut from the role; it is done using a color sensor after having a certain amount of white color (at the end of the label) the label is been cut from the roll. After that every 6 bottles are packaged together and the sent to the logistic part to be transferred. Once again the alignment of the bottles should be considered so that no problems will occur at the logistics department.

7 .1 .5 Beverage g lass bot t les (C lean ing , inspect ing , sea l ing , packag ing) : Cleaning: The glass bottles are usually being reused after retrieving them from the market, and then they are washed with high pressure water to remove all the dirt and sticky left additives. Inspecting: After the bottles are cleaned up, they are inspected using the OMNIVISION II from FILTEC. The OmniVision II Empty Bottle Inspector is the only all-surface empty bottle inspector that provides full 360 degree inspection at line speeds up to 1400 bottles per minute while exceeding all industry standards for quality, repeatability and reliability with minimal false rejects. The OmniVision II provides guaranteed inspection alignment by holding bottles in two places for perfect registration in the inspection zone as opposed to relying on spongy belts that can collect contaminants and require frequent replacement. Additionally, a wide range of bottle sizes and dimensional tolerances can run through the OmniVision II without causing jams or stoppages. http://www.filtec.com/full-container-inspection/ The OmniVision II inspects almost all the parts of the glass bottle including (Base, Inner Sidewall, Outer Sidewall, Residual Liquids Finishing and Thread). But since the bottles have already printed logo on the outer sidewall, this part is excluded from the inspection. The OmniVision II capabilities and algorithms:


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"

7.5 Appendix E: F low Chart D iagram (OpenCV A lgor i th im) "

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Automated Quality Inspection of Bottles Crates Through Image Processing