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Advanced Composite Materials

ISSN: 0924-3046 (Print) 1568-5519 (Online) Journal homepage: https://www.tandfonline.com/loi/tacm20

Carbon rod missing inspection method ofmultidimensional carbon preform based on imageprocessing

Yong-Ho Kim & Jung-Ryul Lee

To cite this article: Yong-Ho Kim & Jung-Ryul Lee (2019): Carbon rod missing inspection methodof multidimensional carbon preform based on image processing, Advanced Composite Materials,DOI: 10.1080/09243046.2019.1573464

To link to this article: https://doi.org/10.1080/09243046.2019.1573464

Published online: 10 Feb 2019.

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Carbon rod missing inspection method of multidimensional carbonpreform based on image processing

Yong-Ho Kim and Jung-Ryul Lee*

Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, 291Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

(Received 18 June 2018; accepted 21 January 2019)

This paper shows that just taking a picture of the carbon preform can be a powerfuldetection method of missing rod. The poor quality of the carbon preforms includingmissing rods cannot provide tailored composite to meet desired properties. By theway, the carbon preform is manufactured manually now and often there happena mistake that a carbon rod is omitted. Therefore, an effective solution for guarantee-ing the quality of the carbon preform is needed. For that reason, this paper proposesnew inspection method that can detect the missing rods of multidirectional carbonpreform. This method utilized some image processing techniques to simplify theimage of carbon preform. In this method, the carbon preform image is modified sothat the carbon rods and background can be easily separated. Thus, this new methodfound all missing rods and marked missing space with red circle. Moreover, becausethe inspection method suggested in this paper can be easily converted into the form ofsoftware, it is expected to be applied effectively to the fields of manufacture of themultidirectional carbon preform.

Keywords: carbon fiber-reinforced carbon; carbon preform; missing rod; imageprocessing

1. Introduction

Carbon/carbon (C/C) composite consists of carbon preform and carbon matrix, accord-ingly its specific strength and specific stiffness scarcely change with their environmentaltemperature change even if the temperature goes up to extremely high temperature fromnormal temperature [1–5]. In addition, C/C composite has a number of other outstandingproperties, including great ablation resistance, low thermal expansion coefficient, andhigh thermal shock resistance [6–11]. Because of these superior engineering properties,C/C composites became the essential material of the field of aerospace engineering. Onthe other hand, C/C composite also has some drawbacks. The most critical drawback iscost and the next one is that the C/C composite is anisotropic [12]. Especially,1-D (unidirectional) and 2-D (two-directional) C/C composite are noticeably anisotropicso they show a weakness in bearing some forces acting on certain directions. This is whymultidirectional C/C composite is often used in the aerospace engineering. For example,the multidirectional C/C composite can be used for a rocket nozzle, leading edge, andnose cap of a space shuttle [13,14].

Figure 1 shows the rendering results of four-directional (4-D) structure drawn usingthree-dimensional (3-D) sketch program. The 4-D structure consists of one axial

*Corresponding author. Email: leejrr@kaist.ac.kr

Advanced Composite Materials, 2019https://doi.org/10.1080/09243046.2019.1573464

© 2019 Japan Society for Composite Materials, Korean Society for Composite Materials and Informa UK Limited, trading asTaylor & Francis Group

direction and three radial directions and this is well displayed in Figure 1. As you can seein Figure 1, three radial directions of carbon rods help the entire structure to withstandthe forces in any directions. For that reason, the 4-D structure is commonly known asmore isotropic and efficient structure than 3-D structure. In this study, the 4-D structurewas mainly dealt with, and it is expected that the same approach can be applied to3-D structure.

In order to produce the C/C composite, carbon preform should be manufactured first.The carbon preform is composed of only carbon rods and it is essential framework oftailored C/C composite. By the way, 3-D and 4-D preforms are manufactured manuallybecause of complexity of 3-D and 4-D structure. For that reason, many mistakes occur inthe manufacturing process of the carbon preform. The poor quality of the carbon preformcannot provide the tailored composite to meet some desired properties such as superiorstrength and good ablation performance [15]. Therefore, effective solutions for guaran-teeing the quality of the carbon preform are needed.

The goal of this paper is to propose an automatic inspection method for detection ofmissing carbon rod with a camera in an electronic device. The arrangement of the carbonrods in the carbon preform should be regular. Therefore, it is possible to detect themissing rods using some image processing techniques. Using the fact that the arrange-ment of carbon rods is always constant, quite simple image processing techniques will beable to find the position of missing carbon rod enough. In addition, by the propagation ofsmart devices, it became very easy to take a picture and the quality of the picture hasbeen improved dramatically. This implies that this image-based approach is reallypromising and likely to be applied to actual industrial sites.

Also, the computing speed of personal smart devices has been improved together.Therefore, the inspection method suggested in this paper can be easily converted into theform of software of smart devices. The target operating system of this software was‘Windows 10’ and the target device was ‘Surface pro 4’. However, it can always extendto any other smart devices. The core functions of this software are taking picture of thestacked carbon preform and displaying the results of the inspection in real time. It candetect the missing rods of top surface and four side surfaces of 4-D carbon preform.

Actually, there have been many studies using image processing techniques forvarious visual inspections or surface inspections. Most of such studies have beenintroduced in image processing to separate objects and backgrounds. Therefore, themost commonly used technique is segmentation. V. R. Rathod et al. found out weld

Figure 1. The four-directional carbon preform structure: (a) overall view and (b) top view.

2 Y.-H. Kim and J.-R. Lee

defects using the watershed transformation-based image segmentation [16] and X. Wanget al. found out weld defects using adaptive thresholding-based image segmentation [17].Both studies have commonality in applying image processing techniques to radiographicimages of weld defects. However, the results of the second study seem to be better interms of accuracy and automation. In other view point, there is a method of analyzingtexture to determine the location of defects, for example, studies using co-occurrencematrix conducted by W. Y. Wu et al. [18]

For convenience, in this paper, the mistakes stated above are restricted to the missing ofthe carbon rods only. In addition, the carbon preform pictures and field test included in thispaper were supported by DACC carbon and one of them is shown in Figure 2. There aremany carbon rods forming the 4-D carbon preform and their diameter is 1.7 mm. Thus, toinspect this carbon preform visually by naked eye is quite time consuming and verylaborious work and the missing rods are often undetected. In order to detect the missingof the carbon rods automatically and with high reliability, some image processing methodsmainly consisting of image binarization and scale-invariant approach were utilized.

2. Image processing methodology

Before applying image processing, the carbon preform picture should be converted intoa grayscale image because there are several reasons and benefits to use grayscale image.First, the size of image data and processing time are reduced. There are three channels to

Figure 2. Picture of four dimensionally stacked carbon preform.

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express the RGB colors in the conventional image, but there only exists one channel inthe grayscale image. This means that the data size and computation time are reduced bya third. In addition, the grayscale image is much easier to handle the image data thancolor image. This is because only intensity values need to be considered when perform-ing image processing. In this study, high quality pictures taken with electronic deviceswere used so using grayscale image was quite helpful.

2.1. Binarization by adaptive thresholding

The main goal of image processing is usually to separate the object from the background.To do that, it needs a clear criterion. In the binarization processing, the criterion isa threshold value of the intensity. A bright pixel with a high intensity value becomes 1and a dark pixel with a low intensity value becomes 0. Generally, the object is brighterthan the background, thus it is possible to classify the pixels into the object group and thebackground group theoretically.

There are two types of threshold. The first type is a global threshold. This type ofthreshold is calculated once and applied to all pixels at a time. If the picture is quitesimple, this type of threshold is very effective and efficient to classify the pixels. Anothertype is a local threshold. This type of threshold is calculated at every pixel usinginformation of surrounding pixels. Because this type of threshold is calculated locallyand adaptively, it is possible to cope with the change of brightness and illuminance of thetarget image.

The carbon preform pictures can be taken under various environments; therefore,the second type of threshold was adopted for binarization in this paper. This can beexpressed in Equation (1) and it is called adaptive thresholding method [19].

b i; jð Þ ¼ 1; f i; jð Þ � t i; jð Þ0; other wise

�(1)

The b i; jð Þ means ith row and jth column element of the binary image. In the samemanner, the f i; jð Þ menas ith row and jth column element of the original image. The valuet i; jð Þ is locally computed threshold of ith row and jth column pixel.

2.2. Integral image

Unfortunately, the adaptive thresholding method has a drawback in processing timebecause the threshold has to be computed at every pixel using the information ofsurrounding pixels. Therefore, it is necessary to reduce the runtime using the integralimage for real-time adaptive thresholding. Using the integral image transformsa convolution operation into simple addition operations, so that the operation time issignificantly reduced [19]. Of course, the time required to calculate the integral imageshould also be considered. By the way, the integral image can be computed relativelyeasily by the Equations (2–5). First, the integral image is defined as Equation (2).

I x; yð Þ ¼Xxi¼1

Xyj¼1

f i; jð Þ (2)

4 Y.-H. Kim and J.-R. Lee

The I x; yð Þ is xth row and yth column element of integral image and it means a simplesummation of all left and upper values of the (x, y) pixel of the original image. UsingEquation (2), these three equations can be easily derived.

I 1; yð Þ ¼ f 1; yð Þ þ I 1; y� 1ð Þ (3)

I x; 1ð Þ ¼ f x; 1ð Þ þ I x� 1; 1ð Þ (4)

I x; yð Þ ¼ f x; yð Þ þ I x; y� 1ð Þ þ I x� 1; yð Þ � I x� 1; y� 1ð Þ (5)

Equations (3)–(5) help the integral image computed fast and they are described in Figure 3.In Figure 3, the first matrix indicates the original image and others show how the integralimage is computed. First, the intensity values are summed once in the row and columndirections. Then, the first row and column of the integral image is completed and itbecomes possible to fill the matrix in the diagonal direction as shown in Equation (5).

2.3. Local sum

After getting the integral image, it is possible to calculate the local sum s x; yð Þ usingEquation (6). The local sum means a simple summation of certain area of the originalimage. In the image processing, the area is called window.

Figure 3. The process of making integral image.

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s x; yð Þ ¼Xx2x¼x1

Xy2y¼y1

f x; yð Þ

¼ I x2; y2ð Þ � I x2; y1 � 1ð Þ � I x1 � 1; y2ð Þ þ I x1 � 1; y1 � 1ð Þ (6)

If the window is square shape and its side length is w, x1 and x2 become x� w�12 and

xþ w�12 respectively. For convenience, the term w�1

2 is often replaced by c. In this study,a square window with a side length of 41 pixels was used. Equation (6) implies that thelocal sum can be computed using only four elements of the integral image and it isdescribed in Figure 4.

Figure 4 shows how to get the local sum of area D which can be expressed as(A + B + C + D) – (B + A) – (C + A) + A. The local sum of area A is indicated by thenumber 6 in the yellow circle of the integral image. In the same manner, the local sum ofarea (A + B) is indicated by the number 17 in the yellow circle of the integral image.Therefore, the local sum of area D can be calculated as 26–17 – 12 + 6. This means thatit is possible to get the local sum fast using the integral image.

Finally, using the local sum s x; yð Þ, the threshold t i; jð Þ can be computed byEquation (7). The first fraction of Equation (7) is an average value of the intensity ofsurrounding pixels. The second fraction of Equation (7) is a kind of margin. If p ispositive, the threshold become lower than the average of surrounding pixels. On theother hand, threshold becomes higher than the average when p is negative. Thus, it ispossible to control the local threshold level by changing the value p. In this study, thep value of −20 was used. This means that the threshold should be 20% greater than theaverage value of surrounding pixels. This value was determined to reduce the error thatoccurs in the background portion between the carbon rods. If a positive p value is used,pixels with accidental high intensity values among the background pixels exceed theirthreshold value. Therefore, a negative p value was set within the range where the imageinformation was not deformed.

t i; jð Þ ¼ s i; jð Þw2

100� p100

(7)

It is very simple to calculate the local threshold because the local sum s i; jð Þ already hasthe information of surrounding pixels in a form of a simple summation. If the integral

Figure 4. The relationship between integral image and local sum.

6 Y.-H. Kim and J.-R. Lee

image and local sum are not utilized for getting the local threshold, it will take extremelylong time because the computation process of the local threshold includes a kind of2-D convolution or image convolution.

3. Scale-invariant approach

As a result of above process, a binary image is obtained and it consists of ones and zeros.The number ones indicate the carbon rod because the carbon rod is the main object of thepicture. On the other hand, the number zeros indicate the background. By the way, theimportant thing is that the missing space of the carbon rods is also indicated by zeros inthe binary image. Therefore, in order to find where the missing space is, it should be ableto distinguish the difference between the background and the missing space. In thisresearch, the size of the missing space was calculated automatically by measuring thediameter of the carbon rod in pixels and the background part was filtered by using imageconvolution with a window corresponding to the missing space size. Thus, this methodcan locate the missing position of the carbon rod irrespective of the scale of the image.

3.1. Calculation of rod radius

It is necessary to know the size of the carbon rod in pixels because the size is highly upto the distance from the camera to the carbon preform. The shorter the stand-off distancefor taking a picture is, the bigger the size of each rod is. Therefore, it should be possibleto calculate the radius of the carbon rod by itself in order to make this method havea scale-invariant property. The main idea of this study is to compare the carbon preformimage with various sizes of circular shape. The circular shape was used because the crosssection of the carbon rods is circular. Therefore, the size of the carbon rod can beestimated by comparing the various sizes of circles with the carbon preform andcalculating the degree of size matching.

First, it needs multi-scale images of unit circle. A circle image is composed of thenumber ones and zeros. The circle part is filled with the number ones and the backgroundpart is filled with the number zeros. It looks like a carbon rod converted into the binaryimage because the carbon rod is filled with the number ones and the other parts is filledwith the number zeros. Therefore, in order to calculate the radius of the carbon rod, themulti-scale circle images and the binary image of the carbon preform should be com-pared to each other. If the size of the circle is almost same with the size of the carbon rod,they have a high normalized cross-correlation coefficient. Figure 5 describes this algo-rithm where the left image of Figure 5 is a binary image of the part of the carbon preformand the right image of Figure 5 is multi-scale images of a unit circle. Knowing the radiusof the carbon rod is very meaningful because the radius information is directly used todetermine the size of the missing space that should be detected by this method.

3.2. Image convolution

In the binary image, the missing space of the carbon rods is only composed of zeros.Therefore, the basic operation principle of this method is to find a group of zeros andmark that group conspicuously. It looks quite simple but, to do this, additional2-D convolution is needed. Convolution result between the group of zeros and the certainwindow of which size is calculated based on the radius of the carbon rod is of course

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zero. On the other hand, convolution result between any other groups having some onesand the window is not zero. Therefore, from now, the problem is equivalent to findingout where the zero is in the result of the 2-D convolution. The size of a window used herewas automatically determined by the result of rod radius calculation and all elements ofthe window had a value of 1.

3.3. Background filtering

However, unfortunately, all zeros in the result of the convolution do not signify themissing space that should be detected for the quality inspection of the carbonpreform. Only very few zeros indicate the accurate missing space because most ofzeros are located at the background of the picture. Because the binarization result ofthe background, like missing space, is filled with only zeros, its results of the2-D convolution with the window are also zeros. To filter out this error induced bythe background, it is necessary to utilize information or feature of the background. Inthe result of the 2-D convolution, there are consecutively distributed zeros on thebackground part, but there is only one zero at the missing space. Therefore, except inconsecutive zeros, only isolated zeros are meaningful. Using this property, the miss-ing space can be distinguished from the background and Figure 6 depicts thissituation. The window size is designed using the radius information of the carbonrods, and there should be isolated zero at the missing space.

4. Results

In a real stacking environment for the carbon rods, some rods were randomly removed.The size of the original carbon preform picture was 4032 by 3024 which is about12 million pixels. However, most smart devices have lower display resolution.Therefore, the size of the picture was adjusted to 2048 by 1536 which is 3 million pixels.

To sum up, all processes of this method can be summarized as Figure 7. The redrectangular in Figure 7 indicates the process of binarization and the blue rectangular inFigure 7 indicates the scale-invariant approach. The equation using in the stage of theconversion of RGB image to grayscale image was referred to Advanced TelevisionSystem Committee standards.

Figure 5. (a) Binary image of carbon rods and (b) multi-scale circle images.

8 Y.-H. Kim and J.-R. Lee

Using this method as summarized in Figure 7, the missing rods detection test wasconducted and following figures are the results of the test. As shown in Figure 8, thecarbon preform picture is converted into the binary image. To check the accuracy of thebinarization result, it needs to expand the binary image of the carbon preform. Figure 9

Figure 6. The result of the two-dimensional convolution and the difference between missingspace and background.

Figure 7. Image processing flowchart.

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is parts of an enlarged binary image and it shows that the result of the binarization isvery accurate and clear. The carbon rods are expressed as white and other parts areexpressed as black. Using this accurate result of the binarization, the missing spacedetection was carried out. In this case, the diameter of the carbon rods in the axialdirection was about 1.7 mm and the diameter of the carbon rods in the radial directionswas about 1 mm.

In addition, to verify this method, the adaptive thresholding method and theOtsu’s algorithm were compared. The Otsu’s algorithm is a method of determininga threshold value through a gray-level histogram analysis [20]. Figure 10 shows thatonly adaptive thresholding result preserved the carbon preform properly. In the othertwo Otsu’s algorithms, the carbon rods disappeared with the background. Therefore,it has been confirmed that the adaptive thresholding method is very effective indetecting the missing of carbon rods. In this case, the diameter of the carbon rodsin both directions was about 1 mm.

Figure 8. Conversion of RGB preform image to binary preform image.

Figure 9. Enlarged binary image of the carbon preform.

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The first picture of Figure 11 is a result of the top view detection. There areseveral red circles in the first picture. The red circles in the first picture is a kind ofcaution that indicates the missing space. Therefore, the first picture means that thereare two missing rods in the carbon preform. The second and third pictures areenlarged pictures around each red circle which is located in the carbon preform.There are some errors but the errors are located outside of carbon preform structureand consequently they signify nothing. In this case, the diameter of the carbon rods inthe axial direction was about 1.7 mm and the diameter of the carbon rods in theradial directions was about 1 mm.

As the same manner, the first picture of Figure 12 is a result of the side viewdetection. The second picture is an enlarged one around the red circle whichindicates the real missing space. On the other hand, the third picture is an enlargedone around the red circles which are located at the boundary of the stacked carbonpreform. The red circles of the third picture are meaningless and they are just errorscaused at the boundary. Actually, the errors detected near the boundary have a slighteffect on the computation speed but they do not interfere with real-time inspectionin the field. In addition, the carbon preforms are always designed in oversizebecause the boundary part is cut from the finished C/C composite product.Therefore, errors that occur at the boundary are not significant.

Figure 13 shows the binarization results of the top and side surfaces and itsenlarged images. In both cases, it can be seen that the binarization is clear becausethe carbon part is white and the background is black. In addition, in the calculationof the rod radius, seven pixels and six pixels were calculated respectively. It canbe confirmed that the calculation result roughly matches the image result inFigure 13.

Figure 10. Comparison of binarization results: (a) grayscale image, (b) adaptive thresholdingresult, (c) Otsu’s algorithm result, (d) multi-level Otsu’s algorithm result.

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5. Conclusion

In this paper, a new detection method for the missing rods of the carbon preformwas proposed. The proposed algorithm was composed of two parts; the binarizationand the scale-invariant approach. For the fast binarization, an integral image andlocal sum were coded. In the scale-invariant approach, the radius of the carbon rodswas calculated by itself so that it could be possible to determine the size of themissing space. Using this information, the proposed algorithm found out the posi-tions of many candidates of real rod missing space. The candidates included thepositions also in background; thus, the errors caused by the background wereeliminated as the outliers.

In the results of the detection, all missing rods randomly selected were markedby red circles without omission in the field tests. Especially, in Figure 11, result ofthe top surface, two red circles indicated the missing spaces correctly despite the

Figure 11. Results of the missing rods detection of top surface.

12 Y.-H. Kim and J.-R. Lee

difference of illuminance and intensity between the two places thanks to theadaptive thresholding technique. The binarization using the adaptive threshold canovercome the change of illuminance and intensity. In addition, the proposed methodworked normally at the side surfaces.

Finally, the proposed image processing based missing rod detection algorithmsuccessfully applied to the real world field tests with real time detection capabilityand scale invariant picturing, in other words, right after the stacking step of carbonrods in the C/C composite manufacturing. Therefore, it is expected to be used fora manufacturing quality control of the carbon preform instead of naked eyes basedvisual inspection.

Figure 12. Results of the missing rods detection of side surface.

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AcknowledgementsThis work was supported by the Manufacturing ICT Convergence Consortium CooperationProgram funded by National IT Industry Promotion Agency and Gyeongnam Techno Park andthe authors appreciate Carbon Nano Fibers for providing the preform images.

Disclosure statementNo potential conflict of interest was reported by the authors.

FundingThis work was supported by the National IT Industry Promotion Agency and Gyeongnam TechnoPark [N/A].

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