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2011-IPP-CT Data Evaluation of Fibre Reinforced Polymers to Determine Fibre Length Distribution_Kannappan


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2011-IPP-CT Data Evaluation of Fibre Reinforced Polymers to Determine Fibre Length Distribution_Kannappan

  1. 1. D. Salaberger1*, K. A. Kannappan1 , J. Kastner1 , J. Reussner2 , T. Auinger3 1 University of Applied Sciences, Upper Austria, Wels, Austria 2 Borealis Polyolefine GmbH, Linz, Austria 3 Transfercenter für Kunststofftechnik GmbH, Wels, Austria Evaluation of Computed Tomography Data from Fibre Reinforced Polymers to Determine Fibre Length Distribution Sub-lm computed tomography (sub-lm-CT) was used to deter- mine the fibre orientation and fibre length distribution in long glass fibre filled polypropylene. For data evaluation two differ- ent concepts based on the application of a sequence of different filters were applied. The first concept is based on segmentation by binarisation using a global threshold, followed by a detailed analysis of regions where fibres are touching. The second con- cept is based on analysis of the original gray value image. For each voxel the local fibre orientation is determined by calcu- lating the Hessian Matrix and analysing its Eigen values. The effectivity of the two data analysis concepts in determin- ing orientation and length was investigated. For this purpose the algorithms were applied to specimens with four different levels of fibre content: 1, 5, 10 and 30% by weight. To quantify the level of error in fibre determination, a minimum and aver- age probability for correct fibre determination were estimated. The results show a strong dependence of the level of error on the fibre content. Whilst the determination of fibre orientation is not significantly affected, determination of fibre length distribu- tion is significantly influenced by fibre content. For samples with fibre content above 5%, concept 1 does not produce cor- rect representations of all fibres. In particular, problems arise if the fibres are touching. Concept 2 delivers much better results and represents most of the fibres correctly even at higher fibre content levels and for touching fibres. This was proven by using artificial CT-data sets generated by CT-simulation and by sys- tematic comparisons. A practical application of the CT-evalua- tion pipeline is presented for glass fibre reinforced rings pro- duced by injection-moulding and extrusion. For both samples the orientation tensors are calculated and the orientations of the fibres are visualized in three dimensions by colour coding. 1 Introduction To be able to predict and modify the mechanical properties of a structural component made from heterogeneous polymers, knowledge of the geometrical details of the different phases is necessary. For long and short glass fibre filled polymers the pa- rameters fibre orientation distribution (FOD), fibre length dis- tribution (FLD) and fibre content have been identified as very important for the mechanical behaviour of this composite ma- terial. (Matsuoka, 1995; von Bradsky et al., 1997) Knowledge of the correlation between manufacturing techni- ques and parameters and mechanical properties is very important for improvement of the technique or the development of new technologies. FOD and FLD are input parameters for mechanical simulations which are useful in the prediction of mechanical properties like the strength or stiffness of the produced part. Therefore these two properties have to be determined accurately. Methods have already been established to measure FOD and FLD. The best established methods are destructive methods that can usually gather information either about orientation or length distribution. (Zak et al, 2000) For orientation, thin cross sections can be analyzed, either with light optical microscopy (LOM) or X-ray methods. (Phelps et al., 2008) Fibre length can be deter- mined by LOM- methods after burning the matrix or estimating the length from cross sections. Correction of fibre lengths deter- mined from cross sections may be necessary when the number of analysed cross sections is low due to statistical effects that have to be taken into account. (Fu et al., 2002, Clarke et al., 1995) Recently, three dimensional methods based on X-ray tomo- graphy (CT) have also been developed. (Shen et al., 2004; Kastner et al., 2008b; Teßmann et al., 2010) Depending on the tomographic method used, the results can only be estimates or detailed analyses. Since data quality is determined by resolu- tion as well as the appearance of artefacts, at the moment the best CT-data quality can be achieved by Synchrotron scanning (SCT). Industrial CT devices are most commonly l-CT de- vices that are capable of resolutions of a few lm. The disadvantages of the destructive methods are usually the time required, the fact that for fibre length and fibre orientation two different methods have to be applied and that the specimen cannot be used for further investigations. Synchrotron CT is cost intensive and availability is a limiting factor. CT devices that deliver CT data with quality approaching SCT quality are available. These sub-lm-CT devices usually work according to the cone beam CT principle. (Kastner, 2008a) The specimen is rotated in an X-ray beam that is gener- SPECIAL ISSUE ON INJECTION MOLDING AND MOLDS Intern. Polymer Processing XXVI (2011) 3 Ó Carl Hanser Verlag, Munich 283 * Mail address: Dietmar Salaberger, University of Applied Sciences, Upper Austria, Stelzhamerstr. 23, 4600 Wels, Austria E-mail: 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  2. 2. D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution ated by an X-ray tube. The X-rays are attenuated according to density and atomic number. The resulting projection is grabbed by a digital detector that usually works according to the scintil- lation principle. More than thousand projections are usually generated from different angles. These projections are recon- structed using mathematical algorithms resulting in a 3D data set which consists of volumetric pixels (voxels) with different grey values. After the specimen has been rotated once through 3608 the full representation of the specimen is available. Sub-lm-CT devices are equipped with X-ray tubes with very small focal spots to be able to minimize the effect of blur- ring. X-ray detectors with more than 2000 pixels in one direc- tion are well established. Best achievable resolutions are in the range of a few 100 nm. These devices are constructed in a desktop-style and can be bought from different manufacturers. The achievable resolution depends on the one hand on the CT equipment and on the other hand on the size of the speci- men. To be able to determine length and orientation very accu- rately a compromise has to be found between small sample size and representative volume. The quality of CT data also determines the way the analysis has to be carried out. Many different algorithms have been de- veloped to determine FOD from many different three dimen- sional data. (Requena et al., 2009; Vincent et al., 2005; Park et al., 2001) It is evident that it was mainly possible to segment single fibres with SCT data whereas global or averaged orien- tations were determined with l-CT data. The algorithms developed in-house for CT data analysis were designed to determine fibre orientation using l-CT data. (Kastner et al., 2008b) The resolution was in the range of the diameter of the fibres or even lower. The orientation could be determined accurately but the fibre length could not be deter- mined since data quality was inadequate. This publication deals with the application of the previously developed algorithms to sub-lm-CT data to determine fibre length distribution as accurately as possible. It will be shown how it was possible to estimate accuracy and how the algo- rithms were improved further. The influence of fibre content on the quality of the analysis is shown as well as a comparison of CT data analysis with the standard method of pyrolysis. 2 Experimental 2.1 Materials The material that was used for this study was injection- moulded polypropylene (PP) filled with long glass fibres (initi- al length 4.5 mm). Levels of fibre content of 1, 5, 10 and 30% by weight were analysed. The average diameter of the raw fi- bres was 13.5 lm. Two mould geometries were used: a standard tensile bar and a ring-shaped geometry. One additional ring was produced by extruding a tube and turning the ring from this tube. 2.2 CT Data Acquisition For the generation of CT data the sub-lm-CT device Nanotom (Phoenix|x-ray, Wunstorf, Germany) was used. The Nanofocus® tube was operated at 50 kV and a focal spot size of about 2 lm. A Hamamatsu detector with 2300 x 2300 pixels was used. For comparison, l-CT data was generated using the RayScan 250 E (RayScan Technologies, Meersburg, Germany) microfocus tube. To be able to visualise the complete cross section of a tensile bar test specimen of 4 by 10 mm edge length, it was possible to set the resolution to 6 lm per voxel. To improve image quality and reduce artefacts, measurements with a resolution of 2 lm per voxel were performed on a smaller part of the specimen. For the datasets for 1 and 5% fibre content, two sample vol- umes were cut out, a smaller and a larger one. For the two ring-shaped specimens the resolution was set to 5 lm per voxel because the outer diameter was 10 mm. Synchrotron-CT-measurements were carried out at the ESRF (European Synchrotron Radiation Facilities) in Greno- ble using monochromatic X-rays at 21.2 keV and a resolution of 0.7 lm. The sample volume that could be scanned was ap- proximately 1 mm in diameter. 2.3 CT Data Analysis Two basic concepts were developed to extract the information about fibre orientation and fibre length from CT data. The ba- sic workflow idea is to define a sequence of filters and subse- quently apply them. Pre-processing is necessary to reduce noise and increase contrast between fibres and matrix. For all datasets an aniso- tropic diffusion filter was applied to reduce noise without blur- ring the edges. The aim is to extract each and every fibre in such a way that start and endpoint are determined accurately. The first concept implemented was based on segmentation using a global threshold followed by a detailed analysis of re- gions where fibres are touching. This sequence of filters was proposed by Kastner et al. (2008c). The fibres are separated from the matrix by binarisation using the principle proposed by Otsu (1979). This guarantees for user independent and com- parable results of binarization. In the next step binary thinning is applied to extract only a single line of voxels for each fibre. The last step treats regions where fibres are touching, or “clus- ters”, in such a way that the fibres are separated into single ob- jects. The following parameters were set for the algorithms: . “Anisotropic diffusion”: iteration (10), time step (0.0625), conductance (5), . “Binarisation”: Otsu-value · 1.25, . “Cluster analysis”: kink angle: 1608, cluster distance: 14 lm (ca. fibre diameter). The performance of this concept will be discussed in this paper. It will be shown that the FOD can be determined very well but an accurate analysis of fibre details like fibre length is not pos- sible with this approach. Therefore a second concept was developed based on the ana- lysis of the original grey value image. The step of binarisation is not essential for this concept. For each voxel the local fibre ori- entation is calculated by calculating the Hessian Matrix and ana- lysing their Eigen values. A similar concept was proposed by Teßmann et al. (2010). In addition to the local orientation, the grey value distribution of an axial cross section through a fibre is taken into account. In the ideal case radial grey value profiles 284 Intern. Polymer Processing XXVI (2011) 3 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  3. 3. in every direction perpendicular to the direction within one cross section should follow a Gaussian distribution. This means that the centre of a fibre has the highest grey value. This is true for free-standing fibres but can be different in regions where fibres are touching. Due to the big difference in density between fibre and matrix (glass: 2.5 g/cm3 , PP: 0.9 g/cm3 ) artifacts are intro- duced into the CT data. These artifacts can also influence the ideal Gaussian grey value distribution. The result of this “ortho plane analysis” is a binary image where only the centre voxels of the fibres are set to foreground. In the touching areas this ap- proach guarantees that most of the fibres are separated from each other. This dataset can be used as input for the filters developed for the first concept to determine the start- and endpoint of each fibre. Information from concept 2 can also be used for improving the remaining cluster points’ analysis. In Fig. 1 the filters and data flow is shown for both concepts, see also Table 1. To evaluate the quality of software-concept 1, the standard 3D data analysis tool “VG Studio MAX 2.0” (Volume Graphics, Heidelberg, Germany) was used to determine the number of fi- bres accurately. The toolbox “Geometry analysis” was therefore used to fit cylinders by manually selecting each individual fibre. The fitted cylinders can be visualised within the slice images which makes it easy to count each fibre exactly once. 2.4 Simulation A CT-simulation tool was used to generate artificial data sets. The tool was implemented by Reiter et al. (2009) mainly to op- timise CT-scan parameters. For this study cylindrical geometries were defined using Constructive Solid Geometries (CSG) which are defined by surface equations. The advantage of sim- ulated data is that every artificial fibre is characterised precisely by the surface equation and the quality of CT data can be chosen by the simulation parameters. Some representative fibre network structures were chosen for the real data- sets that were modelled by CSG. The da- tasets were used to develop the software concepts and to evaluate them. 3 Results and Discussion The result of a CT scan with a sub-lm-CT device delivers much better quality for polymeric samples than a more common l-CT device. (Fig. 2) The reason is on the one hand better quality of the X-ray beam since the focal spot is very small and the energy spectrum has its maximum at low- er energies which leads to a better contrast for less absorbing materials like polymers. On the other hand the detectors used have 1024 (l-CT) and 2300 (sub-lm-CT) pix- els in a row which leads to half the voxel- size for the sub-lm-CT device for the same size of specimen. Fig. 2 shows slice images of data sets that were generated using different CT de- vices. It is clearly visible that the Synchro- tron CT measurement represents each and D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution Intern. Polymer Processing XXVI (2011) 3 285 Specimen 1% small 1% large 5% small 5% large 10% 30% Size (MB) 286 2080 80 1327 33 3.8 Voxels X 533 1064 388 754 210 125 Voxels Y 497 1001 217 751 204 124 Voxels Z 552 1000 485 1200 388 126 Fibres (manually counted) 82 – 107 – 175 112 Table 1. Overview of analysed data, voxel size: 2 lm, the specimens will hereinafter be re- ferred to as: “1% small”, “1% large”, “5% small”, “5% large”, “10%” and “30%” Fig. 1. Filter sequence for concept 1 and 2 Fig. 2. Frontal CT-slice images of different regions in an injection-moulded PP tensile bar with 10% glass fibres. (A) sub-lm-CT at 12 lm voxel size, (B) l-CT at 10 lm voxel size, (C) sub-lm- CT at 2 lm voxel size, (D) synchrotron-CT at 0.7 lm voxel size A) B) C) D) 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  4. 4. D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution every fibre very well. For sub-lm-CT and high resolution (Fig. 2C, 2 lm) the data quality is also good enough to separate single fibres. When resolution decreases (Fig. 2A, 12 lm) single fibres cannot be resolved properly any more. A similar result is achieved with l-CT with the disadvantage of only half the size of measurement volume compared to sub-lm-CT. 3.1 Determination of Fibre Orientation Distribution For two rings, one produced by injection-moulding, the other by extrusion, the software pipeline of concept 1 was applied. It was expected that the fibre orientations would display different be- haviour. For the extrusion process the main fibre orientation should point in the direction of extrusion. For injection-mould- ing the fibres should show a main orientation within the axial plane following the shape of the ring. This behaviour is clearly visible in Fig. 3. On the right most fibres are coloured red which shows that most of the fibres are oriented in the X-direction. On the left of the image the fibres are mainly coloured green and blue which means that most fibres are lying in parallel planes that are oriented in the direction of injection. In addition one can detect the position of the gate and a weld line because at these points the expected fibre orientation is disturbed. Quantitative analysis of the orientation can be performed by calculating a mean fibre orientation tensor for the whole speci- men (Table 2). The elements of the main diagonal (a11, a22, a33) describe the strength of orientation in the three axes of co- ordinates. The results for the injection-moulded ring show simi- lar values in the plane of the flow (Y and Z) and the lowest value in the third coordinate axis. The value in direction X is highest for the extruded ring since this is the direction of extrusion. Very similar results can be gained by performing l-CT scans. Even with limited data quality or limited resolution the orientation of the fibres is visible and can be quantified using appropriate software algorithms. 3.2 Evaluation of Data Analysis Concepts 3.2.1 Qualitative Evaluation For the design of filters for concept 2, artificial CT data sets were produced using CT simulation. Scenarios of touching fi- bres, which are common in the high con- tent specimens, were simulated to be able to investigate the influence of filter pa- rameters and different algorithms on the extracted fibres. The results of this inves- tigation lead to the choice of filters for the improved concept 2. Two scenarios that were simulated are shown in Fig. 4. Most of the typical struc- tures in the region of fibres touching can be analysed correctly. In Fig. 4D the long vertical fibre is broken because of the sharp angle between the two touching fi- bres. Especially this kind of touching structure and also parallel fibres were found most difficult for analysing. Since every fibre in the artificial data sets is defined entirely, these data sets will also be used as reference for parame- ters like fibre diameter, surface and vol- ume. 3.2.2 Quantitative Evaluation To be able to identify how many fibres within the analysed volume are repre- sented correctly, the total number of fi- bres was determined by a manual data analysis. For each fibre a cylinder was fitted to guarantee counting each fibre 286 Intern. Polymer Processing XXVI (2011) 3 Fig. 3. 3D views of the extracted fibres for an injection-moulded (left) and an extruded (right) ring. The orientation of the fibres is visualised by colour coding with voxel size: 5 lm Orientation Production Injection-moulded Extruded a11 (X) 0.26 0.49 a22 (Y) 0.38 0.25 a33 (Z) 0.36 0.26 Table 2. Elements of main diagonal of orientation tensor for two ring-shaped specimens 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  5. 5. only once. In Fig. 5 the fibres are shown semi-transparent so the fitted cylinders as well as the fibres can be seen. For the evaluation of the algorithms all specimens were scanned at 2 lm voxel size. The aim was not only to analyse the quality of the result of extraction by look- ing at images but also to quantify errors. The problem is the determination of the objective fact since no other 3D method is available to extract fibres and a com- parison with destructive methods is not absolutely valid. Quantitative evaluations were performed using concept 1 only. In a first step the result of the automated analysis was evaluated in terms of correct number of fibres, see Table 3. For investigation of the reason for the errors in number of fibres, images of the binarised, the binary thinned as well as the extracted fibres were analysed. (Fig. 5) These images showed that prob- lems only occur in points where fibres are touching. Free fibres are represented correctly. Two reasons were identified for the wrong extraction for concept 1: binarisation and binary thinning. Due to artifacts that influence the grey values for the fibres in different regions of the data set, global thresholding will lead to the loss of small fibres, if the threshold is too high. On the other hand the touch- ing areas will increase if the threshold is too low. Areas of touching fibres, the clusters, affect the result of bin- ary thinning in such a way that the number of fibres cannot be reconstructed in this area. Fibres that run parallel within a small distance cannot be separated using concept 1. The aver- age orientation that can be calculated for predefined volumes is not affected much by these limitations of the applied filters. Fig. 5 shows intermediate results of the filter pipeline of con- cept 1 and concept 2. The original image contains seven fibres that are touching at different angles. The images show the lim- itations of applying binary thinning to the binarised data (Fig. 5D). It is clearly visible that the two horizontal fibres can- not be represented correctly on the right of the sample. In the im- age of extracted fibres (Fig. 5F) one can count eight fibres but more than one fibre is not represented correctly. Applying con- cept 2 to the original data the medial region of the fibres can be extracted well (Fig. 5C). Only very small fragments remain mainly in cluster regions and at the surface of the fibres. These fragments usually have a length smaller than the diameter and can be removed by applying a length threshold at the end of the evaluation without losing real fibres. Image (Fig. 5E) shows the correct number and representation of fibres. D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution Intern. Polymer Processing XXVI (2011) 3 287 Fig. 4. 3D views of two simulated fibre network scenarios. Original grey value image (A, C), fi- bre extraction result (B, D) A) B) C) D) E) F) Fig. 5. 3D images of subsequent results of different stages of the data analysis pipeline. Cluster regions for 20% fibre content are shown: original (A), binarisation (B), skeleton (C, D), extracted fibres (E, F), concept 1 (C, E), concept 2 (D, F) Specimen Manually counted fibres Total fibres Error in No. of fibres % 1% small 85 99 16.5 5% small 107 112 4.7 10% 175 179 2.3 30% 112 130 16.1 Table 3. Comparison of number of fibres counted manually and total fibres extracted automatically 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  6. 6. D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution The effects of binarisation using global threshold and binary thinning lead to the conclusion, that taking only the error in the number of fibres into account is not sufficient to estimate the overall error of the whole fibre extraction process. From the fact that free fibres are always extracted in a cor- rect way, we can define a minimum probability, PM, for ex- tracting the fibres correctly (Table 4): PM ¼ Free fibres Total fibres Á 100 %: For the cluster fibres it turned out that, depending on the fibre content, the error varies. Fig. 5 shows the main disadvantage of global thresholding and binary thinning. Depending on the grey value threshold in regions where fibres are close together or touching, the area of touching varies. In the subsequent step of binary thinning this area is transformed into a line of voxels. The available cluster analysis was not capable of extracting and combining the fibre fragments properly. Since the complexity of the analysis and also artifacts in- crease with increasing fibre content a trust factor t was defined. The factor t was chosen by experience and was used to estimate an average probability of extracting fibres correctly. The idea is to estimate how many fibre segments are extracted and joined together correctly. Different common geometries were found in the touching areas. The complexity of these geome- tries in respect of cluster analysis is similar for 1 and 5% fibre content and is higher for 10 and 30%. The factor t was there- fore set to 0.5 for 1 and 5% and to 0.25 for 10 and 30%. This means that we expect a chance of 50% to join fibre segments correctly in cluster regions for 1 and 5% fibre content (Ta- ble 5). PA ¼ Cluster fibres Total fibres Á t Á 100 % þ PM: The problem discussed above was overcome to a great extent by introducing the second software concept. Avoiding apply- ing a global threshold leads to a much better representation of the medial region. Applying binary thinning to this data leads to a correct separation of fibres even with high fibre content. 3.3 Influence of Size of Analysed Volume on FLD and FOD Since every fibre is represented by its start and endpoint after extraction it is easy to calculate the orientation tensor for every fibre. In Table 6 the average values for a11 (X-direction), a22 (Y-direction) and a33 (Z-direction) for all specimens with 1% fibre content are shown. Fig. 6 shows a comparison of original CT data and the result of fibre extraction according to concept 1. The top image shows the manually fitted cylinders and the fibres whereas the image at the bottom shows the extracted fi- bres using glyphs that connect every start and endpoint. Most of the extracted fibres have corresponding fibres in the original data. For bent fibres a straight line is drawn since the algorithm was not designed for bent fibres and the represented glyphs are straight lines. The colour of the glyphs gives infor- mation about the orientation. It can be seen that many fibres are coloured red, which means that there is a preferred orienta- tion. In this case it is the z-direction which is the direction of in- jection. Also the quantitative analysis shows the same result as a33 has the highest value in both sample volumes. A small sample volume is needed to achieve high resolu- tions. Long fibres may not be completely contained in the sam- ple volume. To investigate this effect, two different data sets were generated for one scan of the 1% specimen. The edge length of the small sample volume was ca. 1.1 mm and ca. 2 mm for the larger sample volume. Fig. 7 and Fig. 8 show the results of the automated fibre extraction in terms of fibre length. The first length class was set to approximately twice the fi- bre diameter (28 lm). Elements in this class are generally fibre fragments that don’t have the ideal cylindrical shape of a fibre 288 Intern. Polymer Processing XXVI (2011) 3 Specimen Total fibres Free fibres Minimum probability PM % 1% small 99 66 67 5% small 112 77 69 10% 179 87 49 30% 130 19 15 Table 4. Estimate of the minimum probability of extracting the fibres correctly from the number of free fibres Specimen Cluster fibres t Average probability PA % 1% small 33 0.50 84 5% small 35 0.50 85 10% 92 0.25 62 30% 111 0.25 36 Table 5. Estimation of average probability to extract fibres cor- rectly Specimen Fibre count a 11 X a 22 Y a 33 Z Mean fibre length lm Max fibre length mm 1% small 84 0.12 0.18 0.70 417 1.12 1% large 493 0.24 0.08 0.68 446 1.65 Table 6. Number of extracted fibres that are longer than twice the diameter (28 lm), elements of the main diagonal of the orientation tensor and mean and maximum fibre length; concept 1 applied 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  7. 7. any more. There is no contribution by these fibres to reinforce- ment since a critical fibre length can be determined (Vas, 2009) that is the minimum necessary for reinforcement. FLD for both sample volumes shows a similar result. The average fibre length without fibre fragments is 417 lm for the small and 446 lm for the larger volume. In the larger sample vol- ume the frequency of the fragments is, at 33%, higher than in the small volume, at 15%. An error will underlay the results of FLD according to the previous results of data analysis evaluation. Nevertheless, the results accord well with the visual inspection since for example fibre fragments are visible in the slice images as well. The proportion of long fibres is very similar in both sam- ple volumes. The longest fibre in the smaller sample volume is 1.12 mm and in the larger sample volume 1.65 mm long. 3.4 Comparison of Standard Method and CT Data Analysis for FLD For a tensile bar test specimen, filled with 5% by weight glass fibres, a well established method was applied to determine FLD. The matrix was burned by pyrolysis, the remaining fibres were then analysed by light optical microscopic analyses. The analysis was carried out for 100 fibres with a minimum fibre length of 43 lm. The comparison of length distribution determined by CT and the standard method show slightly different results. A shift D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution Intern. Polymer Processing XXVI (2011) 3 289 A) B) Fig. 6. 3D images of original CT data (A) and extracted fibres (B) for 1% fibre content and small sample volume Fig. 7. Fibre length distribution for 1% fibre content and small sam- ple volume (edge length: 1.1 mm) Fig. 8. Fibre length distribution for 1% fibre content and large sam- ple volume (edge length: 2 mm) 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  8. 8. D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution towards smaller lengths can be observed for CT analysis. While the number of fibres analysed using CT data and con- cept 1 is 1881, which is much higher compared to 100 fibres for the standard method, mean fibre length differs from 352 lm to 751 lm. From concept 2, 1942 fibres were ex- tracted and the mean fibre length was 373 lm. FLD determined by concept 1 and concept 2 are almost sim- ilar and shown in Fig. 9. Unfortunately it was not possible to investigate exactly the same volume of the specimen with both methods. However two specimens were prepared from the same tensile bar. Differences in FLD can be explained by the small number of fibres which were analysed by the standard method. In a small degree remaining errors in the software concepts contribute to the difference. 4 Conclusions Investigations were presented on the application of X-ray com- puted tomography to determine fibre length and fibre orienta- tion distribution. In a first step, a CT-data analysis pipeline was developed and applied to CT-data of specimens with fibre content be- tween 1 and 30% by weight. 2 lm voxel size was identified as applicable voxel resolution to be able to achieve sufficient data quality together with acceptable sample volume. For eva- luation of the software concept, error estimates were carried out that showed an increase in error with increasing fibre con- tent. The reason is the higher complexity of fibre network with higher fibre content. The number of regions, where fibres are touching, increases. The analysis of these regions is a crucial task within the software pipeline because in contrast to free fi- bres, the cluster regions induce errors in fibre extraction. For the reduction of errors new filters were designed to pre- process the data for subsequent cluster analysis. The improvement could be shown on small samples of real data sets and simulated data containing representative cluster arrangements. The comparison of CT data analysis and standard FLD de- termination showed a shift to smaller fibre lengths for CT data analysis. The expected error for 5% fibre content is low and the num- ber of free fibres is increased using concept 2. In addition the difference of FLD between concept 1 and concept 2 is rather small. For the investigated specimen this can lead to the con- clusion that the FLD from CT-data analysis is more realistic than the FLD from the given pyrolysis analysis. An issue that has to be investigated more closely is the influ- ence of the volume that is analysed. It was shown that not all fi- bres are completely contained within the analysed volume which leads to a shift of lengths to lower values. The investiga- tions on the 1% specimen showed that the FLD changes only slightly when the analysed volume is increased fourfold. Espe- cially for areas where fibres are mainly oriented in one direc- tion the size of specimen in this direction should be high. For ring-shaped specimens the advantages of 3D tomo- graphic methods were demonstrated. Both FOD and FLD can be determined using the same data set without destroying the specimen. Acknowledgements This study was supported by the Austrian Research Promotion Agency (FFG). We thank our partner companies Borealis Poly- olefins and Transfercenter for Polymer Technology (TCKT) for delivering specimens and performing standard testing. Spe- cial thanks go to Christoph Heinzl and Michael Reiter, who supported the development of software concepts. References Bradsky von, G. J., et al., “Characterisation of Finite Length Compo- sites – Part IV: Structural Studies on Injection-Moulded Compo- sites”, Pure Appl. Chem., 12, 2523–2539 (1997) Clarke, A. R., et al., “A Novel Technique for Determining the 3D Spa- tial Distribution of Glass Fibres in Polymer Composites”, Comp. Sci. Tech., 55, 75–91 (1995), DOI:10.1016/0266-3538(95)00087-9 Fu, S., et al., “Correction of the Measurement of Fiber Length of Short Fiber Reinforced Thermoplastics”, Composites Part A., 33, 1549– 1555 (2002), DOI:10.1016/S1359-835X(02)00114-8 Kastner, J. (Ed.): ,,Industrielle Computertomografie Tagung, Wels, Austria, Shaker Verlag, Aachen (2008a) Kastner, J., et al., “Determination of Diameter, Length and Three-di- mensional Distribution of Fibres in Short Glass-fibre Reinforced In- jection-moulded Parts by l-X-ray Computed Tomography”, Pro- ceedings PPS24, Salerno, Italy (2008b) Kastner, J., et al., “Method for Three-dimensional Evaluation and Vi- sualization of the Distribution of Fibres in Glass-fibre Reinforced Injection Molded Parts by l-X-ray Computed Tomography”, WCNDT Proceedings CD, Shanghai (2008c) Matsuoka, T., “Chapter 3 Fiber Orientation Prediction in Injection Molding”, in Polypropylene Structure, Blends and Composites, Vol- ume 3 Composites, Karger-Kocsis, J. (Ed.), Chapman Hall, Lon- don, p. 113–141 (1995) Otsu, N., “A Threshold Selection Method from Grey Level Histo- grams”, IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–66 (1979) 290 Intern. Polymer Processing XXVI (2011) 3 Fig. 9. Fibre length distribution for 5% fibre content and larger sam- ple volume, concept 1 and length threshold 43 lm applied 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.
  9. 9. Park, C. H., et al., “A Study on Fibre Orientation in the Compression Molding of Fibre Reinforced Polymer Composite Materials”, Mat. Proc. Tech., 11, 233–239 (2001), DOI:10.1016/S0924-0136(01)00523-4 Phelps, J. H., et al., “New Models for Fibre Orientation in Injection Molded Composites”, Proceedings PPS24, Salerno, Italy (2008) Reiter, M., et al., “Improvement of X-ray Image Acquisition Using a GPU Based 3D CT Simulation Tool”, SPIE Quality Control by Arti- ficial Vision Wels, Austria (2009) Requena, G., et al., “3D-Quantification of the Distribution of Continu- ous Fibres in Unidirectionally reinforced Composites”, Composites Part A, 40, 152–163, (2009), DOI:10.1016/j.compositesa.2008.10.014 Shen, H., et al., “Direct Observation and Measurement of Fibre Archi- tecture in Short fibre Polymer Composite Foam through Micro-CT Imaging”, Comp. Sci. Tech., 64, 2113–2120 (2004), DOI:10.1016/j.compscitech.2004.03.003 Teßmann, M., et al., “Automatic Determination of Fiber Length Distri- bution in Composite Material Using 3D CT Data”, EURASIP Jour- nal on Advances in Signal Processing, article ID 545030 (2010), DOI:10.1155/2010/545030 Vas, L. M., et al., “Active Fiber Length Distribution and its Applica- tion to Determine the Critical Fiber Length”, Polym. Test., vol. 28, Oct. 2009, pp. 752–759 Vincent, M., et al., “Description and Modelling of Fibre Orientation in Injection Molding of Fibre Reinforced Thermoplastics”, Polymer, 46, 6719–6725 (2005), DOI:10.1016/j.polymer.2005.05.026 Zak, G., et al., “Estimation of Average Fibre Length in Short-fibre Composites by a Two-Section Method”, Comp. Sci. Tech., 60, 1763–1772 (2000), DOI:10.1016/S0266-3538(00)00065-8 Date received: October 30, 2010 Date accepted: March 4, 2011 Bibliography DOI 10.3139/217.2441 Intern. Polymer Processing XXVI (2011) 3; page 283–291 ª Carl Hanser Verlag GmbH Co. KG ISSN 0930-777X You will find the article and additional material by enter- ing the document number IIPP2441 on our website at D. Salaberger et al.: Evaluation of CT Data to Determine Fibre Length Distribution Intern. Polymer Processing XXVI (2011) 3 291 2011CarlHanserVerlag,Munich,Germanywww.polymer-process.comNotforuseininternetorintranetsites.Notforelectronicdistribution.