Surface braiding angle and pitch length are two important parameters for characterizing and evaluating the performance of three-dimensional braided composite preforms. In this paper a new method based on an improved curvature scale space corner detector with adaptive threshold is proposed for measuring these parameters, with applications to three-dimensional, four-directional carbon-fiber braided composite preforms. First, the original image is acquired using a system employing ‘dome light source + CCD camera + circular polarizing filter’. Second, the original image is processed using Lab transform and BM3D (block-matching and 3D filter). Third, the corners are detected using an improved curvature scale space corner detector with adaptive threshold. Finally, the pitch lengths and surface braiding angles are measured from the detected corners. Experimental results show that the proposed method can achieve automatic measurement of the pitch length and surface braiding angle with smaller average errors relative to manual measurements compared with alternative schemes.
BalokasGCzichonSRolfesR. Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty. Compos Struct2017. in press.
2.
XuanHLiuLChenGet al.Impact response and damage evolution of triaxial braided carbon/epoxy composites. Part I: Ballistic impact testing. Text Res J2013; 83(16): 1703–1716.
3.
BilisikK. Multiaxis 3D woven preform and properties of multiaxis 3D woven and 3D orthogonal woven carbon/epoxy composites. J Reinf Plas Compos2010; 29(8): 1173–1186.
4.
PotluriPRawalARivaldiMet al.Geometrical modelling and control of a triaxial braiding machine for producing 3D preforms. Compos Pt A Appl Sci Manuf2003; 34(6): 481–492.
5.
AhnHYuWR. Mechanical analysis of 3D braided and woven composites using fiber-based continuum analysis. Compos Struct2017; 160: 1105–1118.
6.
DongKZhangJJinLet al.Multi-scale finite element analyses on the thermal conductive behaviors of 3D braided composites. Compos Struct2016; 143: 9–22.
BilisikK. Three-dimensional axial braided preforms: experimental determination of effects of structure–process parameters on unit cell. Text Res J2011; 81(20): 2095–2116.
9.
LiBLinLZhuJet al.Mechanical properties and microstructure of 3D 6-directional braided sio2f/sio2 composites by silicasol-infiltration-sintering method. Asian J Chem2013; 25(1): 23–26.
10.
WanZLiJ. Braided angle measurement technique for three-dimensional braided composite material preform using mathematical morphology and image texture. AUTEX Res J2006; 6(1): 30–39.
11.
KostarTDChouTW. A methodology for Cartesian braiding of three-dimensional shapes and special structures. J Mat Sci2002; 37(13): 2811–2824.
12.
AtmacaMDalVYilmazAet al.Investigation of the effects of fabric parameters on air permeability of woolen fabrics. Text Res J2015; 85(20): 2099–2107.
13.
GaoYLiJ. Effects of braiding angle on modal experimental analysis of three-dimensional and five-directional braided composites. Compos Part B Eng2012; 43(5): 2423–2428.
14.
ByunJHChouTW. Process-microstructure relationships of 2-step and 4-step braided composites. Compos Sci Technol1996; 56(3): 235–251.
15.
WanZShenJWangX. Measure and research on braided angle of composites preform. J Text Res (in Chinese)2004; 25(3): 42–43.
GongLWanZ. Automatic measurement technology on braided pitch length of three-dimensional braided composite material preform. Comput Meas Control (in Chinese)2006; 14(6): 730–733.
18.
He XC and Yung NHC. Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: Proceedings of the 17th International Conference on Pattern Recognition volume 2, Cambridge, UK, 23–26 August 2004, pp.791–794.
19.
ZhengYSunYLiJet al.Tensile response of carbon-aramid hybrid 3D braided composites. Mat Des2017; 116: 246–252.
20.
UmeyamaSGodinG. Separation of diffuse and specular components of surface reflection by use of polarization and statistical analysis of images. IEEE Trans Pattern Anal Mach Intell2004; 26(5): 639–647.
21.
Dabov K, Foi A, Katkovnik V, et al. Image denoising with block-matching and 3D filtering. In: Proceedings of SPIE, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning volume 6064, San Jose, CA, USA, 16–18 January 2006, pp.1–12.
22.
Hernández-LópezJJQuintanilla-OlveraALLópez-RamírezJLet al.Detecting objects using color and depth segmentation with Kinect sensor. Procedia Technol2012; 3: 196–204.
23.
CuiJXieJLiuTet al.Corners detection on finger vein images using the improved Harris algorithm. Optik-International Journal for Light and Electron Optics2014; 125(17): 4668–4671.
24.
Harris C and Stephens M. A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, Manchester, UK, 31 August–2 September 1988, pp.147–151.
25.
CannyJ. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell1986; 8(6): 679–698.
26.
Zhang G and Zhu W. Constructing and application of adaptive spline wavelet filter coefficients in image edge detection. In: IEEE 2006 International Conference on Communications, Circuits and Systems, Guilin, China, 25–28 June 2006, pp.425–428.
27.
ZhuXDengXHuD. An edge detection algorithm with multi-threshold selection and edge connection. J Graph (in Chinese)2012; 33(2): 72–76.
28.
OrfanidisJS. Optimum signal processing: An introduction, 2nd ed. New York: Macmillan Publishing Company, 1988.
29.
Kovesi P. Phase congruency detects corners and edges. In: Proceedings of the 7th International Conference on Digital Image Computing: Techniques and Applications, Sydney, Australia, 10–12 December 2003, pp.309–318.
30.
LamLLeeSWSuenCY. Thinning methodologies: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell1992; 14(9): 869–885.
