Improvements to image processing algorithms used for delamination damage extraction and modeling

Assessment and identification of delamination damage in composite materials is necessary for safe operation of modern composite-based aircraft components. Nowadays, numerical simulations provide increasingly reliable predictions of delamination damage growth. However, these simulations often incorporate simplified or idealized models of the initial damage. Authors created a software-based method of extraction of the actual damage and of mapping of the damage to a finite element model. The software utilizes ultrasonic C-Scan images for the mapping process. In the paper, an improvement to the image processing algorithm used in the method is presented. In essence, a metric for the quality of processing has been introduced – this metric will be used for optimal selection of processing parameters in future versions of the software. Simple, impact tested composite coupons were used as an input for the method. Results of the processing, including calculation of the quality metric are presented in

[1]  Toby P. Breckon,et al.  Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab , 2011 .

[2]  John C. Aldrin,et al.  Design and demonstration of automated data analysis algorithms for ultrasonic inspection of complex composite panels with bonds , 2016 .

[3]  R. Challis,et al.  AUTOMATED NON-DESTRUCTIVE ANALYSIS AND ADVANCED 3 D DEFECT CHARACTERISATION FROM ULTRASONIC SCANS OF COMPOSITES , 2009 .

[4]  Pablo D. Ruiz,et al.  Identification of subsurface delaminations in composite laminates , 2007 .

[5]  Karolin Baecker,et al.  Two Dimensional Signal And Image Processing , 2016 .

[6]  Robert A Smith,et al.  Automated analysis and advanced defect characterisation from ultrasonic scans of composites , 2009 .

[7]  William P. Winfree,et al.  Advanced image processing for defect visualization in infrared thermography , 1998, Defense, Security, and Sensing.

[8]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[9]  Robert A. Smith,et al.  Use of 3D ultrasound data sets to map the localised properties of fibre-reinforced composites , 2010 .

[10]  P. Wilcox,et al.  Ultrasonic tracking of ply drops in composite laminates , 2016 .

[11]  B. Drinkwater,et al.  Detection of Fibre Waviness Using Ultrasonic Array Scattering Data , 2013 .

[12]  Ahmad Osman,et al.  Automated Evaluation of Three Dimensional Ultrasonic Datasets , 2014 .

[13]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .

[14]  Robert A. Smith,et al.  Modelling the mechanical properties of as-manufactured composite components based on 3 D non-destructive characterisation , 2016 .

[15]  Krzysztof Dragan,et al.  Ultrasonic C-Scan Image Processing Using Multilevel Thresholding for Damage Evaluation in Aircraft Vertical Stabilizer , 2015 .