Classification of Cracks in Composite Structures Subjected to Low-Velocity Impact Using Distribution-Based Segmentation and Wavelet Analysis of X-ray Tomograms

Abstract

The problem of characterizing the structural residual life is one of the most challenging issues of the damage tolerance concept currently applied in modern aviation. Considering the complexity of the internal architecture of composite structures widely applied for aircraft components nowadays, as well as the additional complexity related to the appearance of barely visible impact damage, prediction of the structural residual life is a demanding task. In this paper, the authors proposed a method based on detection of structural damage after low-velocity impact loading and its classification with respect to types of acting stress on constituents of composite structures using the developed processing algorithm based on segmentation of 3D X-ray computed tomograms using the rebmix package, real-oriented dual-tree wavelet transform and supporting image processing procedures. The presented algorithm allowed for accurate distinguishing of defined types of damage from X-ray computed tomograms with strong robustness to noise and measurement artifacts. The processing was performed on experimental data obtained from X-ray computed tomography of a composite structure with barely visible impact damage, which allowed better understanding of fracture mechanisms in such conditions. The gained knowledge will allow for a more accurate simulation of structural damage in composite structures, which will provide higher accuracy in predicting structural residual life.

Gumbel mixture modelling for multiple failure data

“…The minimum form has been studied less, since the type 𝐼𝐼𝐼 or Weibull distribution has been more often used for such applications [21,36]. However, in a mixture setting, one sub-population could arise from the minimum distribution and another from the maximum distribution [14,24]. For such cases, linking both forms can be fruitful.…”

“…One of them is the Gumbel distribution. Recently, the advantages of the Gumbel mixture model have also been reported [19,24]. In most studies, only the maximum form of the Gumbel distribution is used to model mixtures [10,11,19,25].…”

On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks

“…However, they do not require large training datasets, are faster, require less computational power, and, most importantly, can be used in conjunction with newer classification methods [3,12,13]. They are also much more useful than their supervised counterparts for certain domain-specific tasks where prior knowledge is lacking, whether due to a lack of plausible training images or other factors [12,13,19,20].…”