Impact damage characterization in CFRP plates using PCA and MEEMD decomposition methods in optical lock-in thermography phase images

Carbon fiber reinforced plastics (CFRP) are composite materials which are an interesting alternative to metal alloys in fields such as oil, aerospace, automotive, since CFRP have mechanical properties like metals but with a fraction of their weights. However, these materials have typically a highly anisotropic behavior, which may hinder the characterization of their integrity for example when subjected to an impact, because of its stochastic nature. Non-destructive testing (NDT) methods are interesting for integrity assessment, as they can evaluate the damage extension without affecting any part characteristics. Optical lock-in thermography (OLT) is an convenient NDT inspection alternative since it is a depth-wise method in which one can set different loading frequencies, leading to different scan depths. Pre-processing techniques like Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD) can be used to more accurately evaluate the damaged area. Their dimensionality reduction capability is highly desired as OLT images of CFRP laminates do not only show the defect, but also undesired information such as changes of background radiation, noise and the disposition of the fiber tissue. Traditional feature extraction methods must be highly tuned to obtain useful results. PCA and EMD methods may be considered non-supervised approaches, making them useful for a wide variety of inputs. However, PCA and EMD have their own natural limitations when being applied to images. While PCA may require high computational effort because of mathematical manipulation of matrices due to the mathematical manipulation of large matrices, problems due to mode superposition may occur if EMD original method is applied on images. In this sense, in this work it has been performed a comparison between PCA, with a new input vector architecture used to mitigate its problem with matrix dimensions, and a derivation of EMD method, called Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), applied to prevent the previously mentioned superposition but without a higher computational effort, when segmenting OLT phase images. Outputs in both cases are binary masks with an estimation of the defect region, which were compared to a ground truth manually defined by a specialist. Matthews Correlation Coefficient (MCC) was chosen as segmentation comparison metric instead of F-score, since the last one does not take true negatives into account. The main difference during the application of PCA and MEEMD is that MEEMD yields two kinds of results, one for each frequency image and one average for the whole frequency set, whereas PCA gives only results in relation to the average. This study shown that PCA has better performance than MEEMD when the average results are compared, but MEEMD provides better results if onlt the best image per frequency set is used. Both PCA and MEEMD can yet provide interesting results for three-dimensional reconstruction with OLT images, thus further investigation of such techniques is desirable.