Abstract
Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.
Highlights
Due to the unique features of Carbon-fibre-reinforced polymers (CFRP)—low-density and high-performance physico-chemical properties—the interest in using these lighter products and replacing the conventional materials (Steel, aluminum, etc.) has increased
This study demonstrates its efficiency for defect enhancement capabilities over mixed and various types of defects typically addressed in IRT in composite materials
The present study investigates the benefits of the low-rank matrices for pulsed thermography
Summary
Due to the unique features of Carbon-fibre-reinforced polymers (CFRP)—low-density and high-performance physico-chemical properties—the interest in using these lighter products and replacing the conventional materials (Steel, aluminum, etc.) has increased. The increasing demand for CFRP structures in the aerospace industry is leading to the development of enhanced more eco-efficient manufacturing [1]. Due to the different types of defects during the manufacturing process or the service life of the components, it is important to monitor their efficiency and functionality non-invasively. Among non-destructive testing techniques, infrared thermography, which involves mapping the surface temperatures, can characterize the surface and sub-surface anomalies. Pulsed thermography (PT) is a no-contact and full-field Infrared Non-Destructive Testing (IRNDT) approach based on thermal heat transfer analysis during the cooling period; after the thermal impulse, an incident to the sample’s surface becomes a thermal wave due to conduction and propagates through the material. Manipulating thermal data makes active thermography an attractive and powerful approach for industrial control and maintenance purposes
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