Low energy impact damage in composite materials may be more concerning than it appears visually, often requiring a detailed examination for accurate assessment to ensure safe and sustainable operation. Non-destructive testing (NDT) methods provide such inspection techniques, and in this paper, NDT-based fusion is explored for enhanced identification of defect size and location compared to indepdently using individual NDT methods separately. Three Carbon Fiber Reinforced Polymer (CFRP) specimens are examined, each with an impact damage of a given energy level, using pulsed thermography (PT) and phased array (PA) ultrasonic methods. Following the extraction of binary defect shapes from source images, a decision-level fusion approach is performed. The results indicate that combining ultrasonic and infrared thermography (IRT) inspections for CFRP composite materials is promising to achieve enhanced and improved detection traceability.
This work presents a fast robotized line-scan thermography (LST) modality for the inspection of two paintings on canvas, which are the mock-ups of a famous oil painting, titled Portrait of the Painter's Mother (by James Abbott McNeill Whistler, 1871). Here, a new image restoration (IR) post-processing technique is proposed, which combines both external and internal information for the high-performance reconstruction of the LST data. In the IR technique, a probabilistic atlas is used to model the spatial distribution of gradients, which correspond to various anatomical structures in the LST data. This atlas is then employed to control the level of gradient regularization at each image location, within a weighted total variation regularization prior. IR also leverages the redundancy of nonlocal similar patches through a sparse representation model. Experiments show that IR method outperforms the current proposed approaches; this, for different sampling rates and noise levels. In addition, x-ray imaging was used for comparative purposes. It was concluded that the proposed LST-IR modality is an effective technique for artwork fast inspection, and it can additionally provide a higher image contrast if compared to the state-of-the-art post-processing modalities such as those commonly used in the classical pulsed thermography technique. To reach these conclusions, physical analyses were also conducted. Finally, this work appears useful to investigate the feasibility of a fast robotized modality for large-scale artwork in-line inspection.
Non-destructive testing applications are one of the most crucial steps in maintaining aviation activities in a profitable and timely manner. Infrared thermography (IRT) is a functional technique that uses the thermal radiation/temperature relationship on the inspected structure to ensure efficient detection, in particular when the defect is on a surface or near the surface. Ultrasonic (UT) inspection is an alternative technique that uses the propagation of ultrasound waves into the inspected material for defect detection. While IRT suffers from detectability problems with the increasing structure thickness, UT has inspection limitations on the surface or near-surface area according to applied frequency. Overcoming these limitations of individual methods with the synergistic effect of the fusion approach might provide more precise and apparent marks for defect detection. In this study, decision-level fusion has been applied using the maximum fusion rule to combine unimodal inspection data and compare. Impact-defected Carbon Fiber Reinforced Polymer (CFRP) composite structures have been chosen to represent aerospace structures. The results show the proposed fusion approach is promising in terms of identifying defect location, size and depth to inform further stages such as repair.
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