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.
Composite materials unarguably represent the important structure parts in most modern transport applications such as the aerospace sector. One area that shows great potential in the battle against aircraft structural damage and the diagnosis of composite materials. Very often, detection and diagnosis tools offer a valuable and quick mechanism to the analysts and assist them in the monitoring of the health integrity of the composite materials. Although numerous initiatives to develop damage detection techniques and make operations more efficient were launched, there is still an on-going need to develop/improve upon the existing methods. In this work, Pulsed Thermography (PT) technique was used to acquire healthy and faulty datasets from specially designed composite samples of the same dimensions (300 mm x 300 mm x 2 mm) with three different geometries (planar, curved and trapezoidal). Three plates from carbon fibre-reinforced plastic (CFRP) were tested. The same defects distribution was first introduced to the different samples and the variation of surface temperature over time, and the flow of transient heat generated through an energy stimulus in the samples were then monitored. A machine learning (A Cubic Spine Support Vector Machine) based technique was applied to the resulting thermographic images in order to detect and classify damage on composite structures. The proposed classification model was evaluated for its performance using the common metrics such as the overall accuracy, sensitivity, precision, specificity, etc. It was concluded that the classification approach could provide a reliable estimate of composite material conditions and eventually could lead to 'go / no-go' decisions.
Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.
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