Search Results (9)

The aviation industry currently faces several challenges in inspecting and diagnosing aircraft structures. Current aircraft inspection methods still need to be fully automated, making early detection and precise sizing of defects difficult. Researchers have expressed concerns about current aircraft inspections, citing safety, maintenance costs, and reliability issues. The next generation of aircraft inspection leverages semi-autonomous and fully autonomous systems integrating robotic technologies with advanced Non-Destructive Testing (NDT) methods. Active Thermography (AT) is an example of an NDT method widely used for non-invasive aircraft inspection to detect surface and near-surface defects, such as delamination, debonding, corrosion, impact damage, and cracks. It is suitable for both metallic and non-metallic materials and does not require a coupling agent or direct contact with the test piece, minimising contamination. Visual inspection using an RGB camera is another well-known non-contact NDT method capable of detecting surface defects. A newer option for NDT in aircraft maintenance is 3D scanning, which uses laser or LiDAR (Light Detection and Ranging) technologies. This method offers several advantages, including non-contact operation, high accuracy, and rapid data collection. It is effective across various materials and shapes, enabling the creation of detailed 3D models. An alternative approach to laser and LiDAR technologies is photogrammetry. Photogrammetry is cost-effective in comparison with laser and LiDAR technologies. It can acquire high-resolution texture and colour information, which is especially important in the field of maintenance inspection. In this proposed approach, an automated vision-based damage evaluation system will be developed capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using an RGB camera and a IRT camera through photogrammetry. Such a combined approach is expected to improve defect detection accuracy, reduce aircraft downtime and operational costs, improve reliability and safety and minimise human error. Full article

Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges. Full article

Corrosion is a persistent challenge in the aviation industry, affecting the safety, performance, and maintenance costs of aircraft. While composite materials have gained widespread use due to their lightweight properties and corrosion resistance, certain critical parts, such as the wing and empennage leading edges and the engine inlet, demand alternative solutions. Aluminum, titanium, and stainless steel emerge as mandatory materials for such components, given their exceptional strength and durability. However, protecting these metallic components from corrosion remains crucial. In this paper, we present a study aimed at evaluating the corrosion resistance of stainless steel, employed as an erosion shielding panel for a composite vehicle’s wing, layered with a superhydrophobic coating. The samples with and without coating have been characterized by contact angle measurements, microscopy (optical and electronic), and visual inspection after immersion in two solutions, NaCl and NaOH, respectively. The application of the superhydrophobic coating demonstrated a significant reduction in corrosion extent, especially in the demanding NaCl environment. This was evidenced by diminished formation of ripples and surface roughness, decreased iron oxide formation from oxidative processes, and a lower Surface Free Energy value in both liquid environments. Notably, the surface maintained its superhydrophobic properties even following an 8-day immersion in NaCl and NaOH solutions, demonstrating the reliability of the superhydrophobic coating offering as a potential solution to enhance the longevity and reliability of aircraft structures. Full article

Hidden corrosion remains a significant problem during aircraft service, primarily because of difficulties in its detection and assessment. The non-destructive D-Sight testing technique is characterized by high sensitivity to this type of damage and is an effective sensing tool for qualitative assessments of hidden corrosion in aircraft structures used by numerous ground service entities. In this paper, the authors demonstrated a new approach to the automatic quantification of hidden corrosion based on image processing D-Sight images during periodic inspections. The performance of the developed processing algorithm was demonstrated based on the results of the inspection of a Mi family military helicopter. The nondimensional quantitative measurement introduced in this study confirmed the effectiveness of this evaluation of corrosion progression, which was in agreement with the results of qualitative analysis of D-Sight images made by inspectors. This allows for the automation of the inspection process and supports inspectors in evaluating the extent and progression of hidden corrosion. Full article

Atmospheric corrosion of aluminum aircraft structures occurs due to a variety of reasons. A typical phenomenon leading to corrosion during aircraft operation is the deliquescence of salt contaminants due to changes in the ambient relative humidity (RH). Currently, the corrosion of aircraft is controlled through scheduled inspections. In contrast, the present contribution aims to continuously monitor atmospheric corrosion using the acoustic emission (AE) method, which could lead to a structural health monitoring application for aircraft. The AE method is frequently used for corrosion detection under immersion-like conditions or for corrosion where stress-induced cracking is involved. However, the applicability of the AE method to the detection of atmospheric corrosion in unloaded aluminum structures has not yet been demonstrated. To address this issue, the present investigation uses small droplets of a sodium chloride solution to induce atmospheric corrosion of uncladded aluminum alloy AA2024-T351. The operating conditions of an aircraft are simulated by controlled variations in the RH. The AE signals are measured while the corrosion site is visually observed through video recordings. A clear correlation between the formation and growth of pits, the AE and hydrogen bubble activity, and the RH is found. Thus, the findings demonstrate the applicability of the AE method to the monitoring of the atmospheric corrosion of aluminum aircraft structures using current measurement equipment. Numerous potential effects that can affect the measurable AE signals are discussed. Among these, bubble activity is considered to cause the most emissions. Full article

Hidden corrosion in aircraft structures, not detected on time, can have a significant influence on aircraft structural integrity and lead to catastrophic consequences. According to the widely accepted damage tolerance philosophy, non-destructive inspections are performed to assess structural safety and reliability. One of the inspection techniques used for such an inspection is the optical D-Sight technique. Since D-Sight is used primarily as a qualitative method, it is difficult to assess the evolution of a structural condition simply by comparing the inspection results. In the following study, the method to monitor hidden corrosion growth is proposed on the basis of historical data from D-Sight inspections. The method is based on geometric transforms and segmentation techniques to remove the influence of measurement conditions, such as the angle of observation or illumination, and to compare corroded regions for a sequence of D-Sight images acquired during historical inspections. The analysis of the proposed method was performed on the sequences of D-Sight images acquired from inspections of Polish military aircraft in the period from 2002 to 2017. The proposed method represents an effective tool for monitoring hidden corrosion growth in metallic aircraft structures based on a sequence of D-Sight images. Full article

Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols. Full article

Corrosion is an ever-present phenomena of material deterioration that affects all metal structures. Timely and accurate detection of corrosion is required for structural maintenance and effective management of structural components during their life cycle. The usage of aircraft materials has been primarily driven by the need for lighter, stronger, and more robust metal alloys, rather than mitigation of corrosion. As such, the overall cost of corrosion management and aircraft downtime remains high. To illustrate, $5.67 billion or 23.6% of total sustainment costs was spent on aircraft corrosion management, as well as 14.1% of total NAD for the US Air Force aviation and missiles in the fiscal year of 2018. The ability to detect and monitor corrosion will allow for a more efficient and cost-effective corrosion management strategy, and will therefore, minimize maintenance costs and downtime, and to avoid unexpected failure associated with corrosion. Conventional and commercial efforts in corrosion detection on aircrafts have focused on visual and other field detection approaches which are time- and usage-based rather than condition-based; they are also less effective in cases where the corroded area is inaccessible (e.g., fuel tank) or hidden (rivets). The ability to target and detect specific corrosion by-products associated with the metals/metal alloys (chloride ions, fluoride ions, iron oxides, aluminum chlorides etc.), corrosion environment (pH, wetness, temperature), along with conventional approaches for physical detection of corrosion can provide early corrosion detection as well as enhanced reliability of corrosion detection. The paper summarizes the state-of-art of corrosion sensing and measurement technologies for schedule-based inspection or continuous monitoring of physical, environmental and chemical presence associated with corrosion. The challenges are reviewed with regards to current gaps of corrosion detection and the complex task of corrosion management of an aircraft, with a focused overview of the corrosion factors and corrosion forms that are pertinent to the aviation industry. A comprehensive overview of thin film sensing techniques for corrosion detection and monitoring on aircrafts are being conducted. Particular attention is paid to innovative new materials, especially graphene-derived thin film sensors which rely on their ability to be configured as a conductor, semiconductor, or a functionally sensitive layer that responds to corrosion factors. Several thin film sensors have been detailed in this review as highly suited candidates for detecting corrosion through direct sensing of corrosion by-products in conjunction with the aforementioned physical and environmental corrosion parameters. The ability to print/pattern these thin film materials directly onto specific aircraft components, or deposit them onto rigid and flexible sensor surfaces and interfaces (fibre optics, microelectrode structures) makes them highly suited for corrosion monitoring applications. Full article

Composite materials have attracted significant attention with regard to the manufacturing of structures that require weight reduction, such as automobiles and aircraft, because they are more resistant to corrosion and fatigue than conventional metal materials. However, such materials exhibit a reliability degradation problem, i.e., their mechanical and physical properties deteriorate due to the occurrence of delamination and voids. Ultrasonic inspection methods have been widely applied for nondestructive detection of such defects in structures; however, the application of these approaches has been impeded due to high anisotropy and acoustic attenuation. In addition, the existing ultrasonic inspection methods require considerable time and cost for the inspection of large materials or structures. These problems were addressed in this study by developing an automatic ultrasonic inspection system; this was achieved by adopting a squirter-type water injection device, which uses a multi-joint robot and the through-transmission ultrasonic method. In addition, a software program to correct axis misalignment was developed and verified to solve the deterioration in defect detectability and accuracy that was caused by axis misalignment, which may occur during the use of the developed system. This development was accomplished after measuring the coordinates of the deformed mechanical part using a three-dimensional laser measuring instrument. Full article