Search Results (45)

Aircraft engines are regularly inspected with borescopes to detect faults at an early stage and maintain airworthiness. A critical part of this inspection process is accurately measuring any detected damage to determine whether it exceeds allowable limits. Current state-of-the-art borescope measurement techniques—primarily stereo camera systems and pattern projection—face significant challenges when engines lack sufficient surface features or when illumination is inadequate for reliable stereo matching. MEMS-based 3D scanners address these issues by focusing laser light onto a small spot, reducing dependency on surface texture and improving illumination. However, miniaturized MEMS-based scanner borescopes that can pass through standard engine inspection ports are not yet available. This work examines the essential steps to downsize MEMS 3D scanners for direct integration into borescope inspections, thereby enhancing the accuracy and reliability of aircraft engine fault detection. Full article

With the growing demand for diverse and high-volume manufacturing of composite material parts in aerospace applications, traditional machining methods have faced significant challenges due to their low efficiency and inconsistent quality. To address these challenges, digital twin (DT) technology offers a promising solution for developing automated production systems by enabling optimal configuration of manufacturing parameters. However, despite its potential, the widespread adoption of DT in complex manufacturing systems remains hindered by inherent limitations in adaptability and inter-system collaboration. This paper proposes an integrated framework that combines Model-Based Systems Engineering (MBSE) with deep learning (DL) to develop a digital twin system capable of adaptive machining. The proposed system employs three core components: machine vision-based process quality inspection, cognition-driven reasoning mechanisms, and adaptive optimization modules. By emulating human-like cognitive error correction and learning capabilities, this system enables real-time adaptive optimization of aerospace manufacturing processes. Experimental validation demonstrates that the cognition-driven DT framework achieves a defect recognition accuracy of 99.59% in aircraft cable fairing machining tasks. The system autonomously adapts to dynamic manufacturing conditions with minimal human intervention, significantly outperforming conventional processes in both efficiency and quality consistency. This work underscores the potential of integrating MBSE with DL to enhance the adaptability and robustness of digital twin systems in complex manufacturing environments. Full article

In this paper, we introduce an advanced AI-based solution for predicting structural damage in aircraft laminates. Our innovative approach focuses on detecting and locating fatigue damage within composite structures, thereby enhancing the assessment of aircraft health and usage. By leveraging state-of-the-art ultrasonic guided wave (UGW) inspection simulations of composite laminates integrated with piezoelectric transducers, comprehensive datasets are extracted efficiently. The signals captured by the piezoelectric sensors are utilized to engineer key features sensitive to composite structural damage, which are then used to train a deep neural network (DNN) for accurate structural damage prediction. Our findings demonstrate the significant potential of combining advanced simulation techniques with machine learning to improve the accuracy and reliability of structural health monitoring in aerospace applications. Full article

Fluorescent Penetrant Inspection (FPI) is a widely used inspection technique in the aerospace industry. Because of the aging aerospace sector, and because of the safety-criticality of the inspection, aerospace companies aim to automate (parts of this) inspection process to support inspectors. This paper focuses on a model that can assist inspectors by detecting (possible) defects. YOLOv8 is selected as the object detection model. For training such models, a dataset of sufficient size and variety is necessary to ensure good performance and to prevent overfitting. Because data acquisition is still in its beginning stages, an insufficient amount of data has been acquired. In this paper, we propose a data augmentation technique named Mosaic to artificially create more training data. This technique is tested by applying it to the acquired dataset numerous times and using the resulting dataset to train models with a static architecture (YOLOv8), after which the trained models are evaluated. The best trained model had a 0.834 mAP(50-95) performance, which is an increase of 0.666 mAP(50-95) over its baseline (the model trained on the dataset without data augmentation applied). The results show that, by using this Mosaic technique, promising object detection performance via Deep Convolutional Neural Networks (DCNNs) can be achieved even when the data are limited. Full article

Regular aircraft engine inspections play a crucial role in aviation safety. However, traditional inspections are often performed manually, relying heavily on the judgment and experience of operators. This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. Specifically, this study develops Deep Neural Network models to detect defects in borescope images using various datasets, based on Computer Vision and YOLOv8n object detection techniques. Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. YOLOv8n-based deep learning models are subsequently fine-tuned and trained on these dataset groups. The results indicate that the model trained on wide-shot blade images performs better overall at detecting defects on blades compared to the model trained on zoomed-in images. The comparison of multiple models’ results reveals inherent uncertainties in model performance that while some models trained on data enhanced by Computer Vision techniques may appear more reliable in some types of defect detection, the relationship between these techniques and subsequent results cannot be generalized. The impact of epochs and optimizers on the model’s performance indicates that incorporating rotated images and selecting an appropriate optimizer are key factors for effective model training. Furthermore, models trained solely on artificially generated images from collages perform poorly at detecting defects in real images. A potential solution is to train the model on both synthetic and real images. Future work will focus on improving the framework’s performance and conducting a more comprehensive uncertainty analysis by utilizing larger and more diverse datasets, supported by enhanced computational power. Full article

Aircraft safety is the aviation industry’s primary concern. Inspections must be conducted before each flight to ensure the integrity of the aircraft. To meet the increasing demand for engineers, a system capable of detecting surface defects on aircraft was designed to reduce the workload of the inspection process. The system utilizes the real-time object detection capabilities of the you only look once-version 9 (YOLO v9) algorithm, combined with imagery captured from an unmanned aerial vehicle (UAV)-based aerial platform. This results in a system capable of detecting defects such as cracks and dents on the aircraft’s surface, even in areas that are difficult to reach, such as the upper surfaces of the wings or the higher parts of the fuselage. With the introduction of a Real-Time Messaging Protocol (RTMP) server, the results can be monitored via artificial intelligence (AI) and Internet of Things (IoT) devices in real time for further evaluation. The experimental results confirmed an effective recognition of defects, with a mean average precision (mAP@0.5) of 0.842 for all classes, the highest score being 0.938 for dents and the lowest value 0.733 for the paint-off class. This study demonstrates the potential for developing image detection technology with AI for the aviation industry. Full article

This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. By proposing a proper modeling of the problem and preparing data for the constraint programming algorithm, we demonstrate superior performance in scheduling efficiency and resource utilization. The experimental results show an average utilization of 99.35%, and the method can even achieve 100% utilization in some cases. Full article

Surface angled cracks on critical components in high-speed machinery can lead to fractures under stress and pressure, posing a significant threat to the operational safety of equipment. To detect surface angled cracks on critical components, this paper proposes a “Quantitative Detection Method for Surface Angled Cracks Based on Full-field Scanning Data”. By analyzing different ultrasonic signals in the full-field scanning data from laser ultrasonics, the width, angle, and length of surface angled cracks can be determined. This study investigates the propagation behavior of ultrasonic waves and their interaction with surface angled cracks through theoretical calculations. The crack width is solved by analyzing the distribution of Rayleigh waves in the full-field scanning data. This paper also discusses the differences in ultrasonic wave propagation between near-field and far-field detection and identifies the critical point between these regions. Different computational methods are employed to calculate the inclination angle and the crack endpoint at various scan positions. Four sets of experiments were conducted to validate the proposed method, with results showing that the errors in determining the width, angle, and length of the surface angled cracks were all within 5%. This confirms the feasibility of the method for detecting surface angled cracks. The quantitative detection of surface angled cracks on critical components using this method allows for a comprehensive assessment of the component’s condition, aiding in the prediction of service life and the mitigation of operational risks. This method shows promising application potential in areas such as aircraft engine blade inspection and gear inspection. 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

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine’s piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data. Full article

Pre-flight inspection and maintenance are essential prerequisites for aviation safety. This study focused on developing a real-time monitoring system designed to assess the condition of composite material structures on the exterior of aircraft. Implementing such a system can reduce operational costs, enhance flight safety, and increase aircraft availability. This study aims to detect defects in aircraft fuselages manufactured from composite materials by applying image visual recognition technology. This study integrated a drone and an infrared camera for real-time image transmission to ground stations. MATLAB image analysis software (MATLAB 2020b) was used to analyze infrared (IR) images and detect structural defects in the aircraft’s appearance. This methodology was based on the inspection of damaged engine cowlings. The developed approach compares composite material conditions with known defects before and after repair, considering mechanical performance, defect size, and strength. Simultaneously, tests were conducted on various composite material panels with unknown defects, yielding favorable results. This study underscores an integrated system offering rapid detection, real-time feedback, and analysis, effectively reducing time, and potential hazards associated with high-altitude operations. Furthermore, it addresses blind spots in aircraft inspections, contributing to effective flight safety maintenance. 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

Aircraft reciprocating engines have been in operation over the past 100 years, which is a testament to their high levels of reliability and stability. Compared to turbine engines, reciprocating engines are at a disadvantage when it comes to high-speed flight. Nevertheless, they are widely used mainly for small aircraft thanks to their high specific power or power-to-weight ratio. Considering that propulsion systems account for approximately 40% of the aircraft price, lightness and high performance are key attributes of aircraft to achieve longer endurance. With the advantages offered by diesel engines, such as fuel economy, less maintenance, and a long lifespan, many attempts have been made to mount automotive diesel engines on urban air mobility and light aircraft. Recognizing advanced automotive diesel technology, where the power-to-weight ratio of the diesel engine is approximately 1 PS/kg, we analyzed a case where an automobile engine was converted for use in an aircraft. We focused on the Mercedes-Benz OM640 and the Austro AE300 and disassembled the two engines for comparative analysis. We then classified the engine components modified for aircraft use by (1) defining the major engine parts as fixed and alteration ones; (2) identifying the airworthiness-related alteration parts; and (3) categorizing the conversion purposes into classes A, B, and C. Components under class A were further categorized into subgroups in accordance with the airworthiness certification specifications outlined by the European Union Aviation Safety Agency. This helped determine the corresponding airworthiness standards for each subgroup. An inspection of the oil supply system revealed the need to apply safety wiring for some components to prevent possible oil leakages, which can be caused by the pressure difference with increasing altitude. Moreover, given that sensor manufacturers are required to present guidelines for sensor redundancy through numerous designs and tests and secure single-fault tolerance, we established criteria for selecting and applying sensors and separating sensors that must be made redundant from ones that are not subject to sensor redundancy. Full article

Unmanned aerial system/unmanned aircraft system (UAS) operations have increased exponentially in recent years. With the creation of new air mobility concepts, industries use cutting-edge technology to create unmanned aerial vehicles (UAVs) for various applications. Due to the popularity and use of advanced technology in this relatively new and rapidly evolving context, a regulatory framework to ensure safe operations is essential. To reflect the several ongoing initiatives and new developments in the domain of European Union (EU) regulatory frameworks at various levels, the increasing needs, developments in, and potential uses of UAVs, particularly in the context of research and innovation, a systematic overview is carried out in this paper. We review the development of UAV regulation in the European Union. The issue of how to implement this new and evolving regulation in UAS operations is also tackled. The digital twin (DT)’s ability to design, build, and analyze procedures makes it one potential way to assist the certification process. DTs are time- and cost-efficient tools to assist the certification process, since they enable engineers to inspect, analyze, and integrate designs as well as express concerns immediately; however, it is fair to state that DT implementation in UASs for certification and regulation is not discussed in-depth in the literature. This paper underlines the significance of UAS DTs in the certification process to provide a solid foundation for future studies. Full article

Surface crack detection and sizing is essential for the manufacturing and maintenance of engines, run parts, and other metal elements of aircrafts. Among various non-destructive detection methods, the fully non-contact and non-intrusive technique based on laser-stimulated lock-in thermography (LLT) has recently attracted a lot of attention from the aerospace industry. We propose and demonstrate a system of reconfigurable LLT for three-dimensional surface crack detection in metal alloys. For large area inspection, the multi-spot LLT can speed up the inspection time by a factor of the number of spots. The minimum resolved size of micro-holes is ~50 µm in diameter limited by the magnification of the camera lens. We also study the crack length ranging from 0.8 to 3.4 mm by varying the modulation frequency of LLT. An empirical parameter related to the thermal diffusion length is found to show the linear dependence with the crack length. With the proper calibration, this parameter can be used to predict the sizing of the surface fatigue cracks. Reconfigurable LLT allows us to quickly locate the crack position and accurately measure its dimensions. This method is also applicable to the non-destructive detection of surface or sub-surface defect in other materials used in various industries. Full article