Search Results (8)

Riveting quality inspection is critical for ensuring structural integrity and safety in aerospace, automotive, and civil engineering, as rivet defects during the riveting process may cause catastrophic failures in structural connections. This study focuses on the detection method for multi-type rivet head defects and aims to improve the performance of feature extraction and classification for various head defects. The research is carried out to develop a lightweight classification network with a Dynamic Screening Self-Attention (DSSA) mechanism for 3D point clouds. To achieve the rivet head dataset, we employ Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering to extract each target head data from the dataset of riveted plates. The head dataset can be further simplified using the Non-Maximum Eigenvalue Curvature Method (NMECM). In this way, redundant information can be reduced. The PointGhost network is then designed for the classification of head defects. It contains a sampling module with a Virtual Block Sampling (VBS) mechanism that reduces the computational complexity. In addition, there exists a feature extraction module with a Grouped Pointwise Convolution Ghost (GPC-Ghost) lightweight model that performs local and global feature learning, together with the DSSA mechanism to enhance the riveted head defects. Lastly, the severity levels of rivet protrusion and indentation are quantified using Principal Component Analysis (PCA) and the Total Least Squares (TLS) fitting algorithm. In terms of the experiment, six popular lightweight models are compared, wherein GPC-Ghost shows more significant performance, achieving a 4.31% higher mean accuracy than PointNet++, with less computational cost of 0.66 GFLOPs. Based on the comparative analysis of six attention mechanisms and seven classification networks, the PointGhost model possesses the highest mean accuracy of 99.49%, with an average misclassification rate of 1.19%. The method can balance the accuracy and efficiency effectively, demonstrating its potential for engineering inspection. Full article

Foreign object debris (FOd) in rivet holes, machined holes, and fastener sites poses a critical risk to aerospace manufacturing, where current inspections rely on manual visual checks with flashlights and mirrors. These methods are slow, fatiguing, and prone to error. This work introduces HANNDI, a compact handheld inspection device that integrates controlled optics, illumination, and onboard deep learning for rapid and reliable inspection directly on the factory floor. The system performs focal sweeps, aligns and fuses the images into an all-in-focus representation, and applies a dual CNN pipeline based on the YOLO architecture: one network detects and localizes holes, while the other classifies debris. All training images were collected with the prototype, ensuring consistent geometry and lighting. On a withheld test set from a proprietary ≈3700 image dataset of aerospace assets, HANNDI achieved per-class precision and recall near 95%. An end-to-end demonstration on representative aircraft parts yielded an effective task time of 13.6 s per hole. To our knowledge, this is the first handheld automated optical inspection system that combines mechanical enforcement of imaging geometry, controlled illumination, and embedded CNN inference, providing a practical path toward robust factory floor deployment. Full article

This study investigates the degradation of aircraft paint and its impact on aerodynamic performance, using the PZL M-20 “Mewa” aircraft as a case study. Paint samples were collected from both damaged and intact areas of the airframe and analyzed using electron paramagnetic resonance (EPR) spectroscopy, scanning electron microscopy (SEM), and aerodynamic testing. One of the major challenges addressed in this work was the non-destructive identification of chemical aging effects in operational paint coatings and their correlation with aerodynamic behavior. The application of EPR spectroscopy in conjunction with real-world aerodynamic testing on naturally degraded surfaces represents an innovative approach that offers both scientific insight and practical guidance for maintenance practices. The results indicate significant deterioration in aerodynamic characteristics—such as increased drag and reduced lift—due to coating damage, particularly around riveted and bolted joints. EPR spectra revealed a notable increase in the density of unpaired electron spins in aged coatings, confirming ongoing oxidative degradation processes. While this study was limited to a single aircraft, the findings highlight the critical importance of regular inspection and maintenance of paint coatings to ensure flight safety and operational efficiency. 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

The inspection of fasteners in aluminium joints in the aviation industry is a time consuming and costly but mandatory task. Until today, the manual procedure with the bare eye does not allow the temporal tracking of a damaging behavior or the objective comparison between different inspections. A digital inspection method addresses both aspects while resulting in a significant inspection time reduction. The purpose of this work is to develop a digital and automated inspection method based on In-plane Heatwave Thermography and the analysis of the disturbances due to thermal irregularities in the plate-like structure. For this, a comparison study with Ultrasound Lock-in Thermography and Scanning Laser Doppler Vibrometry as well as a benchmarking of all three methods on one serviceable aircraft fuselage panel is performed. The presented data confirm the feasibility to detect and to qualify countersunk rivets and screws in aluminium aircraft fuselage panels with the discussed methods. The results suggest a fully automated inspection procedure which combines the different approaches and a study with more samples to establish thresholds indicating intact and damaged fasteners. Full article

Riveted workpieces are widely used in manufacturing; however, current inspection sensors are mainly limited in nondestructive testing and obtaining the high-accuracy dimension automatically is difficult. We developed a 3-D sensor for rivet inspection using fringe projection profilometry (FPP) with texture constraint. We used multi-intensity high dynamic range (HDR) FPP method to address the varying reflectance of the metal surface then utilized an additional constraint calculated from the fused HDR texture to compensate for the artifacts caused by phase mixture around the stepwise edge. By combining the 2-D contours and 3-D FPP data, rivets can be easily segmented, and the edge points can be further refined for diameter measurement. We tested the performance on a sample of riveted aluminum frame and evaluated the accuracy using standard objects. Experiments show that denser 3-D data of a riveted metal workpiece can be acquired with high accuracy. Compared with the traditional FPP method, the diameter measurement accuracy can be improved by 50%. Full article

Steel corrosion is one of the most dominant factors in the degradation of transport infrastructure. This article deals with the impact of the atmospheric corrosion of structural steel on the load-carrying capacity of old riveted bridge structures. A study on the impact of corrosion losses on the resistance and, thus, the load-carrying capacity of eight chosen bridge members with riveted I-sections from three different bridge substructures is presented. The load-carrying capacity calculation is carried out using modern procedures and on the basis of the diagnosed state of the structural elements. Within the analysis of the results, the need for long-term in situ corrosion measurements, as well as the need for regular inspections on the existing bridges are also discussed. Full article

In eddy current non-destructive testing of a multi-layered riveted structure, rotating current excitation, generated by orthogonal coils, is advantageous in providing sensitivity to defects of all orientations. However, when used with linear array sensors, the exciting magnetic flux density ( $B_x$ ) of the orthogonal coils is not uniform over the sensor region, resulting in an output signal magnitude that depends on the relative location of the defect to the sensor array. In this paper, the rotating excitation coil is optimized to achieve a uniform $B_x$ field in the sensor array area and minimize the probe size. The current density distribution of the coil is optimized using the polynomial approximation method. A non-uniform coil design is derived from the optimized current density distribution. Simulation results, using both an optimized coil and a conventional coil, are generated using the finite element method (FEM) model. The signal magnitude for an optimized coil is seen to be more robust with respect to offset of defects from the coil center. A novel multilayer coil structure, fabricated on a multi-layer printed circuit board, is used to build the optimized coil. A prototype probe with the optimized coil and 32 giant magnetoresistive (GMR) sensors is built and tested on a two-layer riveted aluminum sample. Experimental results show that the optimized probe has better defect detection capability compared with a conventional non-optimized coil. Full article