Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting

Xie, Qian; Lu, Dening; Du, Kunpeng; Xu, Jinxuan; Dai, Jiajia; Chen, HongHua; Wang, Jun

doi:10.1016/j.cad.2019.102805

Cited by 23 publications

(12 citation statements)

References 25 publications

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“…First of all, artificially erasing the data of the defective part, then the section was measured using the measuring system, the laser centerline was extracted and the intensity of light per unit length was counted, as shown in Fig.7. The special points A, B, and C on the cross-section data were located, and the relative parameter k was calculated by combining (6). Finally, for missing part, the value of illumination intensity and relative parameter k were used to repair the missing data, as shown in Fig.7.…”

Section: Methodsmentioning

confidence: 99%

“…Constants in (5) are converted, so that (5) can be expressed as (6). From the (6), it can see that the light intensity only depends on the Angle and z, which are the key parameters of point position coordinates.…”

Section: Principlementioning

confidence: 99%

“…Defect classification is simultaneously implemented with Hu's moment invariants and back propagation neural (BPN) approach, which can complete the accurate classification of surface defects for an image can be completed in less than 0.1ms. In the application of AOI based on point cloud, an extraction algorithm was proposed in [6] to efficiently and effectively detect rivets from the raw scanned point clouds which can assist in the rapid measurement of riveting quality. The manufacture process of the 3D scanning system with a high precision, miniaturization and lightweight was proposed in [7] to capture the information of microdamage and depth of the modern civil architectural structures.…”

Section: Introductionmentioning

confidence: 99%

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Repair Method of Data Loss in Weld Surface Defect Detection Based on Light Intensity and 3D Geometry

Shiyi

Xing

2020

IEEE Access

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Weld surface quality inspection is an indispensable part in the welding automation engineering. When Laser Triangulation Method (LTM) is used to scan the weld surface, external factors will interfere with the laser center line, resulting in data loss and quality detection failure, and massive data loss is difficult to be repaired in the traditional way. In order to improve the quality of weld surface defect detection, it is necessary to find an effective and strong anti-interference method to fix the lost data. This paper presents a method of fixing the data loss caused by external environmental factors in the process of detecting flatbed lap weld surface defects by LTM. The paper includes the analysis of the causes of data loss, its impact for practical applications and a repair method. The repair method derives a formula which is suitable for expressing three-dimensional points on weld surface by combining Bidirectional Reflectance Distribution Function (BRDF) and weld surface section model. The accuracy of the algorithm is verified by experiments on standard block and single weld cross-section. The accuracy of the method up to 92% and the repair rate improved by 30%. The method was tested on flatbed lap weld to confirm its effectiveness in the field of catching surface defects hidden in the lost data.

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Section: Methodsmentioning

confidence: 99%

Section: Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Repair Method of Data Loss in Weld Surface Defect Detection Based on Light Intensity and 3D Geometry

Shiyi

Xing

2020

IEEE Access

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“…In the particular field of steel structures, we might cite various studies such as the ones by Yang et al [18], Basta et al [19], Laefer and Truong-Hong [20], Donato et al [21], or Morganti et al [22]. There also exist works focused on looking for skin rivets such as the work of Xie et al [23], this paper presents an automated multiple structure adjustment algorithm with density recognition to perform the detection of an aircraft skin rivet based on a 3D point cloud and the adjustment of a circular pattern. However, this method does not seem to be adequate for the case of searching for rivets in historical steel structures.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification and Geometrical Modeling of Steel Rivets of Historical Structures from Lidar Data

et al. 2021

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Riveting is a joining technique that was widely used in iron and steel structures from the mid-nineteenth to the early twentieth century. Nowadays, many of these riveted structures are still in service around the world, and in many instances, also constitute an important part of the built cultural heritage. The maintenance and conservation of this type of construction is a crucial task for which the HBIM (Historic Building Information Modeling) methodology has recently gained increased attention, postulating itself as the ideal tool for tracking control and conservation-related actions. In the process of data collection and 3D modeling of the structure, the rivets are an important part to be taken into account in the structural safety assessment and health monitoring over time. Any structure of this typology typically presents thousands of rivets, so its measurement and subsequent 3D geometrical modeling is a laborious task and a source of possible errors. Accordingly, this work presents a novel methodology that allows the automatic identification and 3D modeling of rivets in iron and steel structures from LIDAR data. The proposed methodology has been tested with both laboratory specimens and a full-scale real bridge.

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“…Xie et al used a commercial 3-D laser scanner to obtain the 3-D data of riveted aircraft skin and then performed multiple structure fitting to detect the rivets. Nevertheless, the quality of the measured point cloud is poor, which suffers from severe noises, missing contours, and density anisotropy [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy 3-D Sensor for Rivet Inspection Using Fringe Projection Profilometry with Texture Constraint

Wang

Zhao

et al. 2020

Sensors

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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%.

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Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting

Cited by 23 publications

References 25 publications

Repair Method of Data Loss in Weld Surface Defect Detection Based on Light Intensity and 3D Geometry

Repair Method of Data Loss in Weld Surface Defect Detection Based on Light Intensity and 3D Geometry

Automatic Identification and Geometrical Modeling of Steel Rivets of Historical Structures from Lidar Data

High-Accuracy 3-D Sensor for Rivet Inspection Using Fringe Projection Profilometry with Texture Constraint

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