Automatic inspection of aeronautic components

Biagio, Marco San; Beltrán-González, Carlos; Giunta, Salvatore; Bue, Alessio Del; Murino, Vittorio

doi:10.1007/s00138-017-0839-1

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…Global economic pressures have gradually led companies to improve these processes to be more competitive. As a result, the demand for intelligent visual inspection systems aimed at guaranteeing high quality in production lines is increasing [20].…”

Section: Related Workmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

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Section: Related Workmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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“…For example, use of photometric stereo to identify defects in casted steel [1], convolutional neural networks for inspection of metallic components in nuclear power plants [2], examining flaws in concrete structures [3], inspection with multiple lights [4,5], have been used to automate visual inspection of cracks from the digital images and videos of the areas to be inspected. Similarly, defect detection in aeronautic components is discussed in [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Illumination Fusion With Crack Enhancement Using Cycle-Consistent Losses

Padalkar

Beltrán-González

Bue

2021

2021 IEEE International Conference on Image Processing (ICIP)

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This paper addresses for the first time, the problem of multiillumination fusion with crack enhancement. Our models are trained using cycle-consistent losses to combine crack details from several mutually registered multi-illumination images of ceramic tiles, into a single representative image. Using realworld industrial data, we show that the crack locations are enhanced in the fused images, making them easily noticeable for remote inspection, and demonstrate the effectiveness of our method compared to a multi-exposure fusion technique.

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“…These robots provide the physical link between intelligence and action. Usually, a robot inspection system has three modules: (1) mechanical: links, joints, end-effector, base, and mobile platform; (2) vision-based sensor system mounted on its end-effector; and (3) the perception, planning, and control unit, which interprets the sensory input and plans and controls the robot's actions [3].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method has the following advantages; it (1) combines the precise information of CAD model with fast and inexpensive 2D sensors, (2) inspects parts with arbitrary geometrical shapes, (3) processes in real-time, and (4) fully automates the inspection process.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Inspection of Aeronautical Mechanical Assemblies by Matching the 3D CAD Model and Real 2D Images

Abdallah

Jovančević²,

Orteu

et al. 2019

J. Imaging

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In the aviation industry, automated inspection is essential for ensuring quality of production. It allows acceleration of procedures for quality control of parts or mechanical assemblies. As a result, the demand of intelligent visual inspection systems aimed at ensuring high quality in production lines is increasing. In this work, we address a very common problem in quality control. The problem is verification of presence of the correct part and verification of its position. We address the problem in two parts: first, automatic selection of informative viewpoints before the inspection process is started (offline preparation of the inspection) and, second, automatic treatment of the acquired images from said viewpoints by matching them with information in 3D CAD models is launched. We apply this inspection system for detecting defects on aeronautical mechanical assemblies with the aim of checking whether all the subparts are present and correctly mounted. The system can be used during manufacturing or maintenance operations. The accuracy of the system is evaluated on two kinds of platform. One is an autonomous navigation robot, and the other one is a handheld tablet. The experimental results show that our proposed approach is accurate and promising for industrial applications with possibility for real-time inspection.

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Automatic inspection of aeronautic components

Cited by 10 publications

References 29 publications

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Multi-Illumination Fusion With Crack Enhancement Using Cycle-Consistent Losses

Automatic Inspection of Aeronautical Mechanical Assemblies by Matching the 3D CAD Model and Real 2D Images

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