Automated Identification of Hidden Corrosion Based on the D-Sight Technique: A Case Study on a Military Helicopter

Article Engineering, Multidisciplinary

Quantification of hidden corrosion in aircraft structures using enhanced D-Sight NDT technique

Andrzej Katunin et al.

Summary: This paper proposes an enhancement to the D-Sight non-destructive testing technique by allowing quantification of hidden corrosion in metallic aircraft fuselages. The approach involves image processing to quantify the spatial dimensions of corroded areas from D-Sight images, and finite element modeling to determine corrosion depth and degree. Validations were conducted using metrological measurements and digital image correlation techniques, correcting for overestimation typical of the D-Sight technique. The results provide a lower bound for the detectability of hidden corrosion using D-Sight, improving inspections of aircraft structures where this remains a serious and unresolved issue.

MEASUREMENT (2023)

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Article Chemistry, Analytical

Monitoring of Hidden Corrosion Growth in Aircraft Structures Based on D-Sight Inspections and Image Processing

Andrzej Katunin et al.

Summary: This study proposed a method to monitor hidden corrosion growth in metallic aircraft structures using a sequence of D-Sight images, by analyzing the images through geometric transforms and segmentation techniques to eliminate the influence of measurement conditions.

SENSORS (2022)

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Review Chemistry, Analytical

A Review of Corrosion in Aircraft Structures and Graphene-Based Sensors for Advanced Corrosion Monitoring

Lucy Li et al.

Summary: Corrosion poses a significant threat to the safety and performance of aircraft structures, highlighting the importance of timely and accurate detection and management. Current corrosion detection methods rely on time and visual inspection, which may not be effective for inaccessible or hidden corrosion areas. Improved approaches targeting specific corrosion by-products and environmental factors offer more effective support for corrosion management.

SENSORS (2021)

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Article Chemistry, Analytical

Aircraft Fuselage Corrosion Detection Using Artificial Intelligence

Bruno Brandoli et al.

Summary: This paper introduces a method for automatic image-based corrosion detection of aircraft structures using deep neural networks, with a precision of over 93% comparable to trained operators. The approach supports specialists and engineers in the aerospace industry, potentially contributing to the automation of maintenance protocols.

SENSORS (2021)

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Article Engineering, Electrical & Electronic