Simulation and experimental study on planar remote field eddy current detection of hidden defects in aircraft multilayer structure

Song, Kai; Fu, Shaodong; Ning, Ning; Fan, Junling; Cui, Ximing

doi:10.1080/10589759.2023.2183953

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NDT techniques can detect internal damage without destroying the performance and internal structure of materials. Common NDT techniques include eddy current [8,9], ultrasonic testing [10,11], microwave imaging [12,13], and so on. Due to the advantages of noncontact, real-time, efficient, and high contrast, microwave imaging is a promising method for NDT.…”

Section: Introductionmentioning

confidence: 99%

Microwave time reversal for nondestructive testing of buried small damage in composite materials

An,

Li,

Long

et al. 2024

Inverse Problems

View full text Add to dashboard Cite

Composite materials are widely applied in aerospace, civil engineering, and sports equipment. Various damages produced during fabrication and long-term use can destroy its original mechanical properties, which brings safety and structural healthy concerns. Microwave imaging based on time reversal is one of the most promising nondestructive testing (NDT) methods for portable, low-cost, and accurate testing with the advantages of auto-focus and super-resolution. This paper applied microwave time reversal for the detection of buried small damage in composites backed by metal plates. Strong reflection from composite-metal interfaces brings challenges in successfully achieving time-reversal auto-focusing on small and weak-scattering damages in composites. Traditional target localization methods, including the entropy regularization method (ERM) and time-integrated energy method (TIEM), may result in the wrong localization of small damages. The main contribution of this paper is that the localization problem caused by the strong reflection from metal plates is revealed first, and the target initial reflection method (TIRM) from through-wall-radar imaging is introduced to solve it. The performance of three target localization methods is investigated, and the physical reasons for failure or successful localization are discussed in detail. Some performance influence factors, such as the arrangement of receivers or the total time step of received signals, are also discussed. Good performance for the detection of a single small damage with a weak scattered signal is achieved, and the performance for detecting multiple damages is studied. All time-reversal simulations are carried out based on the finite-difference time-domain (FDTD) method.

show abstract

Section: Introductionmentioning

confidence: 99%

Microwave time reversal for nondestructive testing of buried small damage in composite materials

An,

Li,

Long

et al. 2024

Inverse Problems

View full text Add to dashboard Cite

show abstract

“…These ECT systems provide a high sensitivity to surface and subsurface damage and can detect hidden corrosion in the layers of the multilayer structure. [6][7][8] The exciting and sensing coils could be arranged in an eddy current array probe to detect a large area of the aircraft and be commercialized using in the practical (e.g. Ommiscan system Olympus, ECA system Eddyfi).…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic testing of rivet in multilayer structure using a convolutional neural network on edge device

et al. 2023

Science Progress

View full text Add to dashboard Cite

Rivets are used to assemble layers in the air intakes, fuselages, and wings of an aircraft. After a long time of working under extreme conditions, pitting corrosion could appear in the rivets of the aircraft. The rivets could be broken down and thread the safety of the aircraft. In this paper, we proposed an ultrasonic testing method integrated with convolutional neural network (CNN) for the detection of corrosion in the rivets. The CNN model was designed to be lightweight enough to be able to run on edge devices. The CNN model was trained with a very limited sample of rivets, from 3 to 9 artificial pitting corrosive rivets. The results show that the proposed approach could detect up to 95.2% of pitting corrosion using experimental data with three training rivets. The detection accuracy could be improved to 99% by nine training rivets. The CNN model was implemented and ran on an edge device (Jetson Nano) in real-time with a small latency of 1.65 ms.

show abstract

Hidden corrosion detection in aircraft structures with a lightweight magnetic convolutional neural network

Vu,

Luong,

2024

Nondestructive Testing and Evaluation

View full text Add to dashboard Cite

Simulation and experimental study on planar remote field eddy current detection of hidden defects in aircraft multilayer structure

Cited by 6 publications

References 11 publications

Microwave time reversal for nondestructive testing of buried small damage in composite materials

Microwave time reversal for nondestructive testing of buried small damage in composite materials

Ultrasonic testing of rivet in multilayer structure using a convolutional neural network on edge device

Hidden corrosion detection in aircraft structures with a lightweight magnetic convolutional neural network

Contact Info

Product

Resources

About