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Real-time fatigue crack prediction using self-sensing buckypaper and gated recurrent unit

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Abstract

Aircraft is regarded as a collection of modern technologies from throughout all industries. However, it is inevitable to develop defects during its service life. In general, the aircraft has a periodic maintenance period, and is inspected according to a well-established process, for example non-destructive testing. However, the maintenance requires massive time and cost. If an unexpected defect occurs due to external environments before the maintenance cycle returns, it is impossible to prevent subsequent damage. This study proposes a novel real-time fatigue crack prediction method using self-sensing carbon nanotube buckypaper and deep learning algorithm. Carbon nanotube buckypaper was fabricated by the wet method. The physics-informed gated recurrent unit was used to predict real time crack growth. The physics-informed deep learning model accurately predicted the fatigue crack length. The results showed that the proposed method is promising in detecting the real-time fatigue crack growth of aircraft structure.

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Abbreviations

GF :

Gauge factor

R 0 :

Initial resistance of BP

R :

Electrical measured resistance of BP

ε :

Strain of specimen

ρ :

Resistivity of specimen

w :

Width of BP

a :

Crack length

x t :

Input vector

Δa t :

Output vector

ã t :

Candidate activation vector

z t :

Update gate vector

r t :

Reset gate vector

W :

Parameter matrices and vector

σ :

Sigmoid function

tanh :

Syperbolic tangent

ω :

Sength of the sequence

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Acknowledgments

This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0017307) supervised by the Korea Institute for Advancement of Technology (KIAT).

Author information

Authors and Affiliations

  1. Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul, 04620, Korea

    Hyeonho Hwang, Jinwoo Song & Heung Soo Kim

  2. School of Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, 85281, USA

    Aditi Chattopadhyay

Authors
  1. Hyeonho Hwang
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  2. Jinwoo Song
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Corresponding author

Correspondence to Heung Soo Kim.

Additional information

Hyeonho Hwang received the B.S. degree in Department of Mechanical, Robotics and Energy Engineering in 2021 from Dongguk University-Seoul, Republic of Korea. His main research interest includes topics related to Prognostics and Health Management (PHM), Structural Health Monitoring (SHM), artificial intelligence, adaptive structures, structural analysis, and composite structures.

Jinwoo Song received an M.S. degree in Engineering Management from Syracuse University, Newyork, USA, in 2018, where he received a Ph.D. degree in Mechanical & Aerospace Engineering Department in 2021. His current research interests include interdisciplinary approaches to cyber-manufacturing system’s security against particular attackers from outside or inside of the system on the following toolboxes: Blockchain technology, forecasting methodology, manufacturing planning and control, inventory management, supply chain management, machine learning, big data analysis, digital twin, physical data collection/analysis, attack tree, virtual environment, and simulation.

Heung Soo Kim received the B.S. degree in Aerospace Engineering in 1997, M.S. degree in Aerospace Engineering from Inha University, Incheon, Korea, in 1999, and the Ph.D. degree from Arizona State University, Tempe, AZ in 2003. He is currently Professor with the Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Republic of Korea. His main research interest includes topics related to Prognostics and Health Management (PHM), artificial intelligence, biomimetic actuator, adaptive structures, structural analysis, numerical analysis, and composite structures. Prof. Kim is the Director of BK21 AIMS Center and DGU Global Smart Factory Center. He has published more than 300 research papers in reputed international journals and conferences. He was awarded the Academic Award at the Korean PHM Society in recognition of his contribution to academic development through outstanding research and presentation on the PHM field.

Aditi Chattopadhyay is a Regents Professor at Arizona State University and Ira A. Fulton Chair Professor of Mechanical and Aerospace Engineering. She is also the Director of Adaptive Intelligent Materials & Systems (AIMS) Center. She received her B. Tech (Hons) in Aeronautical Engineering from IIT Kharagpur, followed by M.S. and Ph.D. degrees in Aerospace Engineering from Georgia Institute of Technology, USA. Her current research areas include multifunctional materials, multiscale modeling, structural health monitoring and damage prognosis, and multiaxial fatigue. She has been the Principal Investigator on numerous grants and collaborated with defense and government laboratories on significant technical transitions.

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Hwang, H., Song, J., Kim, H.S. et al. Real-time fatigue crack prediction using self-sensing buckypaper and gated recurrent unit. J Mech Sci Technol 37, 1401–1409 (2023). https://doi.org/10.1007/s12206-023-0226-y

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