Peridynamics study on crack propagation and failure behavior in Ni/Ni3Al bi-material structure

Wu, Wen-Ping; Li, Ze-Zheng; Chu, Xihua

doi:10.1016/j.compstruct.2023.117453

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…Critical components often operate in harsh environments and are subjected to cyclic stress and strain in specialized applications, such as aerospace engines, offshore platforms, and other similar scenarios, making them susceptible to the formation of micro-cracks and other defects on or near the surface [1][2][3]. If these defects are not detected in a timely manner, they can rapidly propagate and lead to the sudden catastrophic failure of the entire structure [4,5]. Therefore, the detection of surface microdefects, such as surface or near-surface cracks or incomplete fusion, especially through non-destructive evaluation methods, plays a crucial role in the industrial domain [6,7].…”

Section: Introductionmentioning

confidence: 99%

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Fei,

Cao,

et al. 2023

Applied Sciences

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This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.

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Section: Introductionmentioning

confidence: 99%

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Fei,

Cao,

et al. 2023

Applied Sciences

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Influence of substrate properties on fracture behavior of rock-concrete bi-material central interface cracked Brazilian disks at different loading angles

Zhou,

Zhang,

et al. 2024

Theoretical and Applied Fracture Mechanics

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Recent advances in peridynamic theory: A review

Oterkus,

Oterkus

2024

AIMSMATES

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<abstract> <p>Peridynamics is a new approach to continuum mechanics. There has been rapid progress in peridynamics research, especially in recent years. In this review study, recent advances in peridynamics research were summarised. A large number of studies were considered and classified into different categories ranging from additive manufacturing, artificial intelligence and machine learning, composite materials, fatigue, functionally graded materials, impact, reduced order modelling, structural health monitoring, topology optimisation, and many more. Finally, some future directions were highlighted.</p> </abstract>

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Peridynamics study on crack propagation and failure behavior in Ni/Ni3Al bi-material structure

Cited by 3 publications

References 58 publications

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Influence of substrate properties on fracture behavior of rock-concrete bi-material central interface cracked Brazilian disks at different loading angles

Recent advances in peridynamic theory: A review

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