Damage assessment in Q690 high strength structural steel using nonlinear Lamb waves

Wang, Xiao; Xiang, Yanxun; Zhu, Wujun; Ding, Taotao; Li, Huaying

doi:10.1016/j.conbuildmat.2019.117384

Cited by 15 publications

(3 citation statements)

References 24 publications

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“…This work focuses on a typical high-strength structural steel (Chinese grade Q690, 0.12C-2.24Mn-0.26Si-0.07Cr-0.05Nb-0.02Ni) with a yield strength of 690 MPa, tensile strength of 734 MPa, and fracture elongation of 15.4%. Table 1 lists the data obtained by the uniaxial tensile test of Q690 steel [38].…”

Section: Three-types Of Gradient Rate Polycrystalline Modelsmentioning

confidence: 99%

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Optimizing Fatigue Performance in Gradient Structural Steels by Manipulating Grain Size Gradient Rate

Pan,

Chen,

et al. 2024

Preprint

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As a new type of high-performance material, gradient structural steel is widely used in engineering fields due to its unique microstructure and excellent mechanical properties. For the prevalent fatigue failure problem, the rate of change of the local grain size gradients along the structure (referred to as the gradient rate) is a key parameter in the design of gradient structures, which significantly affects the fatigue performance of gradient structural steel. In this work, a method of ‘Voronoi primary + secondary modeling’ is adopted to successfully establish three typical high-strength steel models corresponding to the convex, linear, and concave type gradient rates for gradient structures, focusing on the stress-strain response and crack propagation in structural steel with different gradient rates under cyclic loading. It is found that the concave gradient rate structural model is dominated by finer grains with larger volume fraction, which is conducive to hindering fatigue crack propagation and has the longest fatigue life; the linear structure is the second one, and the convex structure is the shortest one. The simulation results in this work are consistent with the relevant experimental phenomena. Therefore, when regulating the gradient rate, priority should be given to increasing the volume fraction of fine grains and designing a gradient rate structure dominated by fine grains to improve the fatigue life of the material.

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Section: Three-types Of Gradient Rate Polycrystalline Modelsmentioning

confidence: 99%

“…Table 1. The specific engineering stress-strain value of each tensile point [38]. Typically, gradient rates consist of three types, namely convex (A), linear (B), and concave (C) type.…”

Section: Three-types Of Gradient Rate Polycrystalline Modelsmentioning

confidence: 99%

Optimizing Fatigue Performance in Gradient Structural Steels by Manipulating Grain Size Gradient Rate

Pan,

Chen,

et al. 2024

Preprint

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“…The research contents mostly focus on the identification or qualitative characterization (most approaches are capable only of inferring the existence of damage) of small-scale damages such as fatigue, corrosion, and thermal damage. [18][19][20][21] The researches on local microdamage localization using nonlinear guided waves have attracted extensive attention. Lu et al 22 determined the time-of-flight (ToF) of the crack-scattered waves by analyzing the correlation of the wavelet energy spectrum, and then the ellipse localization algorithm is applied for crack localization.…”

Section: Introductionmentioning

confidence: 99%

Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

Liu

et al. 2023

Structural Health Monitoring

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Localization of fatigue cracks imposes immense significance to ensure the health of the engineering structures and prevent further catastrophic accidents. The nonlinear ultrasonic waves, especially the nonlinear Lamb waves, have been increasingly studied and employed for identifying micro-damages that are usually invisible to traditional linear ultrasonic waves. However, it remains a challenge to locate the fatigue cracks using nonlinear Lamb waves owing to the enormous difficulties in decoding location information from acoustic nonlinearity. Motivated by this, this work presents a data-driven method for precise location of fatigue crack using nonlinear Lamb waves. A 1D-Attention-convolutional neural network is developed to correlate the fatigue crack location with the wavelet coefficients at the second harmonic frequency of Lamb wave signals. The introduction of the Attention layer enables the models to pay more attention to the desired nonlinear features which dominates locating the fatigue crack. In particular, a convenient dataset creation scheme guided by the relative value label is proposed to generate sufficient data commonly required for deep learning approach. In addition, a lightweight single-excite-multiple-receive signal acquisition method is adopted instead of full-matrix capture method used in the traditional research, which highly improves detection efficiency. Numerical simulation and experimental validation manifest that the trained network can be used to establish the complex mapping between the nonlinear ultrasonic signals and the fatigue crack location features, so as to locate barely visible fatigue cracks. Our work provides a promising and practical way to facilitate nonlinear Lamb waves to accurately locate fatigue cracks in large-scale plate-like structures.

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Numerical simulation of fatigue fracture in gradient high-strength steel: effects of carbides and gradient structure on stress–strain response and crack propagation behavior

Pan,

Yang,

Zheng

et al. 2024

J Mater Sci

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Damage assessment in Q690 high strength structural steel using nonlinear Lamb waves

Cited by 15 publications

References 24 publications

Optimizing Fatigue Performance in Gradient Structural Steels by Manipulating Grain Size Gradient Rate

Optimizing Fatigue Performance in Gradient Structural Steels by Manipulating Grain Size Gradient Rate

Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

Numerical simulation of fatigue fracture in gradient high-strength steel: effects of carbides and gradient structure on stress–strain response and crack propagation behavior

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