Eigenfrequecy-based damage identification method for non-destructive testing based on topology optimization

Nishizu, Takafumi; Takezawa, Akihiro; Kitamura, Mitsuru

doi:10.1080/0305215x.2016.1190350

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Materials 2020, 13, 33 6 of 14 the elements, and ten inverse analyses were performed using ini d from 0.1 to 1.0, with an increment of 0.1. The penalization exponent p of 3 was used as in previous studies [31,[34][35][36]38]. Sequential quadratic programming in the MATLAB Optimization Toolbox (R2019a, MathWorks, Inc, Natick, MA, USA) was used for exploration and updating of the damage parameters.…”

Section: Damage Identification For Numerical Resultsmentioning

confidence: 99%

“…To clarify the physical meaning of the gray-scale element, Bendsøe et al [42] discussed the range of p based on Hashin-Shtrikman (HS) bounds [43] and proved that the SIMP method is physically permissible as long as p is greater than a certain value (e.g., p ≥ 3 for two-dimensional problems with Poisson's ratio of 1/3). In previous studies of damage identification based on topology optimization, Nishizu and Neumann et al [35,[38][39][40] set p = 3, Reumers et al [36] set p = 1, and Eslami et al [37] changed p gradually from 3 to 1. An element (i.e., microscopic area) will have various Young's modulus depending on the severity of the damage.…”

Section: Effect Of the Penalization Exponentmentioning

confidence: 99%

“…Based on this idea, Lee et al [34] demonstrated numerical examples for estimating damage in thin plates and beam models, taking resonant and anti-resonant frequencies as an objective function. Some numerical examples that focused on natural frequencies were also reported [35][36][37]. Niemann et al [38][39][40] estimated the approximate location of the damage in CFRP laminates after impact tests.…”

Section: Introductionmentioning

confidence: 99%

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Topology Optimization-Based Damage Identification Using Visualized Ultrasonic Wave Propagation

et al. 2019

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This study proposes a new damage identification method based on topology optimization, combined with visualized ultrasonic wave propagation. Although a moving diagram of traveling waves aids in damage detection, it is difficult to acquire quantitative information about the damage, for which topology optimization is suitable. In this approach, a damage parameter, varying Young’s modulus, represents the state of the damage in a finite element model. The feature of ultrasonic wave propagation (e.g., the maximum amplitude map in this study) is inversely reproduced in the model by optimizing the distribution of the damage parameters. The actual state of the damage was successfully estimated with high accuracy in numerical examples. The sensitivity of the objective function, as well as the appropriate penalization exponent for Young’s modulus, was discussed. Moreover, the proposed method was applied to experimentally measured wave propagation in an aluminum plate with an artificial crack, and the estimated damage state and the sensitivity of the objective function had the same tendency as the numerical example. These results demonstrate the feasibility of the proposed method.

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Section: Damage Identification For Numerical Resultsmentioning

confidence: 99%

Section: Effect Of the Penalization Exponentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Topology Optimization-Based Damage Identification Using Visualized Ultrasonic Wave Propagation

et al. 2019

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“…First, it is quite difficult to verify the laws of the distribution of the entire set of diagnostic parameters, and, therefore, it cannot be argued that they are all normal [13]. Secondly, a change in the mechanical properties of the studied zones leads to a change in the distribution laws of the studied informative parameters [14,15]. Therefore, it is necessary to apply criteria that are not sensitive to changes in the distribution laws [16].…”

Section: Introductionmentioning

confidence: 99%

Research of Diagnostic Parameters of Composite Materials Using Johnson Distribution

Babak¹,

Eremenko²,

Zaporozhets³

2019

IJC

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In this paper, it was proposed to carry out a preliminary normalization of diagnostic parameters using the Johnson distribution, which with three basic distribution groups (SL, SB, SU), covers a wide class of empirical distributions. The mathematical description of the family allows us to find the approximating probability density function in an explicit form, to determine the distribution parameters for obtaining the corresponding function (curve), as well as the inverse function for finding the quantiles of the specified levels. To assess the accuracy of the obtained normalized data, they were compared with the data obtained by replacing the resulting law with a Gaussian one. Percentages of values were compared in the implementation under study, which concentrated in the limits of estimated quantiles. Implementations were obtained using the simulation method. By the same method, the correctness (relative systematic error) of determining the quantile values of the specified levels was evaluated. The error value δ was estimated between the conditionally true quantile value calculated from the generated pseudo-general complex and the value estimated using the methods considered in the paper. Obtained data show that the relative error in the calculation of quantiles using the Johnson distribution does not exceed 0.07% and decreases in two orders of magnitude than the currently accepted procedure for replacing sample laws with Gaussian.

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Statistical topology optimization scheme for structural damage identification

Silva

Yoon

2023

Computers & Structures

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Eigenfrequecy-based damage identification method for non-destructive testing based on topology optimization

Cited by 7 publications

References 37 publications

Topology Optimization-Based Damage Identification Using Visualized Ultrasonic Wave Propagation

Topology Optimization-Based Damage Identification Using Visualized Ultrasonic Wave Propagation

Research of Diagnostic Parameters of Composite Materials Using Johnson Distribution

Statistical topology optimization scheme for structural damage identification

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