Estimation of elastic and modal parameters in composites using vibration analysis

Yesilyurt, Isa; Gursoy, H. N.

doi:10.1177/1077546313486275

Cited by 24 publications

(14 citation statements)

References 30 publications

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“…Combined finite element and frequency response was carried out by references [ 22 , 23 ], who aimed to study the dynamic properties of the composite material. The study of the damping properties and modelling of the dynamic properties have been conducted by many investigators [ 24 , 25 , 26 , 27 , 28 ] using the finite element methods to predict the natural frequency and mode shapes. These results allow us to characterize the dynamic behaviors for metals and new composite materials.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics Study for Surface Composite of AMMNCs Matrix Fabricated by Friction Stir Process

Moustafa

2018

Materials

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In the present work, Aluminum Metal Matrix Surface Nano Composites (AMMSNCs) were manufactured using Friction Stir Processing (FSP). Moreover, the fabricated surface composite matrix was exposed to a different number of tool passes with different processing parameters. The tensile test and microstructure examinations were used to study the mechanical properties of the composite surface. The dynamic properties were predicted using modal analysis and finite element methods. After this, dynamic characterization was achieved by combining the numerical and experimental methods to investigate the effects of changing the number of passes on the natural frequency and the damping capacity of the AMMSNCs manufactured using FSP. The results indicated that the damping capacity and dynamic behavior improved with an increased number of FSP passes.

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Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics Study for Surface Composite of AMMNCs Matrix Fabricated by Friction Stir Process

Moustafa

2018

Materials

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“…Exploiting resonant frequencies is quite common in many branches of engineering, and can be effectively used to estimate Young’s modulus. 11–15 In the previous section, it was reported that each specimen was subject to a bending test (under a point tip load), based on the theory from the Experimental elastic modulus extraction approaches section. Each specimen was subject to a free vibration test (without re-clamping): a typical free decay time series is shown in Figure 6(a), in which the tip of the cantilever was displaced and released to promote a response in the first flexural mode.…”

Section: Resultsmentioning

confidence: 99%

On the flexural stiffness of 3D printer thermoplastic

Virgin

2016

International Journal of Mechanical Engineering Education

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This paper describes the process of estimating Young's modulus for the thermoplastic material commonly used in a type of 3D printer. Its twin goals are to compare and contrast a number of simple techniques from elementary structural analysis and to assess the influence of the printer density settings and print orientation (effective material anisotropy). Since components printed using additive manufacturing are used extensively for student projects, often involving load-bearing components, this contribution seeks to shed some light on fundamental modeling issues

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“…Generally, conventional signal processing techniques such as Fast Fourier Transform (FFT) [1][2][3], and Short-Time Fourier Transform (STFT) [4,5] are employed to analyze the linear and stationary signals. However, the responses of structures in the real world are inherently nonlinear according to structural health monitoring systems [6,7] and non-stationary.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Mousavi

Zhang

Masri

et al. 2020

Sensors

119

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Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.

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Estimation of elastic and modal parameters in composites using vibration analysis

Cited by 24 publications

References 30 publications

Dynamic Characteristics Study for Surface Composite of AMMNCs Matrix Fabricated by Friction Stir Process

Dynamic Characteristics Study for Surface Composite of AMMNCs Matrix Fabricated by Friction Stir Process

On the flexural stiffness of 3D printer thermoplastic

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

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