Evidence map›Paper›PMID 36556523›Full record

ArticleMaterials (Basel, Switzerland)2022

Reconstruction of Composite Stiffness Matrix with Array-Guided Wave-Based Genetic Algorithm.

Menglong Liu, Yaohui Zhang, Lun Li, Gongfa Chen, Fangsen Cui

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Menglong LiuSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.ORCID 0000-0002-9861-8815
Yaohui ZhangSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.
Lun LiSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.ORCID 0000-0001-6402-8000
Gongfa ChenSchool of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0001-7232-9583
Fangsen CuiInstitute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632, Singapore.ORCID 0000-0001-6682-9768

Funding

Guangdong Basic and Applied Basic Research Foundation 2019A1515110704National Natural Science Foundation of China 52105142Shenzhen Stable Support Grant GXWD20201230155427003-20200731161831019
6 · The paper itself

Abstract

Accurate measurement of the material parameters of composite in a nondestructive manner is of great significance for evaluating mechanical performance. This study proposes to use a genetic algorithm (GA) to reconstruct the stiffness matrix of carbon fiber reinforced polymer (CFRP) with array-guided wave (GW)-based GA. By comparing the numerically calculated GW dispersion curves with the experimental wave number-frequency contour calculated with a two-dimensional Fourier transform (2D-FFT), the matching coefficient is directly obtained as the objective function of the GA, avoiding the overhead of sorting out the respective GW modes. Then the measured stiffness matrix with tensile testing and the longitudinal wave in the unidirectional CFRP is compared with the reconstructed parameters from unidirectional, cross-ply, and quasi-isotropic CFRPs with the GA. For the four independent parameters, excluding C12, an average value of 11.62% for the maximum deviation is achieved among the CFRPs with three stacking sequences, and an average deviation of 11.03% in unidirectional CFRPs is achieved for the parameters measured with different methods. A further correction of fiber orientation results in a relative deviation of only 2.72% for the elastic modulus along the tensile direction, and an expansion of the GW frequency range for the GA narrows down the relative deviation of C12 to 3.9%. The proposed GW-based GA opens up a way of in situ and nondestructive measurement for the composite stiffness matrix.

Indexed as

carbon-fiber-reinforced polymergenetic algorithmguided wavestiffness matrix

Identifiers

PMID36556523
PMCPMC9781181

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