This research aimed to increase the natural frequencies of a non-rotating 2D tri-axial braided composite (2DTBC) fan blade. The investigation utilized a multidisciplinary approach, integrating Artificial Neural Network (ANN) modeling, analytical method, Finite Element (FE) analysis, optimization techniques, and experimental validation. The ANN captured the complex relationship between the braiding machine and structure parameters. The mechanical properties of the 2DTBC were determined through the micromechanical modeling, and the thin-shell analysis was applied to describe the blade's displacement and strain characteristics. Micromechanical modeling examines material behavior at the microscopic level, and thin shell analysis focuses on modeling and analyzing thin, curved structures using simplified equations. The FE method facilitated the formulation of the equation of motion and the calculation of natural frequencies. A genetic algorithm, focused on a single-objective optimization, was employed to refine the braiding structure parameters and the number of composite layers, thereby enhancing the blade's natural frequencies. The optimized 2DTBC blade was subsequently fabricated and validated through impact hammer modal testing, showing strong agreement with the predictions from the combined ANN-analytical-FEM-GA model. The optimization of the braiding parameters led to a significant increase in the blade's natural frequencies.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582590 | PMC |
| http://dx.doi.org/10.1038/s41598-024-80198-4 | DOI Listing |
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