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• W4386002792 endingPage "117454" @default.
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• W4386002792 abstract "Shape memory polymers are smart materials that can “remember” their original shape and return to it when exposed to certain stimuli, such as heat, light, or a change in pH. This research investigates the compressive nonlinear response of a sinusoidal shape memory unit cell made of PLA. Different architectures with diverse thicknesses are optimized through a finite element analysis, and the values of absorbed energy, dissipated energy, and peak force are extracted. The obtained data is subsequently used to train a judiciously structured artificial neural network. Three algorithms, including Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient backpropagation, are utilized for training the neural network. The function obtained is employed to formulate the objective function to be minimized- (or maximized), and the optimal geometric dimensions are extracted. In the experimental part, the sinusoidal cell is made using a 3D printer and PLA material, and a quasi-static test is performed. After each quasi-static test, the structure is exposed to heat, and thanks to its shape memory properties, it returns to its original shape. This cycle is repeated up to four times. Testing results showed that the proposed sinusoidal structure possessed an attractive mechanical behaviour relevant to the specific geometric parameters and loading conditions. It was observed that the amount of absorbed energy and the maximum forces decreased in each cycle compared to the previous cycle. The experimental results demonstrate that the absorbed energy by the sinusoidal cell decreased by 4.7% in the second loading, 12.98% in the third loading, and 33.7% in the fourth loading. The results reveal that the reduction in the peak force of structure is more pronounced compared to the decrease in absorbed energy under cyclic loading-unloading. Based on the obtained results, it can be concluded that the proposed structure can be used as a suitable energy absorber with the ability to recover the original shape and maintain the mechanical properties up to several cycles of loading." @default.
• W4386002792 created "2023-08-20" @default.
• W4386002792 creator A5020583415 @default.
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• W4386002792 date "2023-11-01" @default.
• W4386002792 modified "2023-10-12" @default.
• W4386002792 title "Experimental and numerical analysis of shape memory sinusoidal lattice structure: Optimization through fusing an artificial neural network to a genetic algorithm" @default.
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• W4386002792 doi "https://doi.org/10.1016/j.compstruct.2023.117454" @default.
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