2022
DOI: 10.1016/j.jmbbm.2021.104918
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Prediction of mechanical solutions for a laminated LCEs system fusing an analytical model and neural networks

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Cited by 3 publications
(2 citation statements)
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“…Remarkably, LCE is capable of sensing external stimuli, making judgments, and exhibiting reversible, large, and complex shape changes in response to different stimuli. The above conventional stimuli include thermal, 21,22 electric, [23][24][25] humidity, 26 and magnetic. 27 The light control is particularly attractive, because of its non-contact remote capability, rapidity, and precision.…”
Section: Introductionmentioning
confidence: 99%
“…Remarkably, LCE is capable of sensing external stimuli, making judgments, and exhibiting reversible, large, and complex shape changes in response to different stimuli. The above conventional stimuli include thermal, 21,22 electric, [23][24][25] humidity, 26 and magnetic. 27 The light control is particularly attractive, because of its non-contact remote capability, rapidity, and precision.…”
Section: Introductionmentioning
confidence: 99%
“…Based on these data, the temperature distribution of metal substrate alloys with different thickness is predicted by ANN. Wang et al (2022) used the exact 2-D thermoelastic model of laminated beam to establish a BP neural network database containing 561 sets of data for predicting the mechanical solutions of laminated beam. It would be of significance to apply the BP neural network to predict the mechanical properties of 3-D laminated plates under combined thermo-mechanical loads.…”
Section: Introductionmentioning
confidence: 99%