Creep and Recovery Behaviors of Lithium-Based Magnetorheological Grease

Ye, Xudan; Wang, Jiong

doi:10.3389/fmats.2021.668125

Cited by 3 publications

(1 citation statement)

References 26 publications

(32 reference statements)

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“…The predicted progress of MRG at magnetic field strength from 0 to 1100 mT shows excellent predictions at 0 h, 4 h, and 8 h, but there is a great discrepancy between the predicted value and the experimental value at 24 h. Further discussion will be given as following. The structural recovery properties of MR materials are more dependent on magnetic field strength and temperature [29]. The test temperature in figure 6 is 65 °C.…”

Section: Prediction Resultsmentioning

confidence: 99%

Prediction and analysis of thermal aging behavior of magnetorheological grease

Yang

Pan

et al. 2021

Mater. Res. Express

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Magnetorheological grease (MRG) is a new type of field-response intelligent material with controllable performance and excellent settlement stability, which is feasible to replace traditional materials. The heating phenomenon of magnetorheological (MR) devices is more common during operation, while the MRG as a medium has more significant thermal rheological characteristics in the heating process. In the process of MRG modeling, a model is established to study the effect of thermal-magnetic coupling on its performance and to save experimental time and reduce costs. Hence, an improved and reliable artificial neural network (ANN) prediction model is established to characterize and predict the relationship among temperature, aging time, magnetic field strength and thermal-rheological properties of MRG. The training data of neural network were obtained from the experiments under the condition of thermomagnetic coupling with rotational rheometer. After the neural network was trained and substituted into the test set data, the predicted results were compared with the experimental results, the correlation coefficient R reached and exceeded 0.95. The results show that the model has excellent prediction accuracy and can provide theoretical reference for the thermal aging behavior of MRG.

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Section: Prediction Resultsmentioning

confidence: 99%