Prediction and analysis of thermal aging behavior of magnetorheological grease
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 and the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2021-01-01
|
| Series: | Materials Research Express |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2053-1591/ac433d |
| _version_ | 1797746737476009984 |
|---|---|
| author | Guangxin Yang Jiabao Pan Dongdong Ye Kaiqiang Ye Hong Gao |
| author_facet | Guangxin Yang Jiabao Pan Dongdong Ye Kaiqiang Ye Hong Gao |
| author_sort | Guangxin Yang |
| collection | DOAJ |
| description | 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 and the influence law of continuous thermal effect (thermal aging) on the performance of MRG needs to be studied. In this article, the effect of thermal aging behavior on the rheological properties of MRG has been investigated. Accelerated heat treat the sample and test the shear stress under the condition of thermo-magnetic coupling. To reduce the time and cost during the study of MR materials, an improved and reliable artificial neural network (ANN) prediction model was developed to characterize and predict the relationship among temperature, aging time, magnetic field strength and the thermo-rheological properties of MRG. The test results of MRG before and after thermal aging show that thermal aging causes irreversible structural damage and the performance decreases with increasing aging time. The comparison of the ANN prediction results with the test results, the correlation coefficient R reached and exceeded 0.95. The results showed that the model had excellent prediction accuracy and could provide theoretical reference for the thermal aging behavior of MRG. |
| first_indexed | 2024-03-12T15:41:08Z |
| format | Article |
| id | doaj.art-5d05dc324abc4c16a0d48ff6807f83db |
| institution | Directory Open Access Journal |
| issn | 2053-1591 |
| language | English |
| last_indexed | 2024-03-12T15:41:08Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj.art-5d05dc324abc4c16a0d48ff6807f83db2023-08-09T15:57:11ZengIOP PublishingMaterials Research Express2053-15912021-01-0181212570110.1088/2053-1591/ac433dPrediction and analysis of thermal aging behavior of magnetorheological greaseGuangxin Yang0https://orcid.org/0000-0001-8535-4358Jiabao Pan1https://orcid.org/0000-0003-1433-459XDongdong Ye2Kaiqiang Ye3Hong Gao4School of Mechanical Engineering, Anhui Polytechnic University , Wuhu 241000, People’s Republic of ChinaSchool of Mechanical Engineering, Anhui Polytechnic University , Wuhu 241000, People’s Republic of ChinaSchool of Mechanical Engineering, Anhui Polytechnic University , Wuhu 241000, People’s Republic of ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University , Nanjing 210037, People’s Republic of ChinaSchool of Mechanical Engineering, Anhui Polytechnic University , Wuhu 241000, People’s Republic of ChinaMagnetorheological 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 and the influence law of continuous thermal effect (thermal aging) on the performance of MRG needs to be studied. In this article, the effect of thermal aging behavior on the rheological properties of MRG has been investigated. Accelerated heat treat the sample and test the shear stress under the condition of thermo-magnetic coupling. To reduce the time and cost during the study of MR materials, an improved and reliable artificial neural network (ANN) prediction model was developed to characterize and predict the relationship among temperature, aging time, magnetic field strength and the thermo-rheological properties of MRG. The test results of MRG before and after thermal aging show that thermal aging causes irreversible structural damage and the performance decreases with increasing aging time. The comparison of the ANN prediction results with the test results, the correlation coefficient R reached and exceeded 0.95. The results showed that the model had excellent prediction accuracy and could provide theoretical reference for the thermal aging behavior of MRG.https://doi.org/10.1088/2053-1591/ac433dmagnetorheological greasethermal agingneural networkprediction |
| spellingShingle | Guangxin Yang Jiabao Pan Dongdong Ye Kaiqiang Ye Hong Gao Prediction and analysis of thermal aging behavior of magnetorheological grease Materials Research Express magnetorheological grease thermal aging neural network prediction |
| title | Prediction and analysis of thermal aging behavior of magnetorheological grease |
| title_full | Prediction and analysis of thermal aging behavior of magnetorheological grease |
| title_fullStr | Prediction and analysis of thermal aging behavior of magnetorheological grease |
| title_full_unstemmed | Prediction and analysis of thermal aging behavior of magnetorheological grease |
| title_short | Prediction and analysis of thermal aging behavior of magnetorheological grease |
| title_sort | prediction and analysis of thermal aging behavior of magnetorheological grease |
| topic | magnetorheological grease thermal aging neural network prediction |
| url | https://doi.org/10.1088/2053-1591/ac433d |
| work_keys_str_mv | AT guangxinyang predictionandanalysisofthermalagingbehaviorofmagnetorheologicalgrease AT jiabaopan predictionandanalysisofthermalagingbehaviorofmagnetorheologicalgrease AT dongdongye predictionandanalysisofthermalagingbehaviorofmagnetorheologicalgrease AT kaiqiangye predictionandanalysisofthermalagingbehaviorofmagnetorheologicalgrease AT honggao predictionandanalysisofthermalagingbehaviorofmagnetorheologicalgrease |