CNTs-Ag-G electrical contact composite material was prepared by means of powder metallurgical method. The influence of the graphite content on sliding wear characteristics of electrical contact levels was examined. In...CNTs-Ag-G electrical contact composite material was prepared by means of powder metallurgical method. The influence of the graphite content on sliding wear characteristics of electrical contact levels was examined. In experiments, CNTs content was retained as 1% (mass fraction), and graphite was added at content levels of 8%, 10%, 13%, 15% and 18%, respectively. The results indicate that with the increase of graphite content, the contact resistance of electrical contacts is enhanced to a certain level then remains constant. Friction coefficient decreases gradually with the increase of graphite content. Wear mass loss decreases to the minimum value then increases. With the small content of graphite, the adhesive wear is hindered, which leads to the decrease of wear mass loss, while excessive graphite brings much more worn debris, resulting in the increase of mass loss. It is concluded that wear mass loss reaches the minimum value when the graphite mass fraction is about 13%. Compared with conventional Ag-G contact material, the wear mass loss of CNTs-Ag-G composite is much less due to the obvious increase of hardness and electrical conductivity, decline of friction surface temperature and inhibition of adhesive wear between composites and slip rings.展开更多
H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission,and has excellent performance in all aspects.Since the wear behavior of electrical contact ...H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission,and has excellent performance in all aspects.Since the wear behavior of electrical contact pairs is particularly complex when they are in service,we evaluated the effects of load,sliding velocity,displacement amplitude,current intensity,and surface roughness on the changes in contact resistance.Machine learning(ML)algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance.Random forest(RF),support vector regression(SVR)and BP neural network(BPNN)algorithms were used to establish RF,SVR and BPNN models,respectively,and the experimental data were trained and tested.It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear.Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties.The wear behavior of electrical contacts is influenced by factors such as load,sliding speed,displacement amplitude,current intensity,and surface roughness during operation.Machine learning algorithms can predict the electrical contact performance after wear caused by these factors.Experimental results indicate that an increase in load,current,and surface roughness leads to a decrease in stable mean resistance,while an increase in displacement amplitude and frequency results in an increase in stable mean resistance,leading to a decline in electrical contact performance.To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors,three algorithms(random forest(RF),support vector regression(SVR),and BP neural network(BPNN))were used to train and test experimental results,resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear.The prediction results showed that the BPNN model performed better in predicting the electrical contact performance compared to the RF and SVR models.展开更多
基金Project(50271021) supported by the National Natural Science Foundation of ChinaProject(ZD2008003) supported by Key Science Foundation of the Education Department of Anhui Province, China+2 种基金Project(CF07-10) supported by the Innovation Center for Postgraduates at HFNL (USTC), ChinaProject(KF0702) supported by the Open Project Program of Ministry of Education of ChinaProject supported by Nippon Sheet Glass Foundation of Japan for Materials Science and Engineering
文摘CNTs-Ag-G electrical contact composite material was prepared by means of powder metallurgical method. The influence of the graphite content on sliding wear characteristics of electrical contact levels was examined. In experiments, CNTs content was retained as 1% (mass fraction), and graphite was added at content levels of 8%, 10%, 13%, 15% and 18%, respectively. The results indicate that with the increase of graphite content, the contact resistance of electrical contacts is enhanced to a certain level then remains constant. Friction coefficient decreases gradually with the increase of graphite content. Wear mass loss decreases to the minimum value then increases. With the small content of graphite, the adhesive wear is hindered, which leads to the decrease of wear mass loss, while excessive graphite brings much more worn debris, resulting in the increase of mass loss. It is concluded that wear mass loss reaches the minimum value when the graphite mass fraction is about 13%. Compared with conventional Ag-G contact material, the wear mass loss of CNTs-Ag-G composite is much less due to the obvious increase of hardness and electrical conductivity, decline of friction surface temperature and inhibition of adhesive wear between composites and slip rings.
基金the Sichuan Science and Technology Planning Project(2022ZYD0029 and 2022JDJQ0019)the National Natural Science Foundation of China(51875343)。
文摘H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission,and has excellent performance in all aspects.Since the wear behavior of electrical contact pairs is particularly complex when they are in service,we evaluated the effects of load,sliding velocity,displacement amplitude,current intensity,and surface roughness on the changes in contact resistance.Machine learning(ML)algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance.Random forest(RF),support vector regression(SVR)and BP neural network(BPNN)algorithms were used to establish RF,SVR and BPNN models,respectively,and the experimental data were trained and tested.It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear.Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties.The wear behavior of electrical contacts is influenced by factors such as load,sliding speed,displacement amplitude,current intensity,and surface roughness during operation.Machine learning algorithms can predict the electrical contact performance after wear caused by these factors.Experimental results indicate that an increase in load,current,and surface roughness leads to a decrease in stable mean resistance,while an increase in displacement amplitude and frequency results in an increase in stable mean resistance,leading to a decline in electrical contact performance.To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors,three algorithms(random forest(RF),support vector regression(SVR),and BP neural network(BPNN))were used to train and test experimental results,resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear.The prediction results showed that the BPNN model performed better in predicting the electrical contact performance compared to the RF and SVR models.
