Electrical Sliding Wear Properties of Mo-Reinforced MoS_2/Cu Composites

An adequate hardness of MoS2/Cu composites has not been achieved if these materials are applied under the extreme wear conditions. Therefore, Me-reinforced MoS2/Cu composites were prepared by powder metallurgy （P/M） methods. The electrical sliding wear properties in the absence or presence of Mereinforced MoS2/Cu composites were tested by HST-100 high speed electric-tribometer. The hardness, electrical conductivity, density, and microstmcture of MoS2/Cu composites were observed. Me-reinforcement MoS2/Cu composites are of good electrical conductivity, while the hardness of Mo-reinforcedment MoS2/Cu composites is about 33.3% higher than that of MoS2/Cu composites. With the addition of Me, composites show better wear properties under high speed and large electric current due to the improvement of hardness. The effects of current intensity and sliding velocity on the wear properties of the tested materials are complicated, and the wear mechanisms of MoS2/Cu composites are mainly abrasive wear and adhesive wear with arc erosion.

Prediction of contact resistance of electrical contact wear using different machine learning algorithms

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.

Effect of La_2O_3 on electrical friction and wear properties of Cu-graphite composites

Sliding friction and wear experiments using Cu-La2O3-graphite composites against Cu-5 wt.%Ag alloy ring were conducted at a constant sliding speed of 10 m/s, a current density of 10 A/cm2 and a load of 2.5 N/cm2. These composites with different La2O3 and graphite contents were fabricated by hot-pressing. Physical and mechanical properties of the composites were examined. Morphologies of the worn surface of composites were observed using optical microscopy. X-ray photoelectron spectroscopy spectra were used to study compositions of the lubricating film. The results showed that with the increasing addition of La2O3, hardness, flexural strength and electrical resistivity increased, but the relative density dropped. The friction coefficient increased with the increasing addition of La2O3. Composite containing 3 vol.% of La2O3 and 37 vol.% of graphite showed the best wear resistance. The main wear mechanisms of composites were abrasive wear, oxidative wear and adhesive wear.