Effect of Nb addition on the microstructural,mechanical and electrochemical characteristics of AlCrFeNiCu high-entropy alloy

AlCrFeNiCuNbx(x=0.05,0.15,and 0.26)high-entropy alloys(HEAs)were successfully fabricated using the laser metal deposition technique.The laser power of 1600 W and scanning speed of 1.2 m/min were used during laser processing of the alloys.The microstructural,mechanical,and electrochemical characteristics of the alloys were evaluated using various advanced characterization techniques.Results showed that the alloys exhibited a dual-phase structure with dendritic grains.The inclusion of Nb in the AlCrFeNiCu alloy matrix promoted the formation of fine eutectic structures and changed the shape of the grains from columnar to equiaxed.The Cu content decreased with the increase in the content of Nb,whereas the Al content increased with the increase in the content of Nb.The findings indicated that the presence of Nb in the alloy promoted the formation and enhanced the stability of the body-centered cubic(bcc)phase.All of the alloys that contained Nb also exhibited high hardness,compressive strength,and wear resistance.Furthermore,the low current density and positive shift in potential exhibited by HEAs with appropriate addition of Nb highlighted the superior anticorrosive properties.

Friction and wear properties of copper matrix composites reinforced by tungsten-coated carbon nanotubes

Carbon nanotubes （CNTs） were coated by tungsten layer using metal organic chemical vapor deposition process with tungsten hexacarbonyl as a precursor. The W-coated CNTs （W-CNTs） were dispersed into Cu powders by magnetic stirring process and then the mixed powders were consolidated by spark plasma sintering to fabricate W-CNTs/Cu composites. The CNTs/Cu composites were fabricated using the similafprocesses. The friction coefficient and mass wear loss of W-CNTs/Cu and CNTs/Cu composites were studied. The results showed that the W-CNT content, interfacial bonding situation, and applied load could influence the friction coefficient and wear loss of W-CNTs/Cu com- posites. When the W-CNT content was 1.0 wt.%, the W-CNTs/Cu composites got the minimum friction coefficient and wear loss, which were decreased by 72.1% and 47.6%, respectively, compared with pure Cu specimen. The friction coefficient and wear loss of W-CNTs/Cu composites were lower than those of CNTs/Cu composites, which was due to that the interracial bonding at （W-CNTs）-Cu interface was better than that at CNTs-Cu interface. The friction coefficient of composites did not vary obviously with increasing applied load, while the wear loss of composites increased significantly with the increase of applied load.

Influence of loading and rotation speed on Friction and Wear properties of CuAlBi Alloy

The variation of the friction coefficient of the CuAlBi alloy at different connecting loading and friction speed were investigated by using MMU-5G sliding friction-wear tester, besides, the wear mass loss of the CuAlBi alloy was measured, and the influence of loading and rotation speed on friction and wear properties of CuAlBi alloy was also discussed. The results show that the friction coefficient increase then decrease with increase of connecting loading as well as decreases with increase of friction speed, and the wear loss mass increases with increase of connecting loading and friction speed. As a result, the wear failure form of CuAlBi alloy is mainly ploughing.

A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic,kernel-based,and weighted extreme learning machine

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated.

Influence of adding carbon nanotubes and graphite to Ag-MoS_2 composites on the electrical sliding wear properties

Silver matrix composite brushes were fabricated by means of powder metallurgy, which included pressing at 300 MPa and then sintering for 1 h in pure H2 protective atmosphere at 700 ℃ and repressing at 500 MPa. Four kinds composites with different compositions were produced, and the mechanical properties and electrical wear performance were investigated. The results showed that the composite added with carbon nanotubes had a higher hardness and strength, a lower contact voltage drop and an excellent anti-wear property in electrical sliding wear, because of the reinforcement ability of carbon nanotubes. Adding graphite to the composite also decreased the wear loss and contact voltage drop, because graphite had an electrical current conducting ability which not only made the current pass the lubricating films easily but also eliminated and reduced the arc and spark effectively.