Frictional behaviour of Al359/SiC/20p composite under isothermal and non-isothermal hot-working conditions as a function of surface roughness

1997 ◽  
Vol 72 (2) ◽  
pp. 195-200 ◽  
Author(s):  
A.M de Sanctis ◽  
A Forcellese ◽  
S.M Roberts ◽  
P.J Withers
2021 ◽  
Vol 158 ◽  
pp. 106928
Author(s):  
Ivan Serebriakov ◽  
Eli Saul Puchi-Cabrera ◽  
Laurent Dubar ◽  
Philippe Moreau ◽  
Damien Meresse ◽  
...  

Ergonomics ◽  
1971 ◽  
Vol 14 (1) ◽  
pp. 85-90 ◽  
Author(s):  
R. B. WELCH ◽  
E. O. LONGLEY ◽  
O. LOMAEV

2019 ◽  
Vol 62 (6) ◽  
pp. 1743-1753
Author(s):  
Tao Wang ◽  
Baoqin Wen ◽  
Za Kan ◽  
Jingbin Li

Abstract. A horizontal mixer can realize the cutting and mixing of coarse and fine feeds and achieve the purpose of scientific feeding. Studying the wear resistance of the mixer blades can improve the service life of a horizontal mixer. The wear performance of blades made of three different materials (manganese steel, tool steel, and spring steel) was studied under laboratory conditions and working conditions. In laboratory conditions, the wear scar morphology and surface elements were analyzed by means of three-dimensional topography, scanning electron microscopy, and energy spectrum analysis. The results show that the friction coefficient, wear quality, and surface roughness of manganese steel blades had the lowest values of 0.49158, 0.0061 mg, and 4.341 µm in three groups of tests. In working conditions, the wear amount and surface roughness of the manganese steel blades in different zones of the mixer were the lowest. In addition, electron backscatter diffraction (EBSD) results showed that the grain size of the manganese steel blades was the smallest. Therefore, compared with the tool steel and spring steel blades, the manganese steel blades showed excellent wear resistance.HighlightsThe wear characteristics of horizontal mixer blades with different materials were studied.The wear characteristics of the blades were studied under laboratory and working conditions.The effect of grain on the wear performance of the blades was studied by electron backscatter diffraction. Keywords: Blade, Grain, Horizontal mixer, Wear resistance, Wear test.


Author(s):  
Ming Qiu ◽  
Yong-Zhen Zhang ◽  
Jun Zhu

By using genetic algorithms and radius basis function (GARBF) neural network, the predicting model of friction coefficient has been established based on a measured database with five sliding velocities of 40, 55, 70, 85, 100 m/s and four different normal pressures of 0.1333, 0.4667, 0.60 and 0.7333 MPa. The modeling results confirm the feasibility of the GARBF network and its good correlation with the experimental results. The predictive quality of the GARBF network can be further improved by enlarging the training datasets and by optimizing the network construction. A well-trained GARBF modeling is expected to be very helpful for selecting composite component under different working conditions, and for predicting tribological properties. Finally, by using GARBF modeling data to predict analysis, the results show that the friction coefficients of these composites were increased with the increase in material thermal capability at some region.


Sign in / Sign up

Export Citation Format

Share Document