The Validity of Linear Elasticity in Analyzing Surface Texturing Effect for Elastohydrodynamic Lubrication

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
A. Shinkarenko ◽  
Y. Kligerman ◽  
I. Etsion
Keyword(s):  
Linear Elasticity ◽  
Surface Texturing ◽  
Elastohydrodynamic Lubrication ◽  
Tribological Performance ◽  
Laser Surface ◽  
Operating Conditions ◽  
Constitutive Law ◽  
Laser Surface Texturing ◽  
Geometrical And Physical Nonlinearities ◽  
Wide Range

This paper presents a nonlinear theoretical model to study the effect of laser surface texturing on the tribological performance in soft elastohydrodynamic lubrication. Both geometrical and physical nonlinearities of the elastomer are considered by using a logarithmic strain and the Mooney–Rivlin constitutive law, respectively. The results of the present nonlinear model are compared with a previous linear one over a wide range of operating conditions. It is found that the simpler linear elasticity model predicts results that are only slightly different from these predicted by the more accurate nonlinear one. Hence, the linear elasticity model can be practically considered valid over the entire range of operating conditions.

The Effect of Laser Surface Texturing on Soft Elasto-Hydrodynamic Lubrication Considering Non-Linear Elasticity

2008 ◽  
Author(s):  
Alexey Shinkarenko ◽  
Yuri Kligerman ◽  
Izhak Etsion
Keyword(s):  
Linear Model ◽  
Hydrodynamic Lubrication ◽  
Load Capacity ◽  
Surface Texturing ◽  
Laser Surface ◽  
Operating Conditions ◽  
Constitutive Law ◽  
Laser Surface Texturing ◽  
Wide Range ◽  
Non Linear

A non-linear theoretical model is presented to study the effect of laser surface texturing (LST) on the load carrying capacity in soft elasto-hydrodynamic lubrication (SEHL). Both geometrical and physical non-linearity of the elastomer is considered by using a logarithmic strain and the Mooney-Rivlin constitutive law, respectively. The results of the present non-linear model are compared with those of a previous linear one over a wide range of operating conditions. It is found that the two models predict the same optimum LST parameters for maximum load capacity but the non-linear model gives load capacity that is up to 10% lower than that obtained from the linear model.

Research on Tribological Performance of Cylinder Liner by Micro-Laser Surface Texturing

2011 ◽  
Vol 4 (4) ◽  
pp. 1318-1324 ◽  
Author(s):  
Yin Bifeng ◽  
Li Xiaodong ◽  
Fu Yonghong ◽  
Wang Yun
Keyword(s):  
Surface Texturing ◽  
Cylinder Liner ◽  
Tribological Performance ◽  
Laser Surface ◽  
Laser Surface Texturing

scholarly journals Tribological Performance of Random Sinter Pores vs. Deterministic Laser Surface Textures: An Experimental and Machine Learning Approach

2021 ◽  
Author(s):  
Guido Boidi ◽  
Philipp G. Grützmacher ◽  
Markus Varga ◽  
Márcio Rodrigues da Silva ◽  
Carsten Gachot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Texturing ◽  
Tribological Performance ◽  
Laser Surface ◽  
Laser Surface Texturing ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Surface Irregularities ◽  
Sintered Materials ◽  
Surface Pores

This work critically scrutinizes and compares the tribological performance of randomly distributed surface pores in sintered materials and precisely tailored laser textures produced by different laser surface texturing techniques. The pore distributions and dimensions were modified by changing the sintering parameters, while the topological features of the laser textures were varied by changing the laser sources and structuring parameters. Ball-on-disc tribological experiments were carried out under lubricated combined sliding-rolling conditions. Film thickness was measured in-situ through a specific interferometry technique developed for the study of rough surfaces. Furthermore, a machine learning approach based on the radial basis function method was proposed to predict the frictional behavior of contact interfaces with surface irregularities. The main results show that both sintered and laser textured materials can reduce friction compared to the untextured material under certain operating conditions. Moreover, the machine learning model was shown to predict results with satisfactory accuracy. It was also found that the performance of sintered materials could lead to similar improvements as achieved by textured surfaces, even if surface pores are randomly distributed and not precisely controlled.

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