AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Friction Article
PDF (1.4 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Multi-objective optimization of the design parameters of texture bottom profiles in a parallel slider

Nilesh D. HINGAWE1( ), Skylab P. BHORE1
Rotor Dynamics and Vibration Diagnostics Lab, Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj 211004, India
Show Author Information

Abstract

In this paper, a square textured parallel slider is considered for a study to improve the hydrodynamic performance of moving parts. The numerical method is employed for the analysis of a square texture with different bottom profiles: flat, triangle T1, triangle T2, and curved. The governing Reynolds equation is solved using a finite difference numerical discretization technique with the Gauss-Seidel iterative scheme. To obtain optimized process parameters, the response surface methodology-based central composite design along with grey relational analysis multi-objective optimization is used. The multi-objective responses are the load capacity and friction coefficient. The triangle T2 bottom profile yields the highest load capacity and the lowest friction coefficient compared to flat, triangle T1, and curved bottom profiles, of which the triangle T1 bottom profile yields the worst results. For the triangle T2 bottom profile, the flow speed is found to be the most significant process parameter, followed by the aspect ratio. Texture density is found to be the least significant parameter based on increasing the load capacity and decreasing the friction coefficient.

Keywords

texture bottom profileload capacityfriction coefficientdesign of experimentmulti-objective optimization

References

[1]
I Etsion. Modeling of surface texturing in hydrodynamic lubrication. Friction 1(3): 195-209 (2013)
Crossref Google Scholar
[2]
D Gropper, L Wang, T J Harvey. Hydrodynamic lubrication of textured surfaces: A review of modeling techniques and key findings. Tribol Int 94: 509-529 (2016)
Crossref Google Scholar
[3]
Y Kligerman, I Etsion. Analysis of the hydrodynamic effects in a surface textured circumferential gas seal. Tribol Trans 44(3): 472-478 (2001)
Crossref Google Scholar
[4]
Y Kligerman, I Etsion, A Shinkarenko. Improving tribological performance of piston rings by partial surface texturing. J Tribol 127(3): 632-638 (2005)
Crossref Google Scholar
[5]
S Yagi, N Katayama, H Hasegawa, H Matsushita, S Okihara. Effects of microscale texture on the tribological behavior of paper-based friction materials for a wet clutch. Tribol Online 10(5): 390-396 (2015)
Crossref Google Scholar
[6]
S Jesudass Thomas, K Kalaichelvan. Comparative study of the effect of surface texturing on cutting tool in dry cutting. Mater Manuf Process 33(6): 683-694 (2018)
Crossref Google Scholar
[7]
S Ghosh, S Abanteriba. Status of surface modification techniques for artificial hip implants. Sci Technol Adv Mater 17(1): 715-735 (2016)
Crossref Google Scholar
[8]
A Arslan, H H Masjuki, M A Kalam, M Varman, R A Mufti, M H Mosarof, L S Khuong, M M Quazi. Surface texture manufacturing techniques and tribological effect of surface texturing on cutting tool performance: A review. Crit Rev Solid State Mater Sci 41(6): 447-481 (2016)
Crossref Google Scholar
[9]
M Wakuda, Y Yamauchi, S Kanzaki, Y Yasuda. Effect of surface texturing on friction reduction between ceramic and steel materials under lubricated sliding contact. Wear 254(3-4): 356-363 (2003)
Crossref Google Scholar
[10]
J W Byun, H S Shin, M H Kwon, B H Kim, C N Chu. Surface texturing by micro ECM for friction reduction. Int J Precis Eng Manuf 11(5): 747-753 (2010)
Crossref Google Scholar
[11]
X L Wang, K Kato. Improving the anti-seizure ability of SiC seal in water with RIE texturing. Tribol Lett 14(4): 275-280 (2003)
Google Scholar
[12]
L S Stephens, R Siripuram, M Hayden, B McCartt. Deterministic micro asperities on bearings and seals using a modified LIGA process. In Proceedings of ASME Turbo Expo 2002: Power for Land, Sea, and Air, Amsterdam, The Netherlands, 2002: 573-580.
Crossref
[13]
A Zavos, P G Nikolakopoulos. Tribological characterization of smooth and artificially textured coated surfaces using block-on-ring tests. FME Trans 43(3): 191-197 (2015)
Google Scholar
[14]
B Raeymaekers, I Etsion, F E Talke. A model for magnetic tape/guide friction reduction by laser surface texturing. Tribol Lett 28(1): 9-17 (2007)
Crossref Google Scholar
[15]
I Etsion. State of the art in laser surface texturing. J Tribol 127(1): 248-253 (2005)
Crossref Google Scholar
[16]
R B Siripuram, L S Stephens. Effect of deterministic asperity geometry on hydrodynamic lubrication. J Tribol 126(3): 527-534 (2004)
Crossref Google Scholar
[17]
V G Marian, M Kilian, W Scholz. Theoretical and experimental analysis of a partially textured thrust bearing with square dimples. Proc Inst Mech Eng, Part J: J Eng Tribol 221(7): 771-778 (2007)
Crossref Google Scholar
[18]
H Yu, H Deng, W Huang, X Wang. The effect of dimple shapes on friction of parallel surfaces. Proc Inst Mech Eng, Part J: Eng Tribol 225(8): 693-703 (2011)
Crossref Google Scholar
[19]
T Nanbu, N Ren, Y Yasuda, D Zhu, Q J Wang. Micro- textures in concentrated conformal-contact lubrication: Effects of texture bottom shape and surface relative motion. Tribol Lett 29(3): 241-252 (2008)
Crossref Google Scholar
[20]
C Shen, M M Khonsari. Effect of dimple’s internal structure on hydrodynamic lubrication. Tribol Lett 52(3): 415-430 (2013)
Crossref Google Scholar
[21]
J Han, L Fang, J P Sun, Y Q Wang, S R Ge, H Zhu. Hydrodynamic lubrication of surfaces with asymmetric microdimple. Tribol Trans 54(4): 607-615 (2011)
Crossref Google Scholar
[22]
J K Schuh, R H Ewoldt. Asymmetric surface textures decrease friction with Newtonian fluids in full film lubricated sliding contact. Tribol Int 97: 490-498 (2016)
Crossref Google Scholar
[23]
Y H Lee, J K Schuh, R H Ewoldt, J T Allison. Enhancing full-film lubrication performance via arbitrary surface texture design. J Mech Des 139(5): 053401 (2017)
Google Scholar
[24]
M Fesanghary, M M Khonsari. Topological and shape optimization of thrust bearings for enhanced load-carrying capacity. Tribol Int 53: 12-21 (2012)
Crossref Google Scholar
[25]
C Shen, M M Khonsari. Numerical optimization of texture shape for parallel surfaces under unidirectional and bidirectional sliding. Tribol Int 82: 1-11 (2015)
Crossref Google Scholar
[26]
C D Lin, Y H Lee, J K Schuh, R H Ewoldt, J T Allison. Efficient optimal surface texture design using linearization. In World Congress of Structural and Multidisciplinary Optimisation, Braunschweig, Germany, 2017: 633-647.
Crossref
[27]
H Zhang, G N Dong, M Hua, K S Chin. Improvement of tribological behaviors by optimizing concave texture shape under reciprocating sliding motion. J Tribol 139(1): 011701 (2017)
Crossref Google Scholar
[28]
W Wang, Y Y He, J Zhao, Y Li, J B Luo. Numerical optimization of the groove texture bottom profile for thrust bearings. Tribol Int 109: 69-77 (2017)
Crossref Google Scholar
[29]
W Wang, Y Y He, J Zhao, J Y Mao, Y T Hu, J B Luo. Optimization of groove texture profile to improve hydrodynamic lubrication performance: Theory and experiments. Friction, in Press, .
Crossref
[30]
R Rahmani, I Mirzaee, A Shirvani, H Shirvani. An analytical approach for analysis and optimisation of slider bearings with infinite width parallel textures. Tribol Int 43(8): 1551-1565 (2010)
Crossref Google Scholar
[31]
R Rahmani, H Rahnejat. Enhanced performance of optimised partially textured load bearing surfaces. Tribol Int 117: 272-282 (2018)
Crossref Google Scholar
[32]
C Y Chen, C S Liu, Y C Li, S Mou. Geometry optimization for asymmetrical herringbone grooves of miniature hydrodynamic journal bearings by using Taguchi technique. Proc Inst Mech Eng, Part J: J Eng Tribol 229(2): 196-206 (2015)
Crossref Google Scholar
[33]
A B Shinde, P M Pawar. Multi-objective optimization of surface textured journal bearing by Taguchi based Grey relational analysis. Tribol Int 114: 349-357 (2017)
Crossref Google Scholar
[34]
M F Qiu, A Delic, B Raeymaekers. The effect of texture shape on the load-carrying capacity of gas-lubricated parallel slider bearings. Tribol Lett 48(3): 315-327 (2012)
Crossref Google Scholar
[35]
M F Qiu, B R Minson, B Raeymaekers. The effect of texture shape on the friction coefficient and stiffness of gas-lubricated parallel slider bearings. Tribol Int 67: 278-288 (2013)
Crossref Google Scholar
[36]
G Fu, A Untaroiu. An optimum design approach for textured thrust bearing with elliptical-shape dimples using computational fluid dynamics and design of experiments including cavitation. J Eng Gas Turbines Power 139(9): 092502 (2017)
Crossref Google Scholar
[37]
A Ramesh, W Akram, S P Mishra, A H Cannon, A A Polycarpou, W P King. Friction characteristics of microtextured surfaces under mixed and hydrodynamic lubrication. Tribol Int 57: 170-176 (2013)
Crossref Google Scholar
[38]
X B Lu, M M Khonsari. An experimental investigation of dimple effect on the Stribeck curve of journal bearings. Tribol Lett 27(2): 169-176 (2007)
Crossref Google Scholar
[39]
M T Johnston, W P King, R H Ewoldt. Shear stress characteristics of microtextured surfaces in gap-controlled hydrodynamic lubrication. Tribol Lett 82: 123-132 (2015)
Crossref Google Scholar
[40]
S Kango, D Singh, R K Sharma. Numerical investigation on the influence of surface texture on the performance of hydrodynamic journal bearing. Meccanica 47(2): 469-482 (2012)
Crossref Google Scholar
Friction
Volume 8 Issue 4,
August 2020
Pages 726-745
DOI: 10.1007/s40544-017-0299-x
Cite this article:
HINGAWE ND, BHORE SP. Multi-objective optimization of the design parameters of texture bottom profiles in a parallel slider. Friction, 2020, 8(4): 726-745. https://doi.org/10.1007/s40544-017-0299-x

830

Views

36

Downloads

15

Crossref

N/A

Web of Science

17

Scopus

3

CSCD

Altmetrics
Received: 11 October 2018
Revised: 19 January 2019
Accepted: 08 May 2019
Published: 16 August 2019
© The author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Return