Christian Orgeldinger, Tobias Rosnitscheck and Stephan Tremmel
Microtextured surfaces can reduce friction in tribological systems under certain contact conditions. Because it is very time-consuming to determine suitable texture patterns…
Abstract
Purpose
Microtextured surfaces can reduce friction in tribological systems under certain contact conditions. Because it is very time-consuming to determine suitable texture patterns experimentally, numerical approaches to the design of microtextures are increasingly gaining acceptance. The purpose of this paper is to investigate to what extent the selected modeling approach affects optimized texturing.
Design/methodology/approach
Using the cam/tappet contact as an application-oriented example, a simplified 2D and a full 3D model are developed for determining the best possible texturing via a design study. The study explores elongated Gaussian-shaped texture elements for this purpose. The optima of the simplified 2D simulation model and the full 3D model are compared with each other to draw conclusions about the influence of the modeling strategy. The target value here is the solid body friction in contact.
Findings
For the elongated texture elements used, both the simplified 2D model and the full model result in very similar optimal texture patterns. In the selected application, the simplified simulation model can significantly reduce the computational effort without affecting the optimization result.
Originality/value
Depending on the selected use case, the simulation effort required for microtexture optimization can be significantly reduced by comparing different models first. Therefore, an exact physical replica of the real contact is not necessarily the primary goal when it comes to texture selection based on numerical simulations.
Details
Keywords
Rajesh Shah, Blerim Gashi, Vikram Mittal, Andreas Rosenkranz and Shuoran Du
Tribological research is complex and multidisciplinary, with many parameters to consider. As traditional experimentation is time-consuming and expensive due to the complexity of…
Abstract
Purpose
Tribological research is complex and multidisciplinary, with many parameters to consider. As traditional experimentation is time-consuming and expensive due to the complexity of tribological systems, researchers tend to use quantitative and qualitative analysis to monitor critical parameters and material characterization to explain observed dependencies. In this regard, numerical modeling and simulation offers a cost-effective alternative to physical experimentation but must be validated with limited testing. This paper aims to highlight advances in numerical modeling as they relate to the field of tribology.
Design/methodology/approach
This study performed an in-depth literature review for the field of modeling and simulation as it relates to tribology. The authors initially looked at the application of foundational studies (e.g. Stribeck) to understand the gaps in the current knowledge set. The authors then evaluated a number of modern developments related to contact mechanics, surface roughness, tribofilm formation and fluid-film layers. In particular, it looked at key fields driving tribology models including nanoparticle research and prosthetics. The study then sought out to understand the future trends in this research field.
Findings
The field of tribology, numerical modeling has shown to be a powerful tool, which is both time- and cost-effective when compared to standard bench testing. The characterization of tribological systems of interest fundamentally stems from the lubrication regimes designated in the Stribeck curve. The prediction of tribofilm formation, film thickness variation, fluid properties, asperity contact and surface deformation as well as the continuously changing interactions between such parameters is an essential challenge for proper modeling.
Originality/value
This paper highlights the major numerical modeling achievements in various disciplines and discusses their efficacy, assumptions and limitations in tribology research.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0076/