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Evaluating Elastic-Plastic Wavy and Spherical Asperity-Based Statistical and Multi-Scale Rough Surface Contact Models
Nolan Ryan Chu1, Robert L Jackson1, Xianzhang Wang2
1Mechanical Engineering Department, Auburn University, Auburn, AL 36849, USA.
Materials (Basel, Switzerland)
|July 24, 2021
Summary
This study provides guidelines for selecting the best elastic-plastic rough surface contact models. By comparing mathematical and deterministic approaches, it identifies optimal methods for friction and contact resistance applications.
Area of Science:
- Tribology
- Materials Science
- Mechanical Engineering
Background:
- Elastic-plastic rough surface contact is crucial for understanding friction and contact resistance.
- Existing models, while useful, have limitations in accuracy or computational efficiency.
- Deterministic models offer accuracy but are computationally intensive; abbreviated models sacrifice some accuracy for speed.
Purpose of the Study:
- To establish guidelines for selecting appropriate models for elastic-plastic rough surface contact problems.
- To compare the performance of mathematical and deterministic approaches for surface contact analysis.
- To evaluate the suitability of different asperity representations, including sinusoidal geometries.
Main Methods:
- Applied mathematical and deterministic models to two reference surfaces in contact with a rigid flat.
- Analyzed discrepancies and similarities between model predictions.
- Focused on elastic-plastic contact scenarios to assess model performance.
Main Results:
- Identified specific scenarios where deterministic models outperform statistical or abbreviated models.
- Highlighted the impact of asperity shape assumptions (e.g., spherical vs. sinusoidal) on contact predictions.
- Quantified the trade-offs between computational cost and accuracy for different modeling approaches.
Conclusions:
- No single model is universally superior for all rough surface contact problems.
- Guidelines were developed based on comparative analysis to aid model selection.
- The findings support more informed choices in modeling friction and contact resistance.
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