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Surface Topography-Based Classification of Coefficient of Friction in Strip-Drawing Test Using Kohonen
Krzysztof Szwajka1, Tomasz Trzepieciński2, Marek Szewczyk1
1Department of Integrated Design and Tribology Systems, Faculty of Mechanics and Technology, Rzeszów University of Technology, ul. Kwiatkowskiego 4, 37-450 Stalowa Wola, Poland.
Materials (Basel, Switzerland)
|July 12, 2025
Summary
This study introduces an AI method to classify the coefficient of friction (CoF) in sheet metal forming using surface roughness data. The unsupervised approach achieved 98% accuracy, improving quality and reducing defects.
Area of Science:
- Materials Science and Engineering
- Artificial Intelligence and Machine Learning
Background:
- Accurate monitoring of the coefficient of friction (CoF) is crucial in sheet metal forming (SMF) for product quality and process efficiency.
- Traditional CoF monitoring methods struggle to identify force signal features sensitive to friction variations.
- Changes in CoF during SMF are influenced by evolving contact conditions, surface topography, contact loads, and material behavior.
Purpose of the Study:
- To develop a novel approach for classifying the CoF in DP780 steel sheets during strip-drawing tests.
- To apply unsupervised artificial intelligence (AI) techniques to handle unbalanced data for CoF classification.
- To identify disturbances in the friction process by analyzing friction parameters and sheet metal surface roughness.
Main Methods:
- Utilized unsupervised AI, specifically the Kohonen self-organizing map (SOM), for CoF classification.
- Collected surface topography parameters, including the Sq roughness parameter, and friction test parameters.
- Conducted strip-drawing tests under varying lubrication conditions, contact pressures, and sliding speeds.
Main Results:
- Achieved a high classification accuracy of up to 98% for the coefficient of friction (CoF).
- Demonstrated the effectiveness of using the SOM for classifying CoF with unbalanced data.
- Showcased that CoF classification is feasible using only the Sq surface roughness parameter and selected friction test parameters.
Conclusions:
- The proposed unsupervised AI approach effectively classifies the CoF in sheet metal forming processes.
- Surface topography parameters, particularly Sq, combined with friction test data, are reliable indicators for CoF classification.
- This method offers a promising solution for monitoring and controlling friction in SMF, leading to improved product quality and process optimization.
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