ArticleMaterials (Basel, Switzerland)2026
Modeling the Friction Behavior of Low-Carbon Steel Sheets Using Various Machine Learning Algorithms Based on Strip Drawing Test Data.
Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The application of machine learning (ML) methods enables the modeling of sheet metal friction phenomena based on experimental data, allowing for the prediction of the coefficient of friction (CoF) under various operating conditions. The aim of this article is to compare the predictive capability of a wide range of ML algorithms trained on the results of the strip drawing test. The variable parameters in the strip drawing test were sheet orientation, load, sample orientation relative to the sheet rolling direction, and the drawing quality of the low-carbon steel sheet metal. Based on the coefficient of determination (R
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