低炭素鋼/工具鋼接触における摩擦現象解析のためのSHAP機械学習アプローチとANNの適用

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:摩擦モデリングへのML適用の事例として参考になる。

🏢実務担当者:潤滑剤選定や工程設計でのML活用の可能性を示す。

The friction analysis in sheet metal forming is difficult due to the complex influence of numerous friction process parameters on the coefficient of friction (COF). Artificial intelligence tools are one of the methods supporting the analysis of complex models. This article presents the application of Additive ExPlanation (SHAP) Machine Learning (ML) methods and artificial neural networks to recognize important friction process parameters based on strip drawing test results for low-carbon steel/tool ​​steel contacts. Friction tests were conducted under lubrication conditions with selected vegetable oils, an alternative to petroleum-based lubricants consistent with sustainable manufacturing. Experimental results showed that rape-seed oil demonstrated the best lubrication performance among the analysed oils with higher and lower viscosity categories. Cumulative SHAP plots and Shapley values indicated that the average surface roughness of counter samples demonstrated the most significant relative importance (35.02%) of a given variable on the COF. The second most significant factor affecting COF was contact pressure, with relative importance of 23.98%-29.33%, depending on the ML algorithm used. The Gradient Boosting ML algorithm demonstrated the best predictive ability over all other tested algorithms (Decision Tree, linear regression, Random Forest, Ridge, and Lasso). Based on the obtained results, it can be concluded that ML models enable the prediction of the COF under various friction conditions, allowing the evaluation of new lubricant formulations already at the sheet metal forming process design stage, thereby reducing the number of laboratory tests.

• openalex https://doi.org/10.30678/fjt.173168first seen 2026-08-23 04:47:12