Tensile strength prediction of gray cast iron for cylinder head based on microstructure and machine learning
Xiaoyuan Teng, Jianchao Pang, Feng Liu, Chenglu Zou, Shouxin Li, Zhefeng Zhang- Materials Chemistry
- Metals and Alloys
- Physical and Theoretical Chemistry
- Condensed Matter Physics
The ultimate tensile strength (UTS) of gray cast iron (GCI) could be affected by numerous parameters due to its complex microstructures. In order to further understand the UTS of GCI, it is necessary to evaluate the impact of various parameters. In this study, a UTS prediction method based on microstructure features and machine learning (ML) algorithms is proposed. The six regression algorithms, namely, Bayesian Ridge (BR), Linear Regression (LR), Elastic Net Regression (ENR), Support Vector Regression (SVR), Gradient Boosting Regressor (GBR), Random Forest Regressor (RFR), are used to develop the prediction models. The predicted results showed that the GBR has the best prediction performance for the predicted UTS and the error bands within the 5%. The feature importance indicates that matrix hardness (MH) has the greatest effect on the UTS in the ML models.
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