Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning

Abstract

The ultimate tensile strength (UTS) of gray cast iron (GCI) can be affected by numerous parameters due to its complex microstructures. To further understand the UTS of GCI, it is necessary to evaluate the impact of various parameters. Herein, a UTS prediction method based on microstructure features and machine learning (ML) algorithms is proposed. The six regression algorithms, namely, Bayesian Ridge, Linear Regression, Elastic Net Regression, Support Vector Regression, Gradient Boosting Regressor (GBR), and Random Forest Regressor are used to develop the prediction models. The predicted results show that the GBR has the best prediction performance for the predicted UTS and the error bands within 5%. The feature importance indicates that matrix hardness has the greatest effect on the UTS in the ML models.