Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model.

ABSTRACT: Hardenability is one of the most basic criteria influencing the formulation of the heat treatment process and steel selection. Therefore, it is of great engineering value to calculate the hardenability curves rapidly and accurately without resorting to any laborious and costly experiments. However, generating a high-precision computational model for steels with different hardenability remains a challenge. In this study, a combined machine learning (CML) model including k-nearest neighbor and random forest is established to predict the hardenability curves of non-boron steels solely on the basis of chemical compositions: (i) random forest is first applied to classify steel into low- and high-hardenability steel; (ii) k-nearest neighbor and random forest models are then developed to predict

SUBMITTER: Geng X 

PROVIDER: S-EPMC9103254 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature