Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning

Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning

Evaluating the dynamic impact properties of automotive steels is critical for structural design and material selection, but physical testing methods result in high costs and long lead times. In this study, a dataset was constructed by collecting data from high-speed tensile experiments on 65 automot...

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Bibliographic Details
Main Authors: Houchao Wang, Fengyao Lv, Zhenfei Zhan, Hailong Zhao, Jie Li, Kangte Yang
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:World Electric Vehicle Journal
Subjects:
automotive steel
machine learning
mechanical property
ensemble model
Online Access:https://www.mdpi.com/2032-6653/16/3/123
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