Wei Hu, Krishna Rajan
This thesis explores data-driven methodologies for metallurgical design aimed at enhancing the performance of high strength low alloy (HSLA) steels. The objective is to develop models that predict the mechanical properties of HSLA steel by utilizing multivariate data. The research employs techniques such as linear regression, artificial neural networks, and recursive partitioning to analyze input variables and their interactions. Through comprehensive data collection and model evaluation, the study identifies critical factors influencing yield strength and ultimate tensile strength of HSLA steels. The results indicate that the integrated modeling approach can significantly improve the predictive accuracy of steel property outcomes. Insights gained from this research have considerable implications for advancing metallurgical design practices and optimizing the material properties of HSLA steels for various industrial applications.
@article{23cd14ed-b8d8-4196-9643-3e6d6ceb1acf,
title={Data-driven metallurgical design for high strength low alloy (HSLA) steel},
author={Wei Hu and Krishna Rajan},
year={2008},
language={English}
}TY - JOUR TI - Data-driven metallurgical design for high strength low alloy (HSLA) steel AU - Wei Hu AU - Krishna Rajan PY - 2008 LA - English ER -
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