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Machine Learning for High-Temperature Steel Properties by saibal1234 is a document available to read on EtoBox.

What is Machine Learning for High-Temperature Steel Properties about?

This study presents a machine learning framework for predicting the high-temperature mechanical behavior of high-strength steels (HSS) using a comprehensive dataset from tensile tests. Various ML models were evaluated, with Gradient Boosting achieving the highest accuracy (R2 > 0.98), demonstrating the effectiveness of data-driven approaches over traditional methods. The findings facilitate safer and more efficient design of structural components exposed to elevated temperatures.

Author

saibal1234

Language

EN