Gradient Boosting Method-based Stacking Fault Energy Prediction for Austenitic Steel

Disclaimer

The results from this tool is based on the gradient boosting model based on 349 collected experiments measured SFE. Details can be found in the manuscript: Xin Wang, Wei Xiong, "Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning." Science and Technology of Advanced Materials, 21:1 (2020) 626-634. . All of the calculated results are used for informational purposes only, the developers may not be held responsible for any decisions based on this tool.

Please Enter Steel Composition

Element name Content (wt.%) Element name Content (wt.%) Element name Content (wt.%)
C (0.00-3.21 wt.%) Cr (0-30 wt.%) Mn (0-32.69 wt.%)
N (0-1 wt.%) Al (0-4.8 wt.%) Mo (0-2.7 wt.%)
Ni (0-31.16 wt.%) Si (0-6.22 wt.%) Temperature (90-600 K)

Model Predicted Results

Calculated SFE (mJ/m2)
Developer:                    Xin Wang, Wei Xiong
Reference:                    Xin Wang, Wei Xiong, "Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning." Science and Technology of Advanced Materials, 21:1 (2020) 626-634.
Acknowledgements:    The authors acknowledge funding from the National Science Foundation under award DMR - 1808082, and from the University of Pittsburgh, through the Central Research Development Fund (CRDF). The authors thank the support of the Thermo-Calc software company providing the software and databases for this research.