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


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)