CALPHAD-based Uncertainty Quantification Modeling for HSLA Steel Design

Disclaimer

The results from this tool is based on the gradient boosting model based on the CALPHAD-based property model described in the manuscript: Xin Wang, Wei Xiong, "Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework."npj Nature Computational Materials, 6 (2020) 188. The details of this model are available in the manuscript, especially in the supplementary files. 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.%) Uncertainty ± (wt.%) Element name Content (wt.%) Uncertainty ± (wt.%) Element name Content (wt.%) Uncertainty ± (wt.%)
C (0.02-0.1 wt.%) Cr (0.1-1.1 wt.%) Cu (0.8-1.7 wt.%)
Mn (0.2-2.1 wt.%) Nb (0.01-0.1 wt.%) Mo (0.2-1.2 wt.%)
Ni (2-5 wt.%) Si (0-0.5 wt.%) Al (0.01-0.1 wt.%)
sample size (1-100000) YS requirement (MPa) DBTT requirement (°C) Freezing range requirement (K)

Model Predicted Results

Property Possibility of meeting requirement Property of the nominal composition Mean property of all sampled composition Standard deviation of all sampled composition
Yield strength (MPa)
In Graville diagram zone 1 Not available Not available
Freezing range (K)
DBTT (°C)
Developer:                    Xin Wang, Wei Xiong
Reference:                    Xin Wang, Wei Xiong, "Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework."npj Nature Computational Materials, 6 (2020) 188.
Acknowledgements:   Office of Naval Research (ONR) Additive Manufacturing Alloys for Naval Environments (AMANE) program (Contract no. N00014-17-1-2586) is gratefully acknowledged for performing the current research. APC charges for this article were fully paid by the University Library System, University of Pittsburgh.