About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Gaussian Process Regression Modelling of Superalloy Microstructure |
Author(s) |
Patrick Taylor, Gareth Conduit |
On-Site Speaker (Planned) |
Patrick Taylor |
Abstract Scope |
Gaussian process regression is used to model the phase compositions of nickel-base superalloys. The method makes use of a physically informed kernel and a novel Bayesian approach to ensuring predictions are consistent when summed across phases. The GPR model delivers good predictions for laboratory and commercial superalloys with R2>0.8 for all but three components and R2=0.924 for the γ' phase fraction. It also outperforms CALPHAD for predictions on four benchmark SX-series superalloys. Furthermore, unlike CALPHAD the GPR model quantifies uncertainty in predictions, can be retrained as new data becomes available, and can be used to identify outliers in the initial training dataset. |
Proceedings Inclusion? |
Undecided |