Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy

The hot deformation behaviors of FGH98 nickel-based powder superalloy were experimentally investi-gated and theoretically analyzed by Arrhenius models and machine learning (ML). Hot compression tests were conducted with a Gleeble-380 0 thermo-mechanical simulation machine on the FGH98 superalloy at strain rates of 0.001-1 s -1 and temperatures of 1025-1175 degrees C. The peak stresses under different defor-mation conditions were analyzed via the Sellars model and an ML-inspired Gaussian process regression (GPR) model. The prediction of the GPR model outperformed that from the Sellars model. In addition, the stress-strain responses were predicted by the GPR model and tested by experimentally measured stress-strain curves. The results indicate that the developed GPR model has great power with wide gen-eralization capability in the prediction of hot deformation behaviors of FGH98 superalloy, as evidenced by the R 2 value higher than 0.99 on the test dataset.(c) 2022 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Automated summary

The hot deformation behaviors of FGH98 nickel-based powder superalloy were investigated experimentally and theoretically. Hot compression tests were performed on the FGH98 superalloy at various temperature and strain rate conditions. The peak stresses under different deformation conditions were analyzed using the Sellars model and a Gaussian process regression (GPR) model based on machine learning. The GPR model showed better prediction performance than the Sellars model. Additionally, the stress-strain responses were predicted and validated using the GPR model and experimental data. The developed GPR model demonstrated high predictive capability for the hot deformation behaviors of FGH98 superalloy, with an R2 value above 0.99 on the test dataset.