Abstract
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, Tbreak, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black-box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low- and high-temperature regimes, is presented. The results presented extend previous work on high-entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature-dependent strength behavior of superalloys as well as HEAs.
| Original language | English |
|---|---|
| Article number | 2201903 |
| Journal | Advanced Engineering Materials |
| Volume | 25 |
| Issue number | 14 |
| DOIs | |
| State | Published - Jul 2023 |
| Externally published | Yes |
Funding
X.F. and P.K.L. very much appreciate the support of the U.S. Army Research Office Project (W911NF‐13‐1‐0438 and W911NF‐19‐2‐0049) with the program managers, Drs. M. P. Bakas, S. N. Mathaudhu, and D. M. Stepp. P.K.L. thanks the support from the National Science Foundation (DMR‐1611180, 1809640, and 2226508) with the program directors, Drs. J. Madison, J. Yang, G. Shiflet, and D. Farkas. X.F. and P.K.L. also appreciate the support from the Bunch Fellowship. X.F. and P.K.L. would like to acknowledge funding from the State of Tennessee and Tennessee Higher Education Commission (THEC) through their support of the Center for Materials Processing (CMP). BS very much appreciates the support from the National Science Foundation (IIP‐1447395 and IIP‐1632408), with the program directors, Drs. G. Larsen and R. Mehta, from the U.S. Air Force (FA864921P0754), with J. Evans as the program manager, and from the U.S. Navy (N6833521C0420), with Drs. D. Shifler and J. Wolk as the program managers. The authors also want to thank Dr. Graham Tewksbury for offering insightful comments on strengthening mechanisms in superalloys during an October 21, 2021, material science graduate seminar presentation, conducted by BS at Oregon State University. The authors similarly want to thank Dr. Chanho Lee for bringing precursors to Figure S1–S3 from the Supporting Information, to their attention. X.F. and P.K.L. very much appreciate the support of the U.S. Army Research Office Project (W911NF-13-1-0438 and W911NF-19-2-0049) with the program managers, Drs. M. P. Bakas, S. N. Mathaudhu, and D. M. Stepp. P.K.L. thanks the support from the National Science Foundation (DMR-1611180, 1809640, and 2226508) with the program directors, Drs. J. Madison, J. Yang, G. Shiflet, and D. Farkas. X.F. and P.K.L. also appreciate the support from the Bunch Fellowship. X.F. and P.K.L. would like to acknowledge funding from the State of Tennessee and Tennessee Higher Education Commission (THEC) through their support of the Center for Materials Processing (CMP). BS very much appreciates the support from the National Science Foundation (IIP-1447395 and IIP-1632408), with the program directors, Drs. G. Larsen and R. Mehta, from the U.S. Air Force (FA864921P0754), with J. Evans as the program manager, and from the U.S. Navy (N6833521C0420), with Drs. D. Shifler and J. Wolk as the program managers. The authors also want to thank Dr. Graham Tewksbury for offering insightful comments on strengthening mechanisms in superalloys during an October 21, 2021, material science graduate seminar presentation, conducted by BS at Oregon State University. The authors similarly want to thank Dr. Chanho Lee for bringing precursors to Figure S1–S3 from the Supporting Information, to their attention.
| Funders | Funder number |
|---|---|
| State of Tennessee and Tennessee Higher Education Commission | |
| National Science Foundation | 2226508, DMR‐1611180, 1809640 |
| National Science Foundation | |
| Army Research Office | W911NF‐19‐2‐0049, W911NF‐13‐1‐0438 |
| Army Research Office | |
| U.S. Air Force | FA864921P0754 |
| U.S. Air Force | |
| Oregon State University | |
| U.S. Navy | N6833521C0420 |
| U.S. Navy | |
| Tennessee Higher Education Commission | IIP‐1447395, IIP‐1632408 |
| Tennessee Higher Education Commission |
Keywords
- high temperature
- machine learning
- physics-based modeling
- structural materials
- superalloys
- yield strength