| About this Abstract |
| Meeting |
TMS Specialty Congress 2024
|
| Symposium
|
Accelerating Discovery for Mechanical Behavior of Materials 2024
|
| Presentation Title |
Determination of Intrinsic Mechanical Properties of Polycrystalline Nickel-based Superalloy Using Spherical Indentation and Bayesian Inference |
| Author(s) |
Hyung Kim, Michael Buzzy, Camilla Johnson, Surya R Kalidindi |
| On-Site Speaker (Planned) |
Hyung Kim |
| Abstract Scope |
Extracting the intrinsic mechanical properties of polycrystalline materials poses a significant challenge in materials science due to the absence of straightforward mechanical testing methods. Furthermore, when such materials contain second-phase precipitates, conventional mechanical tests become unfeasible. In this study, we introduce a novel approach that utilizes spherical indentation coupled with Bayesian inference techniques, specifically Gaussian Process Regression (GPR) and Markov Chain Monte Carlo (MCMC) sampling. Through this methodology, we demonstrate a solution to overcome both challenges on a polycrystalline nickel-based superalloy which contains a relatively high volume fraction of second-phase precipitates. Our results indicate that spherical indentation, in conjunction with advanced statistical tools, offers a promising avenue for accurately characterizing the intrinsic mechanical properties of complex polycrystalline materials. This research contributes to the advancement of materials characterization techniques, enhancing our understanding of critical mechanical properties in high-performance materials like nickel-based superalloys. |
| Proceedings Inclusion? |
Definite: Other |