|About this Abstract
||Materials Science & Technology 2019
||Data Science for Material Property Interpretation
||Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
||Hyung Kim, Surya R Kalidindi, Xuan Liu, Max Kaplan
|On-Site Speaker (Planned)
Nickel superalloys are excellent material candidates for turbine engine applications where high temperature strength is a necessity. Their impressive mechanical properties are largely a result of the microstructure containing gamma prime precipitates embedded within the gamma matrix. Furthermore, the macroscopic properties are governed by the distributions of these inhomogeneities. To better understand trends in the design and optimization of superalloy material, statistically reliable quantification of the distributions of these inhomogeneities is a critical first step in establishing structure-property relationships. However, common practice involves manual tracing of phase boundaries which is a rather inefficient and non-repeatable approach to microstructure characterization. In the present study, we demonstrate computationally efficient and automated image analysis workflows to eliminate elements of human error and allow high sampling rates to collect statistically representative and repeatable feature quantities. These digital protocols are extendable to other material systems that are polycrystalline and populated by precipitates / secondary phases.
||Definite: At-meeting proceedings