About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing, Directed Energy Deposition of Metals: Processing – Microstructure – Mechanical Property Relationships
|
| Presentation Title |
Towards Statistical Microstructure Quantification to Guide the Directed Energy Deposition Process |
| Author(s) |
Bhagyashree Chandrakant Prabhune, Patxi Fernandez-Zelaia, Gyan Shankar, Brian Jordan, Michael Kirka, Yousub Lee |
| On-Site Speaker (Planned) |
Bhagyashree Chandrakant Prabhune |
| Abstract Scope |
A significant challenge in microstructural control is the absence of a comprehensive methodology to quantify microstructural differences. Up to date, microstructures have been compared qualitatively or using a limited set of descriptors using pole figures, average grain size, and orientation distributions. Qualitative methods introduce operator-bias and often fail to capture the complexity of microstructural features. This work introduces a framework to rigorously quantify microstructure differences considering both texture and morphology. Microstructure is represented quantitatively using methods such as angular chord length distribution and generalized spherical harmonics basis with 2-point spatial statistics. These statistical representations are then used to compute four “dissimilarity scores” that offer quantitative measures to compare microstructures, considering both texture and morphology. Their application is demonstrated for automated calibration of simulation parameters. The proposed statistical framework enables adaptive microstructure control by linking process settings to microstructural features. |