About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Accelerated Discovery and Insertion of Next Generation Structural Materials
|
| Presentation Title |
Coupled Design and Characterization Tools to Enable Components with Spatially Varying Materials and Properties |
| Author(s) |
John Sharon, Jamie Guest, Kevin J. Hemker, Syed Jalali, Matt Lynch, Mitra Taheri, Yakov Zelickman |
| On-Site Speaker (Planned) |
John Sharon |
| Abstract Scope |
The advent of topology optimization and additive manufacturing has enabled remarkable use of geometric complexity to improve part performance. However, potential remains untapped as components are still mostly designed for a single material. It is desirable to enable parts designed with location-specific properties. This talk will highlight efforts by RTX Technology Research Center, Johns Hopkins University, and Raytheon, to unlock design freedom through spatial variation of material properties and rapid property assessment. A topology optimization-based framework with material as an explicit, continuous variable, enabled by a machine learning based surrogate model connecting properties to composition will be shared. Property data for a spectrum of composition space, not satisfied by existing alloy handbook data is required. Therefore, novel approaches, leveraging additively manufactured coupons, to expeditiously capture tensile, fatigue, and corrosion behavior were also developed. The characterization methods and results, benchmarked against traditional protocols (i.e. ASTM E8), will be reviewed. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Modeling and Simulation, Mechanical Properties |