About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
2026 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-33: Agentic Additive Manufacturing Alloy Evaluation |
| Author(s) |
Peter Pak, Achuth Chandrasekhar, Amir Barati Farimani |
| On-Site Speaker (Planned) |
Achuth Chandrasekhar |
| Abstract Scope |
Agentic systems enable the intelligent use of research tooling, augmenting a researcher’s ability to investigate and propose novel solutions to existing problems. Within Additive Manufacturing (AM), alloy selection and evaluation remains a complex challenge, often requiring expertise in the various domains of materials science, thermodynamic simulations, and experimental analysis. Large Language Model (LLM) enabled agents can facilitate this endeavor by utilizing their extensive knowledge base to dispatch tool calls via Model Context Protocol (MCP) to perform actions such as thermophysical property diagram calculations and lack of fusion process map generation. In addition, the multi-agent system can effectively reason through complex user prompts and provide analysis on the lack of fusion process window of common alloys such as SS316L and IN718 along with proposed composition variants of known alloys. These agents can dynamically adjust their task trajectory to the outcomes of tool call results, effectively enabling autonomous decision-making in practical environments. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Additive Manufacturing, Computational Materials Science & Engineering, Machine Learning |