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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Diagnosing Mechanical Assembly Discontinuities with LLMs
Author(s) Erick Braham, Andrew Fassler, Nathan Culmer, Juan Cruz Rivera, James Hardin, Nathan Hertlein
On-Site Speaker (Planned) Erick Braham
Abstract Scope Large Language Models (LLMs) have enabled new approaches to solving problems across various domains. In manufacturing, where reliability requirements tend to be tight, trust in LLM performance will be critical. The foundation of that trust relies on understanding the limitations of what LLMs can interpret, reason about, and reliably solve. In this work we investigate the ability of AI assistants to interpret a mechanical assembly, perceive and diagnose failures, and make decisions on how to resolve them. The test battery compares performance when using several different representations of a mechanical assembly including graphs, natural language, and images when increasing the complexity of the challenge presented. Once established, these findings are used to integrate the virtual representation of the problem with a physical robotic system using sensors and visual motion capture feedback.
Proceedings Inclusion? Undecided

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