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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Adaption to Uncontrolled Variables in Additive Manufacturing
Author(s) Andrew Fassler, Erick Braham, Jennifer Ruddock, James Hardin
On-Site Speaker (Planned) James Hardin
Abstract Scope Unmonitored and uncontrolled variables, such as ambient temperature or curing kinetics, can exert significant influence on the performance and output quality of additive manufacturing systems. This inherent variability can compromise the efficacy of machine learning approaches, as the system's output quality may appear to diminish or drift over time with these unmonitored variables. This would render earlier datasets less representative of the current operational state. To mitigate this issue, we explored applying a modulated uncertainty methodology to Gaussian process regression. These machine learning models were applied to a direct ink write printing process to facilitate the optimization of arched and spanning structures fabricated across a 4 mm gap. Closed-loop experimentation and optimization were realized through the utilization of image processing techniques to assess the printed structures against predefined geometric targets. Testing encompassed real-world experiments exploring both discrete and continuous shifts in system output, including printing curing material with changing rheology.
Proceedings Inclusion? Undecided

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