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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title In-Situ Prediction of 3D-Printed Outcomes
Author(s) Jennifer Ruddock, James Hardin
On-Site Speaker (Planned) Jennifer Ruddock
Abstract Scope Direct-Ink-Write 3D printing is a high-mix low-volume manufacturing process used for a broad range of materials and applications. However, with new materials or desired structures, print parameters are typically optimized through a laborious trial-and-error process and analyzed ex-situ. Parameter optimization is especially challenging when a material’s extrusion properties change with time or are sensitive to environmental conditions. We address this challenge by developing a process for in-situ print outcome prediction. Using a convolutional neural net and computer vision tools, an image of a simple test print pattern may be used to predict print performance metrics such as slumping behavior and the presence of voids. We have built a dataset for this purpose, using inks from different materials systems and different proposed test print patterns. The development of an in-situ prediction model could lead to in-situ print optimization, streamlining the parameter tuning process.
Proceedings Inclusion? Undecided

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