About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Symposium
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2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Presentation Title |
Cross-Printer Reproducibility Modeling Using a Deep Auto-Encoded Multi-Domain Gaussian Process |
| Author(s) |
Logan Heck, Krystel K. Castillo-Villar, Adel Alaeddini |
| On-Site Speaker (Planned) |
Logan Heck |
| Abstract Scope |
Additive manufacturing (AM) enables complex geometries but suffers from inconsistent dimensional accuracy across identical printer units, limiting industrial scalability. This study proposes a Deep Auto-Encoded Multi-Domain Gaussian Process (DAE-MDGP) framework to predict dimensional deviations in stereolithography (SLA) with emphasis on cross-printer reproducibility. The approach integrates an autoencoder with Gaussian Process regression to learn a data-driven, nonstationary kernel capturing nonlinear interactions among process parameters and machine-specific behavior. Unlike fixed-kernel or single-domain methods, the learned kernel models similarity across multiple printers. The framework is evaluated using thirty prints of a complex part across three SLA printers under varied parameters. Results show DAE-MDGP achieves the lowest prediction error across multiple metrics, outperforming baseline and conventional models. These findings demonstrate improved predictive consistency across printers, enabling better parameter selection and more reliable AM production. |
| Proceedings Inclusion? |
Undecided |