Machine learning for projection multi-photon 3D printing

Jason Johnson; Liang Pan; Guang Lin; Xianfan Xu

doi:10.1117/12.3001830

13 March 2024 Machine learning for projection multi-photon 3D printing

Jason Johnson, Liang Pan, Guang Lin, Xianfan Xu

Proceedings Volume PC12876, Laser 3D Manufacturing XI; PC128760E (2024) https://doi.org/10.1117/12.3001830
Event: SPIE LASE, 2024, San Francisco, California, United States

Abstract

Projection multi-photon lithography, like all additive manufacturing techniques, requires optimization of process parameters to achieve geometrically accurate results. Determining these optimal parameters is often time-consuming. Machine learning can be used to avoid the need for experimentation by predicting optimal process parameters. A data collection scheme is presented where image analysis on optical microscope images is used to measure the dimensions of individual 2D layers printed with the projection multi-photon printing process for a range of process parameters. The dimensional accuracy of these 2D shapes is then used to train a Gaussian process regression model for forward prediction.

Conference Presentation

Citation Download Citation

Jason Johnson, Liang Pan, Guang Lin, and Xianfan Xu "Machine learning for projection multi-photon 3D printing", Proc. SPIE PC12876, Laser 3D Manufacturing XI, PC128760E (13 March 2024); https://doi.org/10.1117/12.3001830

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