About this Abstract |
Meeting |
7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
|
Symposium
|
ICME 2023
|
Presentation Title |
Development of a Computational Framework to Predict Resin Additive Manufacturing for
Experimental Design |
Author(s) |
Joseph Leonor, Evan Jones, Cheng Sun, Gregory J. Wagner |
On-Site Speaker (Planned) |
Joseph Leonor |
Abstract Scope |
Resin-based additive manufacturing has been increasingly used in industries such as optics to produce customized parts efficiently. To achieve printing speeds to be competitive with other manufacturing methods, researchers have developed continuous liquid interface production (CLIP), which uses both an oxygen-permeable membrane to enable continuous printing and a UV projection to parallelize production. CLIP allows significant decrease in production time, but experiments have demonstrated inadequate print quality of the final parts due to non-optimal combinations of print speeds and UV light intensity. To address this problem, we are developing a computational framework to optimize parameter selection for experimental design. First, we couple the polymerization and fluid models to accurately simulate the CLIP printing process and predict the expected part results based on resin monomer concentration. Results from this model will be used to train a deep learning regression model for optimal parameter selection and experimental validation. |
Proceedings Inclusion? |
Planned: Other (describe below) |