Differentiable Modeling of Material Spreading in Inkjet Printing for Appearance Prediction
Session3D Printing, Manufacturing
DescriptionInkjet 3D printers produce solid shapes using very small voxels made of polymeric materials. While state-of-the-art methods for predicting the appearance of inkjet-printed objects assume a perfect grid, the printed patterns have an irregular material distribution due to complex spreading behavior. This irregularity leads to imprecise appearance predictive tools. Here, we
propose a fully differentiable method that models the material spreading behavior of inkjet printing. We use a differentiable simulator along with a differentiable volume renderer. Then, using an image of only one printed calibration pattern, we obtain a generalizable material spreading transformation that can be applied to an input, nominal grid of materials and produce the effective material grid. By taking into account the dynamics of the printing process, our method significantly outperforms state of the art 3D printing appearance prediction models.
propose a fully differentiable method that models the material spreading behavior of inkjet printing. We use a differentiable simulator along with a differentiable volume renderer. Then, using an image of only one printed calibration pattern, we obtain a generalizable material spreading transformation that can be applied to an input, nominal grid of materials and produce the effective material grid. By taking into account the dynamics of the printing process, our method significantly outperforms state of the art 3D printing appearance prediction models.
Event Type
Technical Papers
TimeThursday, 5 December 202411:13am - 11:27am JST
LocationHall B5 (1), B Block, Level 5
