An Improved Height Difference Based Model of Height Profile for Drop-on-Demand 3D Printing With UV Curable Ink
Yumeng Wu, George Chiu
TL;DR
The paper addresses accurate height profile prediction for drop-on-demand UV-curable ink 3D printing, especially for 2D patterns. It extends prior height-profile models by deriving height from volume and area via $h = c \frac{v}{a}$ and by modeling $\Delta v$ and $\Delta a$ as piecewise functions of height differences within a 3×3 ROI, with empirically learned coefficients. The empirical determination of $m_v^+$, $m_v^-$, $m_a^+$, and $m_a^-$ from multiple patterns (with bootstrapping) demonstrates RMS errors consistently below 7% across six 2D patterns and shows improvement over graph-based models while matching prior 1D results. The approach promises real-time applicability for process control in UV-curable ink printing by providing accurate, volume-conserving height predictions for complex patterns.
Abstract
This paper proposes an improved height profile model for drop-on-demand 3D printing with UV curable ink. It is extended from a previously validated model and computes height profile indirectly from volume and area propagation to ensure volume conservation. To accommodate 2D patterns using multiple passes, volume change and area change within region of interest are modeled as a piecewise function of height difference before drop deposition. Model coefficients are experimentally obtained and validated with bootstrapping of experimental samples. Six different drop patterns are experimentally validated. The RMS height profile errors for 2D patterns from the proposed model are consistently smaller than existing models from literature and are on the same level as 1D patterns reported in our previous publication.
