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January 24, 2026Advanced Functional Materials0 citations

Controlling Convection in Volumetric Additive Manufacturing for Large Volume Structures at Extreme Throughput

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CDCameron Darkes‐BurkeyYPY. K. ParkGEGözde Aktaş Eken

Key Points

  • The research aims to address the challenge of printing arbitrarily thick sections in all axes using volumetric additive manufacturing.
  • Used index matching fluid as an active cooling source
  • Optimized resin formulation for deeper light penetration
  • Implemented a 4k light engine for higher intensity projections
  • Achieved part volumes of at least 70,153 mm³ at throughputs of approximately 390 mm³/s
  • Produced parts approximately 60% larger and 400% faster than the next largest volumetric additive manufacturing method
  • Demonstrated about 1,740% larger parts and 23% faster speeds than the next fastest method

Abstract

ABSTRACT Volumetric Additive Manufacturing (VAM) offers unparalleled speed in creating arbitrary 3D geometries; primarily due to requiring only one degree of freedom (DoF), rotation. A limitation, however, has been its size scale (∼3 cm), which has been attributed to light absorption. Accordingly, efforts have focused on adding translational DoF's to expose more material volume to this light path. The additional translational DoF increases print times, and still has not yielded thicker parts in all axes. This paper focuses on an important challenge to printing arbitrarily thick sections in all axes, thermal evolution from photopolymerization. In this work, we describe a scientific investigation and engineering solution to this issue, along with improvements to remaining challenges by: (i) using the index matching fluid as an active cooling source, (ii) optimizing the resin for deeper light propagation, and (iii) implementing a 4k light engine and large lens for higher intensity projections. With this system, we were able to print at least 70,153 mm 3 part volumes at throughputs of ∼390 mm 3 s −1 . Our printing system produces parts ∼60% larger and ∼400% faster than the next largest VAM method, and ∼1,740% larger and ∼23% faster than the next fastest method.

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Cite This Study

Darkes‐Burkey et al. (2026) studied this question.

synapsesocial.com/papers/69746090bb9d90c67120a5eahttps://doi.org/10.1002/adfm.202518936
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