Automated Exposure Control Breakthrough Accelerates Volumetric Additive Manufacturing
A research collaboration between the National Research Council Canada and the University of Victoria has yielded a promising breakthrough in the field of volumetric additive manufacturing (VAM). The team has developed a fully automatic exposure control system for tomographic VAM, marking a significant leap forward in precision and efficiency for this cutting-edge 3D printing method.
In tomographic VAM, objects are fabricated volumetrically by projecting a series of computed light patterns through a rotating resin vat. Rather than curing one thin layer at a time, the resin solidifies simultaneously throughout the entire geometry, enabling smooth surfaces and rapid builds. However, variations in resin absorption, light intensity fall-off, and accumulation of photoinitiator byproducts can lead to over- or under-exposure in different regions—compromising dimensional accuracy and repeatability.
The NRC–UVic system integrates real-time optical sensors and a closed-loop control algorithm that continuously monitors resin darkening and dose accumulation. Before each projection, the system adjusts intensity and exposure duration locally, compensating for drift and ensuring each voxel receives exactly the dosage required. The result is a ‘set-and-forget2 workflow: once the print begins, no manual calibration or stage-wise intervention is needed.
The newly developed automatic exposure control system addresses this challenge head-on. By dynamically adjusting light exposure based on in-situ feedback and advanced modeling, the system enables hands-free operation with optimized accuracy and consistency. According to the team’s findings, published as a preprint on arXiv, the system delivers feature resolution comparable to or exceeding that of commercial stereolithography (SLA) and digital light processing (DLP) printers, while printing up to ten times faster.
This innovation could have wide-ranging implications for industrial and biomedical applications where speed and precision are paramount. The hands-free nature of the system not only streamlines the VAM workflow, but also lays the groundwork for automated, scalable additive manufacturing environments.
Although the results are preliminary and await peer review, the early data suggest a transformative step toward real-time, high-throughput 3D printing with metrology-grade fidelity. As additive manufacturing continues to evolve toward greater automation and integration within smart factories, such innovations are poised to play a pivotal role in redefining digital fabrication standards.
