Tomographic volumetric additive manufacturing (VAM) is an innovative 3D printing technology that polymerizes an entire volume of photopolymer resin simultaneously. VAM enables an increased printing speed and higher output compared with traditional stereolithography, layer-by-layer printing. We explore the operational implications of adopting VAM in an intelligent manufacturing context by considering process planning and production control issues exacerbated by the time bottlenecks introduced in downstream post-processing stages. Discrete Event Simulation (DES) was used to model production flow for two conceptual scenarios: a small-batch low-mix production environment and a high-mix variable-batch production environment. We simulated production, analyzed bottlenecks and tested intervention strategies that may be implemented: (1) increasing the availability of post-processing equipment, (2) modifying the number of available printers and (3) implementing improved workforce scheduling to reassign skilled operators during downtime of certain machines to reduce waiting time. VAM can speed up the creation of the primary part, but post-processing steps such as curing, washing and finishing the produced part might nullify those savings. Through the intervention methods we studied, the overall system utilization rate can be increased. VAM can achieve higher throughput rates in intelligent manufacturing settings only when it is incorporated into intelligent planning systems with high-speed post-processing. We provide some operational considerations in scaling up the VAM manufacturing capability, specifically focusing on planning challenges and gaps in adoption within manufacturing contexts. In this context, we find that coupling data-driven simulation methods with process planning algorithms may further improve workflow in smart manufacturing environments.
León-Becerra et al. (Sat,) studied this question.