Spatiotemporal Projection‐Based Additive Manufacturing: A Data‐Driven Image Planning Method for Subpixel Shifting in a Split Second

Abstract

Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.