Abstract
Vat photopolymerization (VPP) is widely known for its exceptional printing accuracy, but effectively controlling stitching errors to achieve high-precision, large-scale printing remains a significant challenge. This study introduces a novel compensation method, termed "Spatial-Pixel Integration Compensation" (SPIC), to address this key obstacle. SPIC combines Spatial Motion Compensation to initially control stitching errors between adjacent sub-images to be within 100 µm and Pixel Compensation for precise adjustments, further reducing stitching errors. Integrating SPIC with our high-precision printing system resulted in a remarkable reduction of stitching errors by a factor exceeding 40. Crucially, this method minimizes the stitching error between any two sub-images to be less than one pixel point, specifically below 4 µm. Notably, SPIC is the first method specifically focused on effectively controlling and reducing stitching errors to achieve large-scale printing while maintaining high precision. To validate the practical applicability of the SPIC method, we used SPIC to print both Zig-Zag and herringbone structures (HBS) and achieved high-precision large-scale manufacturing with dimensions of 44 mm × 33 mm. In the HBS structure, the minimum feature size was 14.2 µm, and the printing error was less than 1 µm. These printed structures were used as molds to fabricate proof-of-concept polydimethylsiloxane (PDMS) microfluidic chips, demonstrating the successful mixing of rhodamine B and ethanol within the microfluidic channels. Furthermore, SPIC was extended to the printing of complex three-dimensional (3D) Triple-Periodic Minimal Surface (TPMS) structures, successfully producing TPMS-Gyroid and TPMS-Diamond structures with a minimum feature size of 20 µm and a printing error below 1.5 µm. These results demonstrate that the SPIC algorithm effectively enables high-precision, large-scale printing. This work significantly advances the application of VPP technology in fields requiring complex, high-precision, and large-scale 3D structures and provides valuable insights for future research in high-precision and large-scale printing.
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