Multimaterial vat photopolymerization: Computational optimization of slicing workflow for complex tissue geometries

Multimaterial printing using digital light processing (DLP) has progressed from a niche laboratory method to a scalable technology capable of fabricating complex and functional tissue constructs. However, current multimaterial DLP workflows face significant limitations. Material changes typically require repeated washing and reloading steps, which increase print time, raise the risk of cross-contamination or layer misalignment, and ultimately constrain scaffold design complexity and biological relevance. To address these challenges, we present a computational pipeline that significantly improves the efficiency, precision, and usability of DLP for multimaterial bioprinting. Our system includes three key innovations: (i) a high-resolution segmentation and material-labeling method using computer graphics techniques for accurate material assignment in Standard Tessellation Language (STL) models; (ii) a computer vision-based algorithm for real-time detection and correction of material interference or contamination; and (iii) a GPU-accelerated layer sequencing method that supports rapid, precise material switching within single-layer projections. Experimental validation demonstrates improved print fidelity, reduced processing time, and higher material resolution. We further showcase the practical utility of our system by bioprinting a multimaterial tissue construct composed of a poly(ethylene glycol) diacrylate-based scaffold integrated with a gelatin methacryloyl-based cell-laden microenvironment. This work represents a significant step toward enabling scalable, high-resolution, and biologically functional scaffold fabrication for advanced tissue engineering applications.