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

Abstract

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.

Keywords

3D printing protocol / Multimaterial digital light processing printer / Process planning algorithm / Slicing

Funding

Alejandro González-Santos was supported by a predoctoral research grant from the Xunta de Galicia (Consellería de Cultura, Educación, Formación Profesional e Universidades). This grant was co-funded by the European Union (FSE+ Galicia 2021-2027). Daniel Nieto was funded by the European Research Council Consolidator Grant (101125172 HOT-BIOPRINTING- HE-ERC-2023COG) and was supported by the Oportunius Programme (Xunta de Galicia) since 2024.

Conflict of Interest

The authors declare that they have no conflict of interest.

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