Multi-Objective Optimization of Process Parameters for Dental Resin 3D Printing Using Improved NSGA-II Algorithm

Abstract

This study presents an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing process parameters in dental resin 3D printing. The optimization framework addresses three conflicting objectives: dimensional accuracy, surface roughness, and printing time. Five critical process parameters, including layer thickness, exposure duration, print angle, infill density, and lift speed, are investigated using a design of experiments approach based on Response Surface Methodology. The improved NSGA-II incorporates an adaptive mutation operator and an elite-preservation strategy to enhance convergence and solution diversity. Experimental validation using nine dental model resin types demonstrates that the proposed algorithm achieves a 25.3% improvement in dimensional accuracy compared to default parameter settings. The Pareto-optimal solutions provide dental laboratories with flexible parameter configurations that balance quality requirements and production efficiency. Statistical analysis confirms that layer thickness and exposure duration are the most influential parameters affecting print quality.