Modeling and optimization of dimensional accuracy in FDM-printed bistable PCL stents: A combined empirical and mechanistic approach

Fused deposition modeling offers a promising route for fabricating patient-specific bioresorbable stents, However, achieving high dimensional accuracy for complex non-uniform stent structures remains challenging due to the intricate coupling of multiple process parameters. In this work, the effects of printing temperature, pneumatic pressure, and nozzle moving speed on strut width w of non-uniform bistable stents are study systematically investigates via a full factorial experimental design. Main effect analysis and unreplicated ANOVA reveal that nozzle moving speed dominates the dimensional variation (47.5% contribution, p < 0.001), followed by temperature (27.8%, p < 0.001), while pneumatic pressure shows no significant effect. A response surface model is established with excellent predictive accuracy ( R 2 = 0.946), enabling rapid process optimization. More importantly, physics-informed mechanistic model incorporating melt spreading and crystallization shrinkage is proposed, where polymer viscoelastic behavior is quantified via the Deborah number. The mechanistic model is calibrated by nonlinear regression ( R 2 = 0.831), providing interpretable physical correlations between printing conditions and polymer deposition behavior. The two models serve complementary roles, the response surface model provides precise empirical prediction, while the mechanistic model offers physically interpretable parameters that elucidate the underlying mechanisms. Preliminary in vitro cytocompatibility and degradation tests conducted on representative samples demonstrate favorable laboratory-scale biological performance, without confirming clinical translatability. This work establishes a quantitative process modeling framework for improved dimensional control in FDM manufacturing of bistable vascular stents, providing both empirical predictive capability and physics-grounded guidance for laboratory-scale process control and optimization.