Regression-guided computational design of auxetic scaffolds for soft tissue applications

Abstract

The mechanical performance of tissue-engineered scaffolds plays a critical role in their effectiveness for regenerative medicine applications. Auxetic metamaterials, characterized by a negative Poissons ratio, offer enhanced conformability and tunable mechanical behavior, making them promising candidates for scaffold design. This study presents a computational framework combining finite element method (FEM) simulations with regression-based predictive modeling to optimize auxetic scaffold architectures. A design of experiments (DOE) strategy enabled the training of FEM-accurate regression models capable of predicting mechanical responses from scaffold microstructural parameters. Sensitivity analysis guided the development of robust optimization strategies for identifying optimal geometries. Validation of the predictive framework was performed using experimentally derived, skin-representative mechanical properties from published literature, demonstrating strong agreement with FEM results. To facilitate broader use, we developed a software tool integrating this pipeline. It includes a manual mode for direct input of design and geometry parameters, and a predictive mode that returns optimized scaffold designs based on target properties. This integrated methodology supports robust, customizable scaffold design, advancing patient-specific approaches in soft tissue engineering.