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

Lecina-Tejero, Óscar ; Asín, Jesús (Universidad de Zaragoza) ; Cuartero, Jesús (Universidad de Zaragoza) ; Pérez, María Ángeles (Universidad de Zaragoza) ; Borau, Carlos (Universidad de Zaragoza)

Resumen: The mechanical performance of tissue-engineered scaffolds plays a critical role in their effectiveness for regenerative medicine. While auxetic metamaterials offer tunable mechanical behavior ideal for soft tissues, their design typically relies on inefficient, iterative trial-and-error processes. To address this limitation, this study presents an integrated computational framework for the inverse design of auxetic scaffolds. By combining Finite Element Method (FEM) simulations with regression-based models, we developed accurate predictive models capable of mapping microstructural parameters directly to macroscopic mechanical responses. This data-driven approach allowed for the rigorous optimization of four distinct auxetic architectures to replicate the complex, non-linear anisotropic properties of human skin, achieving strong agreement with literature targets. A primary contribution of this work is the development of a user-friendly software tool that integrates this pipeline. The tool allows users to input target mechanical properties and automatically generates optimized, fabrication-ready designs (including custom MEW G-code), effectively bridging the gap between theoretical metamaterial optimization and practical clinical application. This methodology supports robust, patient-specific scaffold development, significantly advancing the capabilities of soft tissue engineering.

Idioma: Inglés

DOI: 10.1016/j.ijmecsci.2026.111251

Año: 2026

Publicado en: INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 312 (2026), 111251 [12 pp.]
ISSN: 0020-7403

Financiación: info:eu-repo/grantAgreement/ES/AEI/RYC2023-042524-I
Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-23R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/PID2023-146072OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MINECO/PID2024-155426OB-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Área Ingen.e Infraestr.Transp. (Dpto. Ingeniería Mecánica)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)