000168504 001__ 168504
000168504 005__ 20260209162330.0
000168504 0247_ $$2doi$$a10.1016/j.ijmecsci.2026.111251
000168504 0248_ $$2sideral$$a147931
000168504 037__ $$aART-2026-147931
000168504 041__ $$aeng
000168504 100__ $$aLecina-Tejero, Óscar
000168504 245__ $$aRegression-guided computational design of auxetic scaffolds for soft tissue applications
000168504 260__ $$c2026
000168504 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168504 5203_ $$aThe 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.
000168504 536__ $$9info:eu-repo/grantAgreement/ES/AEI/RYC2023-042524-I$$9info:eu-repo/grantAgreement/ES/DGA/E46-23R$$9info:eu-repo/grantAgreement/ES/MINECO/PID2023-146072OB-I00$$9info:eu-repo/grantAgreement/ES/MINECO/PID2024-155426OB-I00
000168504 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168504 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168504 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, Jesús$$uUniversidad de Zaragoza
000168504 700__ $$0(orcid)0000-0002-6100-7412$$aCuartero, Jesús$$uUniversidad de Zaragoza
000168504 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, María Ángeles$$uUniversidad de Zaragoza
000168504 700__ $$0(orcid)0000-0002-3784-1140$$aBorau, Carlos$$uUniversidad de Zaragoza
000168504 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000168504 7102_ $$15004$$2530$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Ingen.e Infraestr.Transp.
000168504 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000168504 773__ $$g312 (2026), 111251 [12 pp.]$$pInt. j. mech. sci.$$tINTERNATIONAL JOURNAL OF MECHANICAL SCIENCES$$x0020-7403
000168504 8564_ $$s3336110$$uhttps://zaguan.unizar.es/record/168504/files/texto_completo.pdf$$yVersión publicada
000168504 8564_ $$s2690600$$uhttps://zaguan.unizar.es/record/168504/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168504 909CO $$ooai:zaguan.unizar.es:168504$$particulos$$pdriver
000168504 951__ $$a2026-02-09-14:42:20
000168504 980__ $$aARTICLE