Design–performance relationships in Ti-6Al-4V lattice metamaterials: in-vivo osseointegration and ex-vivo biomechanical pull-out assessment

  • Miguel Araya*
  • , Rafael Vindas
  • , Jos Malda
  • , Antti Järvenpää
  • , Eva Stronkman
  • , Harold Brommer
  • , Mylène de Ruijter
  • , Roberto Estrada
  • , Teodolito Guillén
  • *Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This study investigated the influence of lattice topology, relative density, and surface condition on the osseointegration and biomechanical fixation of porous Ti-6Al-4 V implants in a large-animal model. Eight horses received cylindrical gyroid or stochastic scaffolds (0.2 and 0.3 relative density; as-built and chemically etched) bilaterally in the tuber coxae for six months. Postmortem analyses included radiography, SEM, histology, and axial pull-out testing. Gyroid lattices at a relative density of 0.2 achieved the most favorable balance of fixation strength (∼2.0 kN), displacement (∼5 mm), deformation energy (>9 J), and bone ingrowth, indicating progressive rather than abrupt failure at the interface. Increasing relative density to 0.3 enhanced stiffness but reduced bone infiltration and fixation, reflecting a porosity–strength trade-off. Stochastic lattices reached similar mean fixation strength but showed higher variability due to heterogeneous bone contact. Chemical etching enhanced displacement and energy absorption in gyroid 0.2 lattices but was unable to compensate for poor infiltration in high-density stochastic scaffolds. Overall, lattice topology and relative density governed fixation outcomes more strongly than surface state, underscoring the translational potential of TPMS-based titanium lattices for future load-bearing orthopedic applications.

Original languageEnglish
Article number115327
Number of pages12
JournalMaterials and Design
Volume261
DOIs
Publication statusPublished - Jan 2026

Keywords

  • Additive manufacturing
  • Architected lattices
  • Osseointegration
  • Pull-out test
  • Stochastic Voronoi
  • TPMS gyroid

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