Multiscale structure-property relationships in additively manufactured load-bearing metallic implants.

Authors

Sangharatna M Ramteke, Anil Kumar, Avishkar Rathod, Diana Berman, Max Marian

Published in

Biomaterials advances. Volume 190. Pages 215127. Aug 25, 2026. Epub Aug 25, 2026.

Abstract

The advancement of additive manufacturing (AM) has transformed the design and fabrication of biomedical implants, enabling complex, patient-specific geometries. However, clinical success depends on coupled mechanical and biological performance emerging from features spanning nano-, micro-, and macroscales. This review critically assesses multi-scale characterization methods, emphasizing how each level contributes to mechanical integrity, tribological reliability, and biological integration. A central premise is that, while AM does not directly resolve nanoscale features through geometric control as current AM resolutions are on the order of tens to hundreds of micrometres, the thermal gradients and cyclic reheating inherent to layer-wise fabrication profoundly influence nanoscale grain structure, phase distributions, and surface characteristics that cascade across length scales to determine implant performance. Furthermore, AM-fabricated surfaces serve as ideal substrates for post-process nanoscale engineering. Rather than treating characterization methods independently, this review integrates them within a consolidated processing-structure-property framework that clarifies how scale-specific features collectively determine clinically relevant outcomes such as stress shielding, wear-induced osteolysis, and long-term durability. By reframing multi-scale characterization as a driver for optimization rather than solely a diagnostic tool, we highlight practical routes to translating analytical insights into improved design strategies and process refinements. Finally, we identify key research needs beyond characterization, including in-situ process monitoring, machine learning-assisted multi-scale data fusion, predictive computational models, standardized bio-relevant testing protocols, and functionally graded implant architectures. These concerted efforts will enable predictive design, standardized evaluation, and accelerated clinical translation of AM bioimplants with enhanced longevity, reliability, and biological integration.

PMID:
42667697
Bibliographic data and abstract were imported from PubMed on 30 Aug 2026.

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