AI-Optimized Lattice Structures for Biomechanics Scaffold Design

TY - JOUR

T1 - AI-Optimized Lattice Structures for Biomechanics Scaffold Design

AU - Omigbodun, Francis T.

AU - Oladapo, Bankole I.

N1 - Publisher Copyright: © 2025 by the authors.

PY - 2025/2/1

Y1 - 2025/2/1

N2 - This research paper explores the development of AI-optimized lattice structures for biomechanics scaffold design, aiming to enhance bone implant functionality by utilizing advanced human–AI systems. The primary objective is to create scaffold structures that mimic the mechanical properties of natural bone and improve bioactivity and biocompatibility, adapting to patient-specific needs. We employed polylactic acid (PLA), calcium hydroxyapatite (cHAP), and reduced graphene oxide (rGO) as base materials, leveraging their synergistic properties. The scaffolds were intricately designed using nTopology software (nTop 5.12) and fabricated via 3D printing techniques, optimizing for biomechanical load-bearing and cellular integration. The study’s findings highlight a notable enhancement in the mechanical properties of the scaffolds, with the Gyroid lattice design demonstrating a 20% higher energy-absorption capacity than traditional designs. Thermal and chemical analysis revealed a 15% increase in the thermal stability of the composites, enhancing their resilience under physiological conditions. However, the research identified minor inconsistencies in filament diameter during 3D printing, which could affect scaffold uniformity. These findings underscore the potential of integrating AI-driven design with advanced material composites in revolutionizing orthopedic implant technologies.

AB - This research paper explores the development of AI-optimized lattice structures for biomechanics scaffold design, aiming to enhance bone implant functionality by utilizing advanced human–AI systems. The primary objective is to create scaffold structures that mimic the mechanical properties of natural bone and improve bioactivity and biocompatibility, adapting to patient-specific needs. We employed polylactic acid (PLA), calcium hydroxyapatite (cHAP), and reduced graphene oxide (rGO) as base materials, leveraging their synergistic properties. The scaffolds were intricately designed using nTopology software (nTop 5.12) and fabricated via 3D printing techniques, optimizing for biomechanical load-bearing and cellular integration. The study’s findings highlight a notable enhancement in the mechanical properties of the scaffolds, with the Gyroid lattice design demonstrating a 20% higher energy-absorption capacity than traditional designs. Thermal and chemical analysis revealed a 15% increase in the thermal stability of the composites, enhancing their resilience under physiological conditions. However, the research identified minor inconsistencies in filament diameter during 3D printing, which could affect scaffold uniformity. These findings underscore the potential of integrating AI-driven design with advanced material composites in revolutionizing orthopedic implant technologies.

KW - 3D printing technology

KW - algorithmic scaffold modeling

KW - computational design

KW - data-driven material

KW - human–AI systems

KW - machine learning optimization

UR - https://www.scopus.com/pages/publications/85218897500

U2 - 10.3390/biomimetics10020088

DO - 10.3390/biomimetics10020088

M3 - Article

C2 - 39997111

AN - SCOPUS:85218897500

SN - 2313-7673

VL - 10

JO - Biomimetics

JF - Biomimetics

IS - 2

M1 - 88

ER -