Data-Driven Design and Additive Manufacturing of Patient-Specific Lattice Titanium Scaffolds for Mandibular Bone Reconstruction
Beisekenov N. Azamatov B. Sadenova M. Dogadkin D. Kaliyev D. Rudenko S. Syrnev B.
September 2025Multidisciplinary Digital Publishing Institute (MDPI)
Journal of Functional Biomaterials
2025#16Issue 9
The reconstruction of segmental bone defects requires patient-specific scaffolds that combine mechanical safety, biological functionality, and rapid manufacturing. We converted CT-derived mandibular geometry into a functionally graded Ti-6Al-4V lattice and optimised porosity, screw layout, and strut thickness through a cyber-physical loop that joins high-fidelity FEM, millisecond ANN, and a BN for uncertainty quantification. Fifteen candidate scaffolds were fabricated by direct metal laser sintering and hot isostatic pressing and were mechanically tested. FEM predicted stress and stiffness with 98% accuracy; the ANN reproduced these outputs with 94% fidelity while evaluating 10,000 designs in real time, and the BN limited failure probability to <3% under worst-case loads. The selected 55–65% porosity design reduced titanium use by 15%, shortened development time by 25% and raised multi-objective optimisation efficiency by 20% relative to a solid-plate baseline, while resisting a 600 N bite with a peak von Mises stress of 225 MPa and micromotion < 150 µm. Integrating physics-based simulation, AI speed, and probabilistic rigour yields a validated, additively manufactured scaffold that meets surgical timelines and biomechanical requirements, offering a transferable blueprint for functional scaffolds in bone and joint surgery.
additive manufacturing (AM) , artificial neural network (ANN) , Bayesian network (BN) , direct metal laser sintering (DMLS) , finite-element modelling (FEM) , lattice architecture , material property prediction , patient-specific implant design
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Department of Ecology and Conservation Biology, Texas A&M University, College Station, 77843, TX, United States
Graduate School of Science and Technology, Niigata University, Niigata, 950-2181, Japan
Smart Engineering Competence Centre, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev Street, Ust-Kamenogorsk, 070010, Kazakhstan
Center of Excellence “VERITAS”, D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, 070004, Kazakhstan
Department of Ecology and Conservation Biology
Graduate School of Science and Technology
Smart Engineering Competence Centre
Center of Excellence “VERITAS”
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