Research Progress on Personalized Bone Implants Based on Additive Manufacturing
Abstract
1. Introduction
2. Design Method for Personalized Bone Implants
2.1. Medical Imaging and 3D Modeling
2.2. Design Method for Porous Structures of Implants
2.2.1. Constructive Solid Geometry Method
2.2.2. Topology-Optimization-Based Design of Porous Bone Implants
2.2.3. Design Method for Triply Periodic Minimal Surfaces
2.2.4. Comparative Analysis of Design Methods
3. Material Systems for Personalized Bone Implants
3.1. Metal Materials
3.2. Polymer Materials
3.3. Ceramic Materials
3.4. Considerations for Material Selection
4. Manufacture Method for Bone Implants Based on Additive Manufacturing
4.1. Powder Bed Fusion
4.1.1. Selective Laser Melting
4.1.2. Electron Beam Melting
4.2. Fused Deposition Modeling
4.3. Binder Jetting
4.4. Stereolithography
4.5. Comparison of Different Additive Manufacturing Technologies
5. Performance Evaluation of Personalized Bone Implants
5.1. Mechanical Property
5.2. Surface Antimicrobial Properties
5.3. Osteointegration Effect
6. Application of AI in the Field of Bone Implants
7. Conclusions and Perspectives
- (1)
- Machine learning models can be trained on long-term clinical follow-up data, which can be used to predict patient implant lifespan and identify risk factors. Furthermore, machine learning models can simulate the systematic distribution and biological effects of degradation products, transforming safety assessments from post-analysis to proactive design.
- (2)
- A digital twin virtual platform can overcome regulatory bottlenecks for personalized implants. Implants equipped with biosensors can feed patient physiological data back to their digital twin. This feedback loop can achieve real-time detection of postoperative status, prevent the occurrence of complications, and customize personalized rehabilitation plans.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
| CT | computed tomography |
| Mimics | Materialise’s interactive medical image control system |
| L-PBF | Laser Powder Bed Fusion |
| SLM | Selective Laser Melting |
| AI | artificial intelligence |
| CSG | Constructive Solid Geometry |
| TPMS | Triple Periodic Minimal Surface |
| LSRCMS | layered rod-connected hexagonal porous structure |
| NPR | Negative Poisson’s Ratio |
| OC | optimality criteria |
| HA/HAp | hydroxyapatite |
| PLA | polylactic acid |
| β-TCP | β-tricalcium phosphate |
| BCP | biphasic calcium phosphate |
| Mg | magnesium |
| Fe | iron |
| Zn | zinc |
| PEEK | polyether ether ketone |
| UHMWPE | ultra-high molecular weight polyethylene |
| PDA | poly-dopamine |
| Mn | manganese |
| Cu | copper |
| nMgO | nano-magnesium oxide |
| PLLA | left-handed polylactic acid |
| ATZ | alumina toughened zirconia |
| CPC | calcium phosphate ceramics |
| BAG | bioactive glass |
| TTCP | tricalcium phosphate |
| WH | Whitlockite |
| PMMA | polymethyl methacrylate |
| PCL | polycaprolactone |
| EBM | Electron Beam Melting |
| FDM | Fused Deposition Modeling |
| BJ | Binder Jetting |
| PBF | Powder Bed Fusion |
| SLS | Selective Laser Sintering |
| MJF | Multi-Jet Fusion |
| SLA | Stereolithography |
| DO | diamond-like |
| RD | rhombic dodecahedral |
| PLGA | polylactic-polyhydroxyacetic acid copolymer |
| PA | polyamide |
| Qr | quercetin |
| NO | nitric oxide |
| ML | machine learning |
| CNN | Convolutional Neural Networks |
| FE | finite element |
| SSM | statistical shape modeling |
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| Constructive Solid Geometry | Topology Optimization | Triply Periodic Minimal Surfaces | |
|---|---|---|---|
| Design Principles | Constructing complex structures through Boolean operations on basic geometric primitives | Simulation-driven material distribution under specified constraints to achieve optimal performance | Generation of Periodic Porous Structures Based on Implicit Mathematical Surfaces |
| Core Advantages | Simple operation, rapid generation, and easy implementation of gradient structures | Structural lightweighting, optimal mechanical properties, low stress shielding effect | Structures exhibit continuous smoothness, excellent pore connectivity, and high biocompatibility |
| Limitations | Structural connectivity and mechanical properties are inferior to TPMS and topology optimization | Complex design process with high computational costs, requiring integration of manufacturing constraints | Mathematical modeling is relatively complex; gradient design requires integration with other methods |
| Primary Applications | Bionic gradient structures and personalized implants | Lightweight implant design, load-bearing structure optimization, multi-objective performance design | Promotes osseointegration, features highly permeable structures, and mimics biological systems |
| Development Trends | Combined with bioactive coatings, smart materials, and multi-material printing | Multiphysics optimization, biodegradable material design, AI-assisted | Gradient TPMS, topology optimization fusion, multiscale modeling |
| Material Type | Representative Materials | Advantages | Disadvantages | Scope of Application |
|---|---|---|---|---|
| Metallic Materials | Titanium and its alloys | Excellent biocompatibility, low elastic modulus, high strength, corrosion resistance | Poor wear resistance, relatively high cost | Load-bearing implants: joint prosthesis stems, bone plates, screws, spinal fusion devices |
| Cobalt-chromium alloys | Exceptional wear resistance, highest strength and hardness, corrosion resistance | Excessively high elastic modulus, relatively poor biocompatibility, allergenic potential | Friction pairs in joint replacements: femoral heads and femoral condyles in artificial hip | |
| Stainless steel (316L) | Low cost, easy machinability, good mechanical properties | Worst corrosion resistance, high elastic modulus, relatively poorest biocompatibility | Primarily used for temporary implants: fracture fixation pins, plates, screws | |
| Biodegradable alloys | Biodegradable and absorbable, elastic modulus closest to bone, degradation products promote osteogenesis | Difficult to control degradation rate, potential hydrogen gas generation during degradation | Fracture internal fixation in non-weight-bearing areas: cardiovascular stents, maxillofacial | |
| Ceramic Materials | Alumina/zirconia | Optimal biocompatibility, highest wear resistance, corrosion resistance | High brittleness, difficult to process and manufacture, high cost | Wear-resistant interfaces in joint replacements: Femoral heads and acetabular liners in hip joints |
| Hydroxyapatite | Excellent osteoconductivity, capable of forming chemical bonds with bone | High brittleness, low strength, unsuitable for load-bearing applications | Surface coatings for metal implants, bone defect fillers | |
| β-tricalcium phosphate | High bioactivity, promotes bone products serve as osteogenic raw materials | High brittleness, low strength, degradation rate requires control | Bone defect filling: Spinal fusion, bone cysts, alveolar ridge augmentation | |
| Polymeric Materials | Ultra-high molecular weight polyethylene | Exceptional toughness, good wear resistance, low friction coefficient | Creep behavior, wear particles may induce bone resorption | Wear-bearing surfaces in joint replacements: Acetabular liners, tibial pads |
| Polyether ether ketone | Low elastic modulus, radiolucent, fatigue-resistant, high machinability | Lacks osseointegration capability, average wear resistance, low strength | Spinal fusion devices, non-load-bearing components for joint replacements |
| SLM | EBM | FDM | BJ | SLA | |
|---|---|---|---|---|---|
| Working Principle | Laser selective complete melting of metal powder layers | Electron beam melts preheated metal powder layers in vacuum | Heated nozzles melt and extrude polymer filaments, layering them sequentially | The nozzle sprays binder onto the powder bed, followed by sintering to densify the part | Ultraviolet light selectively cures the flowing slurry and undergoes post-processing |
| Core Advantages | High precision, high density, mechanical properties close to forgings | Low residual stress, fast printing speed, clean vacuum environment | Extremely low cost, simple operation, diverse materials | Extremely fast printing speed, capable of producing highly complex structures, no thermal stress | High resolution, good surface finish, high density |
| Primary Disadvantages | Significant residual stresses, high equipment and material costs, surface requires polishing | Rougher surface, relatively lower precision, extremely high equipment and maintenance costs | Poor precision and surface quality, risk of microbial growth | Complex post-processing, high shrinkage rate, mechanical properties depend on sintering quality | Complex post-processing, Limitations of materials and slurries |
| Typical Materials | Ti6Al4V, CoCr, 316L stainless steel | Ti6Al4V, pure titanium | PEEK, PLA | Titanium powder, calcium phosphate ceramics | Ceramics, polymers |
| Surface Quality | Rough surface (requires post-processing) | Very rough (requires post-processing) | Noticeable layer lines, rough surface | Depends on powder and sintering process | Good surface finish |
| Primary Applications | Load-bearing permanent implants (hip, knee) | Load-bearing permanent implants (large low-stress components) | Surgical guides, planning models, surface coatings | Complex porous bone scaffolds, non-load-bearing implants | Dental, craniofacial, and orthopedic |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gao, B.; Sun, Z.; Tong, Y.; Yu, H.; Wang, F. Research Progress on Personalized Bone Implants Based on Additive Manufacturing. Micromachines 2025, 16, 1339. https://doi.org/10.3390/mi16121339
Gao B, Sun Z, Tong Y, Yu H, Wang F. Research Progress on Personalized Bone Implants Based on Additive Manufacturing. Micromachines. 2025; 16(12):1339. https://doi.org/10.3390/mi16121339
Chicago/Turabian StyleGao, Bingwei, Zhonghui Sun, Yanquan Tong, Hongtao Yu, and Feng Wang. 2025. "Research Progress on Personalized Bone Implants Based on Additive Manufacturing" Micromachines 16, no. 12: 1339. https://doi.org/10.3390/mi16121339
APA StyleGao, B., Sun, Z., Tong, Y., Yu, H., & Wang, F. (2025). Research Progress on Personalized Bone Implants Based on Additive Manufacturing. Micromachines, 16(12), 1339. https://doi.org/10.3390/mi16121339
