Zhao Shangyan, Shi Yixuan, Huang Chengcong, Li Xuan, Lu Yuchen, Wu Yuzhi, Li Yageng, Wang Luning
Beijing Advanced Innovation Center for Materials Genome Engineering, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, China.
J Funct Biomater. 2025 Feb 21;16(3):77. doi: 10.3390/jfb16030077.
The global increase in osteomuscular diseases, particularly bone defects and fractures, has driven the growing demand for metallic implants. Additive manufacturing (AM) has emerged as a transformative technology for producing high-precision metallic biomaterials with customized properties, offering significant advantages over traditional manufacturing methods. The integration of machine learning (ML) with AM has shown great promise in optimizing the fabrication process, enhancing material performance, and predicting long-term behavior, particularly in the development of orthopedic implants and vascular stents. This review explores the application of ML in AM of metallic biomaterials, focusing on four key areas: (1) component design, where ML guides the optimization of multi-component alloys for improved mechanical and biological properties; (2) structural design, enabling the creation of intricate porous architectures tailored to specific functional requirements; (3) process control, facilitating real-time monitoring and adjustment of manufacturing parameters; and (4) parameter optimization, which reduces costs and enhances production efficiency. This review offers a comprehensive overview of four key aspects, presenting relevant research and providing an in-depth analysis of the current state of ML-guided AM techniques for metallic biomaterials. It enables readers to gain a thorough understanding of the latest advancements in this field. Additionally, the this review addresses the challenges in predicting performance, particularly degradation behavior, and how ML models can assist in bridging the gap between tests and clinical outcomes. The integration of ML in AM holds great potential to accelerate the design and production of advanced metallic biomaterials.
全球骨肌肉疾病的增加，尤其是骨缺损和骨折，推动了对金属植入物需求的增长。增材制造（AM）已成为一种变革性技术，用于生产具有定制特性的高精度金属生物材料，相较于传统制造方法具有显著优势。机器学习（ML）与增材制造的结合在优化制造工艺、提高材料性能和预测长期行为方面显示出巨大潜力，特别是在骨科植入物和血管支架的开发中。本综述探讨了机器学习在金属生物材料增材制造中的应用，重点关注四个关键领域：（1）部件设计，其中机器学习指导多组分合金的优化以改善机械和生物学性能；（2）结构设计，能够创建符合特定功能要求的复杂多孔结构；（3）过程控制，便于实时监测和调整制造参数；（4）参数优化，可降低成本并提高生产效率。本综述全面概述了四个关键方面，介绍了相关研究并深入分析了机器学习指导的金属生物材料增材制造技术的现状。它使读者能够全面了解该领域的最新进展。此外，本综述还讨论了预测性能方面的挑战，特别是降解行为，以及机器学习模型如何有助于弥合测试与临床结果之间的差距。机器学习与增材制造的结合具有加速先进金属生物材料设计和生产的巨大潜力。
