LPBF工艺参数对多孔结构生物医用金属影响的研究进展

Research Progress on Effect of LPBF Process Parameters on Porous Metallic Biomaterials

摘要:

激光粉末床熔融（laser powder bed fusion，LPBF）技术已成为一种变革性的增材制造技术，可用于制备多孔结构金属，其定制化特性适用于多种生物医学应用场景。这类构件凭借轻量化设计、优异的力学性能和可定制化几何形状，在骨植入物、支架以及其他承重医疗器械中展现出独特优势。然而，LPBF工艺参数与最终结构及力学性能之间存在复杂的相互作用，这给实现临床应用所需的精度和可靠性带来了巨大挑战。本综述全面分析了激光功率、扫描速度、层厚等LPBF工艺参数对致密度、尺寸精度、微观结构和表面粗糙度等关键性能指标的影响，并深入探讨了这些参数对材料强度、抗疲劳性及功能特性等力学性能的作用，尤其关注其在生物医学领域的相关性。此外，还讨论了拓扑结构、单胞尺寸和取向等晶格设计因素的影响，强调了这些因素在优化医疗应用中生物相容性和结构完整性方面的重要作用。表面缺陷、几何精度偏差和微观结构不均匀性等问题被视为LPBF技术在生物医学领域广泛应用的主要障碍。未来的研究方向聚焦于通过工艺优化以及与机器学习等先进计算工具的融合来推动LPBF技术的发展，从而高效制造复杂的、患者特异性的结构。通过攻克这些挑战，LPBF技术有望为下一代生物材料的研发带来革命性突破，以满足不断发展的临床需求并改善患者预后。

Abstract:

Laser powder bed fusion (LPBF) has emerged as a powerful additive manufacturing technique for fabricating architected cellular metallic structures with precisely tailored properties, making it particularly attractive for biomedical applications such as bone implants, scaffolds, and load-bearing devices. By enabling lightmass designs, complex geometries, and tunable mechanical behavior, LPBF offers unique opportunities for matching implant performance with biological and mechanical requirements. However, the intricate coupling between LPBF process parameters and the resulting structural, microstructural, and mechanical properties remain a major challenge for achieving the consistency and reliability demanded in clinical practice. This review comprehensively analyzed the effects of key LPBF parameters, such as laser power, scanning speed, and layer thickness on dimensional accuracy, relative density, microstructure, and surface roughness of metallic cellular structures. Their subsequent influence on mechanical performance, such as strength, fatigue resistance, and functional behavior, was critically discussed with a focus on biomedical relevance. In addition, the role of lattice design variables, including topology, unit cell size, and build orientation, was examined, highlighting their importance in optimizing mechanical integrity and biocompatibility. Current challenges, such as surface defects, geometric deviations, and microstructural heterogeneity, were identified as critical barriers to broader biomedical adoption. Finally, future perspectives emphasize process optimization and the integration of advanced computational approaches, particularly machine learning, to accelerate the design and manufacturing of complex, patient-specific biomedical architectures.