Biomedical applications of the powder-based 3D printed titanium alloys: A review

被引:65
作者
Guo, Amy X. Y. [1 ]
Cheng, Liangjie [1 ]
Zhan, Shuai [1 ]
Zhang, Shouyang [1 ]
Xiong, Wei [1 ]
Wang, Zihan [1 ]
Wang, Gang [1 ]
Cao, Shan Cecilia [1 ]
机构
[1] Shanghai Univ, Mat Genome Inst, Shanghai 200444, Peoples R China
来源
JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY | 2022年 / 125卷
关键词
The powder-based 3D printing technology; Ti-based alloys; Biomedical applications; Artificial intelligence; MECHANICAL-PROPERTIES; ORTHOPEDIC IMPLANTS; ANTIBACTERIAL ACTIVITY; TOPOLOGICAL DESIGN; GRAIN-REFINEMENT; BONE INGROWTH; IN-VIVO; MICROSTRUCTURE; SCAFFOLDS; DEPOSITION;
D O I
10.1016/j.jmst.2021.11.084
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
3D printing technology is a new type of precision forming technology and the core technology of the third industrial revolution. The powder-based 3D printing technology of titanium and its alloys have received great attention in biomedical applications since its advantages of custom manufacturing, costsaving, time-saving, and resource-saving potential. In particular, the personalized customization of 3D printing can meet specific needs and achieve precise control of micro-organization and structural design. The purpose of this review is to present the most advanced multi-material 3D printing methods for titanium-based biomaterials. We first reviewed the bone tissue engineering, the application of titanium alloy as bone substitutes and the development of manufacturing technology, which emphasized the advantages of 3D printing technology over traditional manufacturing methods. What is more, the optimization design of the hierarchical structure was analyzed to achieve the best mechanical properties, and the biocompatibility and osseointegration ability of the porous titanium alloy after implantation in living bodies was analyzed. Finally, we emphasized the development of digital tools such as artificial intelligence, which provides new ideas for the rational selection of processing parameters. The 3D printing titanium-based alloys will meet the huge market demand in the biomedical field, but there are still many challenges, such as the trade-off between high strength and low modulus, optimization of process parameters and structural design. We believe that the combination of mechanical models, machine learning, and metallurgical knowledge may shape the future of metal printing. ?? 2022 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
引用
收藏
页码:252 / 264
页数:13
相关论文
共 123 条
  • [51] Metallic Architectures from 3D-Printed Powder-Based Liquid Inks
    Jakus, Adam E.
    Taylor, Shannon L.
    Geisendorfer, Nicholas R.
    Dunand, David C.
    Shah, Ramille N.
    [J]. ADVANCED FUNCTIONAL MATERIALS, 2015, 25 (45) : 6985 - 6995
  • [52] Powder based additive manufacturing for biomedical application of titanium and its alloys: a review
    Jang, Tae-Sik
    Kim, DongEung
    Han, Ginam
    Yoon, Chang-Bun
    Jung, Hyun-Do
    [J]. BIOMEDICAL ENGINEERING LETTERS, 2020, 10 (04) : 505 - 516
  • [53] Machine learning: Trends, perspectives, and prospects
    Jordan, M. I.
    Mitchell, T. M.
    [J]. SCIENCE, 2015, 349 (6245) : 255 - 260
  • [54] Anisotropy characteristics of microstructures for bone substitutes and porous implants with application of additive manufacturing in orthopaedic
    Kang, Jianfeng
    Dong, Enchun
    Li, Dichen
    Dong, Shuangpeng
    Zhang, Chen
    Wang, Ling
    [J]. MATERIALS & DESIGN, 2020, 191
  • [55] Copper and homocysteine in cardiovascular diseases
    Kang, Y. James
    [J]. PHARMACOLOGY & THERAPEUTICS, 2011, 129 (03) : 321 - 331
  • [56] Effect of hot isostatic pressing on structure and properties of intermetallic NiAl-Cr-Mo alloy produced by selective laser melting
    Khomutov, M.
    Potapkin, P.
    Cheverikin, V
    Petrovskiy, P.
    Travyanov, A.
    Logachev, I
    Sova, A.
    Smurov, I
    [J]. INTERMETALLICS, 2020, 120 (120)
  • [57] Advances in gamma titanium aluminides and their manufacturing techniques
    Kothari, Kunal
    Radhakrishnan, Ramachandran
    Wereley, Norman M.
    [J]. PROGRESS IN AEROSPACE SCIENCES, 2012, 55 : 1 - 16
  • [58] Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
    Kotsiopoulos, Thanasis
    Sarigiannidis, Panagiotis
    Ioannidis, Dimosthenis
    Tzovaras, Dimitrios
    [J]. COMPUTER SCIENCE REVIEW, 2021, 40
  • [59] Larimian T., 2019, ADDITIVE MANUFACTURI, P1
  • [60] The Components of Bone and What They Can Teach Us about Regeneration
    Le, Bach Quang
    Nurcombe, Victor
    Cool, Simon McKenzie
    van Blitterswijk, Clemens A.
    de Boer, Jan
    LaPointe, Vanessa Lydia Simone
    [J]. MATERIALS, 2018, 11 (01)