Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook

被引:57
|
作者
Ibhadode, Osezua [1 ,2 ,4 ]
Zhang, Zhidong [2 ]
Sixt, Jeffrey [2 ]
Nsiempba, Ken M. [2 ]
Orakwe, Joseph [2 ]
Martinez-Marchese, Alexander [2 ]
Ero, Osazee [2 ]
Shahabad, Shahriar Imani [2 ]
Bonakdar, Ali [3 ]
Toyserkani, Ehsan [2 ]
机构
[1] Univ Alberta, Dept Mech Engn, Edmonton, AB, Canada
[2] Univ Waterloo, Multiscale Addit Mfg Lab, Waterloo, ON, Canada
[3] Siemens Energy Canada Ltd, Montreal, PQ, Canada
[4] Univ Alberta, Donadeo Innovat Ctr Engn, Dept Mech Engn, 10-352,116 St NW, Edmonton, AB T6G 1H9, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Metal additive manufacturing; additive manufacturing; topology optimisation; aerospace; automotive; medical; SELF-SUPPORTING STRUCTURES; MAXIMUM LENGTH SCALE; OVERHANG ANGLE CONTROL; STRUCTURAL OPTIMIZATION; DESIGN OPTIMIZATION; RESIDUAL-STRESSES; LATTICE STRUCTURES; HEAT SINKS; THERMAL-CONDUCTIVITY; TITANIUM IMPLANTS;
D O I
10.1080/17452759.2023.2181192
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Metal additive manufacturing is gaining immense research attention. Some of these research efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure-property characterisation, structural design optimisation, or equipment enhancements for cost reduction and faster throughputs. In this review, the focus is drawn on the utilisation of topology optimisation for structural design in metal additive manufacturing. First, the symbiotic relationship between topology optimisation and metal additive manufacturing in aerospace, medical, automotive, and other industries is investigated. Second, support structure design by topology optimisation for thermal-based powder-bed processes is discussed. Third, the introduction of capabilities to limit manufacturing constraints and generate porous features in topology optimisation is examined. Fourth, emerging efforts to adopt artificial intelligence models are examined. Finally, some open-source and commercial software with capabilities for topology optimisation and metal additive manufacturing are explored. This study considers the challenges faced while providing perceptions on future research directions.
引用
收藏
页数:53
相关论文
共 50 条
  • [31] UTILIZING DESIGN FOR METAL ADDITIVE MANUFACTURING AND TOPOLOGY OPTIMIZATION TO IMPROVE PRODUCT DESIGNS
    Tanaka, Martin L.
    Smith, Jeremy J.
    PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2019, VOL 14, 2020,
  • [32] Self-support topology optimization considering distortion for metal additive manufacturing
    Miki, Takao
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2023, 404
  • [33] Suitability of metal additive manufacturing processes for part topology optimization - A comparative study
    Nirish, Mudda
    Rajendra, R.
    MATERIALS TODAY-PROCEEDINGS, 2020, 27 : 1601 - 1607
  • [34] Current trends and issues of additive manufacturing
    Miura, Hideshi
    Funtai Oyobi Fummatsu Yakin/Journal of the Japan Society of Powder and Powder Metallurgy, 2015, 62 (08):
  • [35] Morphable components topology optimization for additive manufacturing
    Yeming Xian
    David W. Rosen
    Structural and Multidisciplinary Optimization, 2020, 62 : 19 - 39
  • [36] An intelligent algorithm for topology optimization in additive manufacturing
    Reza Karimzadeh
    Mohsen Hamedi
    The International Journal of Advanced Manufacturing Technology, 2022, 119 : 991 - 1001
  • [37] Topology optimization for additive manufacturing of CFRP structures
    Xu, Yanan
    Feng, Zhaoxuan
    Gao, Yunkai
    Wu, Chi
    Fang, Jianguang
    Sun, Guangyong
    Qiu, Na
    Steven, Grant P.
    Li, Qing
    INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, 2024, 269
  • [38] Topology optimization subject to additive manufacturing constraints
    Moritz Ebeling-Rump
    Dietmar Hömberg
    Robert Lasarzik
    Thomas Petzold
    Journal of Mathematics in Industry, 11
  • [39] Topology optimization and additive manufacturing for aerospace components
    Laura Berrocal
    Rosario Fernández
    Sergio González
    Antonio Periñán
    Santos Tudela
    Jorge Vilanova
    Luis Rubio
    Jose Manuel Martín Márquez
    Javier Guerrero
    Fernando Lasagni
    Progress in Additive Manufacturing, 2019, 4 : 83 - 95
  • [40] Topology optimization methods for additive manufacturing: a review
    El Khadiri I.
    Zemzami M.
    Nguyen N.-Q.
    Abouelmajd M.
    Hmina N.
    Belhouideg S.
    International Journal for Simulation and Multidisciplinary Design Optimization, 2023, 14