Current and future trends in topology optimization for additive manufacturing

Jikai Liu, Andrew T. Gaynor, Shikui Chen, Zhan Kang, Krishnan Suresh, Akihiro Takezawa, Lei Li, Junji Kato, Jinyuan Tang, Charlie Wang, Lin Cheng, Xuan Liang, Albert. C. To

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563 Citations (Scopus)
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Manufacturing-oriented topology optimization has been extensively studied the past two decades, in particular for the conventional manufacturing methods, for example, machining and injection molding or casting. Both design and manufacturing engineers have benefited from these efforts because of the close-to-optimal and friendly-to-manufacture design solutions. Recently, additive manufacturing (AM) has received significant attention from both academia and industry. AM is characterized by producing geometrically complex components layer-by-layer, and greatly reduces the geometric complexity restrictions imposed on topology optimization by conventional manufacturing. In other words, AM can make near-full use of the freeform structural evolution of topology optimization. Even so, new rules and restrictions emerge due to the diverse and intricate AM processes, which should be carefully addressed when developing the AM-specific topology optimization algorithms. Therefore, the motivation of this perspective paper is to summarize the state-of-art topology optimization methods for a variety of AM topics. At the same time, this paper also expresses the authors’ perspectives on the challenges and opportunities in these topics. The hope is to inspire both researchers and engineers to meet these challenges with innovative solutions.

Original languageEnglish
Pages (from-to)2457-2483
Number of pages27
JournalStructural and Multidisciplinary Optimization
Issue number6
Publication statusPublished - 2018

Bibliographical note

Accepted author manuscript


  • Additive manufacturing
  • Lattice infill
  • Material feature
  • Multi-material
  • Post-treatment
  • Support structure
  • Topology optimization
  • Uncertainty


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