Current and future trends in topology optimization for additive manufacturing

被引：6
作者：
Jikai Liu
Andrew T. Gaynor
Shikui Chen
Zhan Kang
Krishnan Suresh
Akihiro Takezawa
Lei Li
Junji Kato
Jinyuan Tang
Charlie C. L. Wang
Lin Cheng
Xuan Liang
Albert. C. To
机构：
[1] University of Pittsburgh,Department of Mechanical Engineering and Materials Science
[2] U.S. Army Research Laboratory,Materials Response and Design Branch, Weapons and Materials Research Directorate
[3] RDRL-WMM-B,Department of Mechanical Engineering
[4] State University of New York,State Key Laboratory of Structural Analysis for Industrial Equipment
[5] Dalian University of Technology,Department of Mechanical Engineering
[6] University of Wisconsin-Madison,Department of Transportation and Environmental Engineering, Graduate School of Engineering
[7] Hiroshima University,Department of Civil & Environmental Engineering & Earth Science
[8] University of Notre Dame,Mechanics of Materials Laboratory
[9] Tohoku University,State Key Laboratory of High
[10] Central South University,Performance Complex Manufacturing
[11] Delft University of Technology,Department of Design Engineering
来源：
Structural and Multidisciplinary Optimization | 2018年 / 57卷
关键词：
Additive manufacturing; Topology optimization; Support structure; Lattice infill; Material feature; Multi-material; Uncertainty; Post-treatment;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：2457 / 2483
页数：26
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