Print Email Facebook Twitter Current and future trends in topology optimization for additive manufacturing Title Current and future trends in topology optimization for additive manufacturing Author Liu, Jikai (University of Pittsburgh) Gaynor, Andrew T. (U.S. Army Research Laboratory) Chen, Shikui (State University of New York) Kang, Zhan (Dalian University of Technology) Suresh, Krishnan (University of Wisconsin-Madison) Takezawa, Akihiro (Hiroshima University) Li, Lei (University of Notre Dame) Kato, Junji (Tohoku University) Tang, Jinyuan (Central South University) Wang, C.C. (TU Delft Materials and Manufacturing) Cheng, Lin (University of Pittsburgh) Liang, Xuan (University of Pittsburgh) To, Albert. C. (University of Pittsburgh) Date 2018 Abstract 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. Subject Additive manufacturingLattice infillMaterial featureMulti-materialPost-treatmentSupport structureTopology optimizationUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:3e9873dd-a6d6-49d6-8fc5-57c0ab99acae DOI https://doi.org/10.1007/s00158-018-1994-3 Embargo date 2019-06-30 ISSN 1615-147X Source Structural and Multidisciplinary Optimization, 57 (6), 2457-2483 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type journal article Rights © 2018 Jikai Liu, Andrew T. Gaynor, Shikui Chen, Zhan Kang, Krishnan Suresh, Akihiro Takezawa, Lei Li, Junji Kato, Jinyuan Tang, C.C. Wang, Lin Cheng, Xuan Liang, Albert. C. To Files PDF SMO_Survey.pdf 9.91 MB Close viewer /islandora/object/uuid:3e9873dd-a6d6-49d6-8fc5-57c0ab99acae/datastream/OBJ/view