A rapid design exploration framework under additive manufacturing process uncertainty
Current computer-aided design (CAD) systems are not computationally efficient for exploring the growing design space of products manufactured with additive manufacturing (AM). A rapid method with sufficient fidelity is desired. Meanwhile, uncertainties within the additive manufacturing process sh...
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sg-ntu-dr.10356-882732020-09-24T20:11:27Z A rapid design exploration framework under additive manufacturing process uncertainty Xiong, Yi Duong, Pham Luu Trung Raghavan, Nagarajan Rosen, David W School of Mechanical and Aerospace Engineering Proceedings of the 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018) Singapore Centre for 3D Printing Design For Additive Manufacturing Computer-aided Design DRNTU::Engineering::Mechanical engineering::Prototyping Current computer-aided design (CAD) systems are not computationally efficient for exploring the growing design space of products manufactured with additive manufacturing (AM). A rapid method with sufficient fidelity is desired. Meanwhile, uncertainties within the additive manufacturing process should be adequately considered. This study presents a rapid design exploration framework which considers the additive manufacturing process uncertainty based on metamodels. The framework is demonstrated with a multi-material designed stiffness construct design case, with an embedded horseshoe structure, which is manufactured with projection-based stereolithography (SLA) process. Published version 2018-08-29T07:00:48Z 2019-12-06T16:59:36Z 2018-08-29T07:00:48Z 2019-12-06T16:59:36Z 2018 Conference Paper Xiong, Y., Duong, P. L. T., Raghavan, N., & Rosen, D. W. (2018). A rapid design exploration framework under additive manufacturing process uncertainty. Proceedings of the 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018), 208-213. doi:10.25341/D44K5S https://hdl.handle.net/10356/88273 http://hdl.handle.net/10220/45732 10.25341/D44K5S en © 2018 Nanyang Technological University. Published by Nanyang Technological University, Singapore. 6 p. application/pdf |
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Design For Additive Manufacturing Computer-aided Design DRNTU::Engineering::Mechanical engineering::Prototyping Xiong, Yi Duong, Pham Luu Trung Raghavan, Nagarajan Rosen, David W A rapid design exploration framework under additive manufacturing process uncertainty |
description |
Current computer-aided design (CAD) systems are not computationally efficient for
exploring the growing design space of products manufactured with additive manufacturing (AM).
A rapid method with sufficient fidelity is desired. Meanwhile, uncertainties within the additive
manufacturing process should be adequately considered. This study presents a rapid design
exploration framework which considers the additive manufacturing process uncertainty based on
metamodels. The framework is demonstrated with a multi-material designed stiffness construct
design case, with an embedded horseshoe structure, which is manufactured with projection-based
stereolithography (SLA) process. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Xiong, Yi Duong, Pham Luu Trung Raghavan, Nagarajan Rosen, David W |
format |
Conference or Workshop Item |
author |
Xiong, Yi Duong, Pham Luu Trung Raghavan, Nagarajan Rosen, David W |
author_sort |
Xiong, Yi |
title |
A rapid design exploration framework under additive manufacturing process uncertainty |
title_short |
A rapid design exploration framework under additive manufacturing process uncertainty |
title_full |
A rapid design exploration framework under additive manufacturing process uncertainty |
title_fullStr |
A rapid design exploration framework under additive manufacturing process uncertainty |
title_full_unstemmed |
A rapid design exploration framework under additive manufacturing process uncertainty |
title_sort |
rapid design exploration framework under additive manufacturing process uncertainty |
publishDate |
2018 |
url |
https://hdl.handle.net/10356/88273 http://hdl.handle.net/10220/45732 |
_version_ |
1681056900636999680 |