A Bayesian sampling framework for constrained optimisation of build layouts in additive manufacturing-Reference-Cited by-同舟云学术

A Bayesian sampling framework for constrained optimisation of build layouts in additive manufacturing

Published:2024-01-11 Issue:16 Volume:62 Page:5772-5790
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Kim Suh In¹,Gee Kaitlyn¹,Hart A. John¹

Affiliation:

1. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

Ford-MIT

Alliance

MIT-Portugal

Programme

(MPP2030)

MIT-CUHK

collaboration

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2298477

Reference40 articles.

1. Support optimization in additive manufacturing for geometric and thermo-mechanical constraints

2. A voxel-based method of constructing and skinning conformal and functionally graded lattice structures suitable for additive manufacturing

3. Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible

4. BMW Group. 2020. “Industrial-Scale 3D Printing Continues To Advance at BMW Group” [Online] Available: https://www.press.bmwgroup.com/global/article/detail/T0322259EN/industrial-scale-3d-printing-continues-to-advance-at-bmw-group [Accessed: 22-Dec-2022].

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