A Bayesian sampling framework for constrained optimisation of build layouts in additive manufacturing
Name
A Bayesian sampling framework for constrained optimisation of build layouts in additive manufacturing.pdf
Description
Published version
Size
6.44 MB
Format
Adobe PDF
Checksum (MD5)
17893952116af5a6c77356dabf54685a
Author(s)
Kim, Suh In
Gee, Kaitlyn
Hart, A John
Date Issued
August 17, 2024
Journal
International Journal of Production Research
Publisher
Taylor & Francis
Citation
Kim, S. I., Gee, K., & Hart, A. J. (2024). A Bayesian sampling framework for constrained optimisation of build layouts in additive manufacturing. International Journal of Production Research, 62(16), 5772–5790.
Version
Final published version
Abstract
In additive manufacturing processes such as laser powder bed fusion, the build orientation and packing of components affect the required support structures, the number of parts in each build, and the surface roughness of the printed parts, among other factors. Maximising the packing density while minimising the build height can increase effective machine utilisation and decrease per-part cost. Yet, the build layout optimisation problem is highly nonlinear and difficult to solve using human intuition, so a systematic algorithm approach is required. Here, we present and demonstrate a voxel-based analysis method with Bayesian optimisation for determining component build orientation in additive manufacturing. We introduce selected case studies incorporating exemplary process attributes of laser powder bed fusion, including the determination of orientation and packing configurations based on support removal and tool-accessibility constraints.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/00207543.2023.2298477
