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

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  • Suh In Kim
  • Kaitlyn Gee
  • A. John Hart

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.

Suggested Citation

  • Suh In Kim & Kaitlyn Gee & A. John Hart, 2024. "A Bayesian sampling framework for constrained optimisation of build layouts in additive manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 62(16), pages 5772-5790, August.
    • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:16:p:5772-5790
    DOI: 10.1080/00207543.2023.2298477
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