Topology optimisation for large-scale additive manufacturing: generating designs tailored to the deposition nozzle size

E. Fernández*, C. Ayas, M. Langelaar, P. Duysinx

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)
41 Downloads (Pure)

Abstract

Additive Manufacturing (AM) processes intended for large-scale components deposit large volumes of material to shorten process duration. This reduces the resolution of the AM process, which is typically defined by the deposition nozzle size. If the resolution limitation is not considered when designing for Large-Scale Additive Manufacturing (LSAM), difficulties can arise in the manufacturing process, which may require the adaptation of deposition parameters. This work incorporates the nozzle size constraint into Topology Optimisation (TO) in order to generate optimised designs suitable to the process resolution. This article proposes and compares two methods, which are based on existing TO techniques that enable control of minimum and maximum member size, and of minimum cavity size. The first method requires the minimum and maximum member size to be equal to the deposition nozzle size, thus design features of uniform width are obtained. The second method defines the size of solid members sufficiently small for the resulting structure to resemble a structural skeleton, which can be interpreted as the deposition path. Through filtering and projection techniques, the thin structures are thickened according to the chosen nozzle size. Thus, a topology tailored to the deposition nozzle size is obtained along with a deposition proposal. The methods are demonstrated and assessed using 2D and 3D benchmark problems.

Original languageEnglish
Pages (from-to)196-220
JournalVirtual and Physical Prototyping
Volume16
Issue number2
DOIs
Publication statusPublished - 2021

Bibliographical note

Accepted Author Manuscript

Keywords

  • design for AM
  • maximum size
  • Nozzle size
  • topology optimisation
  • WAAM

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