Abstract

Tools for analysing additive manufacturability often employ complex models that lack transparency; this impedes user understanding and has detrimental effects on the implementation of results. An expert system tool that transparently learns features for successful printing has been created. The tool uses accessible data from STL models and printer configurations to create explainable parameters and identify risks. Testing has shown good agreement to print behaviour and easy adaptability. The tool reduces the learning curves designers face in understanding design for additive manufacturing.

Original languageEnglish
Pages (from-to)1809-1818
Number of pages10
JournalProceedings of the Design Society
Volume4
Early online date16 May 2024
DOIs
Publication statusPublished - 31 May 2024
Event2024 International Design Society Conference, Design 2024 - Cavtat, Dubrovnik, Croatia
Duration: 20 May 202423 May 2024

Keywords

  • additive manufacturing
  • bayesian
  • design for additive manufacturing
  • expert systems
  • machine learning

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Software
  • Modelling and Simulation

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