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 language | English |
|---|---|
| Pages (from-to) | 1809-1818 |
| Number of pages | 10 |
| Journal | Proceedings of the Design Society |
| Volume | 4 |
| Early online date | 16 May 2024 |
| DOIs | |
| Publication status | Published - 31 May 2024 |
| Event | 2024 International Design Society Conference, Design 2024 - Cavtat, Dubrovnik, Croatia Duration: 20 May 2024 → 23 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