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A Bayesian expert system for additive manufacturing design assessment

Published online by Cambridge University Press:  16 May 2024

Benedict Alexander Rogers ,
Neill Campbell ,
Mandeep Dhanda ,
Alexander James George Lunt ,
Elise Catherine Pegg  and
Vimal Dhokia
Benedict Alexander Rogers*
Affiliation:
University of Bath, United Kingdom
Neill Campbell
Affiliation:
University of Bath, United Kingdom
Mandeep Dhanda
Affiliation:
University of Bath, United Kingdom
Alexander James George Lunt
Affiliation:
University of Bath, United Kingdom
Elise Catherine Pegg
Affiliation:
University of Bath, United Kingdom
Vimal Dhokia
Affiliation:
University of Bath, United Kingdom
*
bar34@bath.ac.uk

Abstract

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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.

Keywords

additive manufacturingexpert systemsbayesiandesign for additive manufacturingmachine learning

Information

Type
Design for Additive Manufacturing
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1809 - 1818
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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