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DATA ANALYSIS AS THE BASIS FOR IMPROVED DESIGN FOR ADDITIVE MANUFACTURING (DFAM)

Published online by Cambridge University Press:  27 July 2021

Dominika Hamulczuk  and
Ola Isaksson
Dominika Hamulczuk*
Affiliation:
Chalmers University of Technology
Ola Isaksson
Affiliation:
Chalmers University of Technology
*
Hamulczuk, Dominika, Chalmers University of Technology, Mechanics and Maritime Sciences, Sweden, [email protected]

Abstract

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Additive Manufacturing (AM) has a large potential to revolutionize the manufacturing industry, yet the printing parameters and part design have a profound impact on the robustness of the printing process as well as the resulting quality of the manufactured components. To control the printing process, a substantial number of parameters is measured while printing and used primarily to control and adjust the printing process in-situ. The question raised in this paper is how to benefit from these data being gathered to gain insight into the print process stability. The case study performed included the analysis of data gathered during printing 22 components. The analysis was performed with a widely used Random Forest Classifier. The study revealed that the data did contain some detectable patterns that can be used further in assessing the quality of the printed component, however, they were distinct enough so that in case the test and train sets were comprised of separate components the predictions’ result was very poor. The study gives a good understanding of what is necessary to do a meaningful analytics study of manufacturing data from a design perspective.

Keywords

Design for Additive Manufacturing (DfAM)Machine learningAdditive Manufacturing
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 811 - 820
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), 2021. Published by Cambridge University Press

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