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FROM EXPERIENCE-BASED TO KNOWLEDGE-DRIVEN DESIGN: A CASE STUDY OF A 3D-PRINTED PRODUCT

Published online by Cambridge University Press:  19 June 2023

Jakob Højeng-Swensson
Affiliation:
Technical University of Denmark
Victor Mathias Pisinger
Affiliation:
Technical University of Denmark
Herle Kjemtrup Juul-Nyholm*
Affiliation:
Technical University of Denmark
Brian Nyvang Legarth
Affiliation:
Technical University of Denmark
Tobias Eifler
Affiliation:
Technical University of Denmark
*
Juul-Nyholm, Herle Kjemtrup, Danmarks Tekniske Universitet / Technical University of Denmark Denmark, [email protected]

Abstract

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In this paper, a case study of a redesign process for 3D-printed parts has been analysed. The purpose was to compare the implementation of specialist knowledge in hands-on engineering tasks with the previous experience-based approach. Here, specialist knowledge refers to systematic experimental work as a basis for Computer Aided Engineering (CAE). The case involves a set of compliant arms for an oil extraction device developed by a start-up company. Tensile tests of 3D printed dog-bone were performed to characterise the Young's modulus, tensile strength, and orthotropic behaviour of the material to build a material model based on Finite Element Analysis (FEA). With the material characteristics and three simple tests to estimate the optimisation constraints, the existing solution was disproven. Then, new solution candidates were generated and evaluated with input from the start-up company. The process resulted in a feasible solution as well as a reduction of maximum stress from 54MPa to 20MPa. The case highlights the value of specialist knowledge for characterisation of new technologies and design space constraints to reduce and improve iterations to solve a practical design problem.

Type
Article
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), 2023. Published by Cambridge University Press

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