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KNOWLEDGE-BASED DATA IDENTIFICATION FOR MACHINE LEARNING USE CASES

Published online by Cambridge University Press:  19 June 2023

Helena Ebel ,
Sahar Ben Hassine  and
Rainer Stark
Helena Ebel*
Affiliation:
Technische Universität Berlin
Sahar Ben Hassine
Affiliation:
Technische Universität Berlin
Rainer Stark
Affiliation:
Technische Universität Berlin
*
Ebel, Helena, Technische Universität Berlin, Germany, [email protected]

Abstract

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The number of digital solutions based on machine learning has increased in recent years. In many industrial sectors, they try to enhance automation in manual or repetitive tasks or provide decision support for complex problems. Data plays an essential role in the selection and implementation of ML algorithms, as it determines the quality of the training and the results. As data drive ML models, selecting the correct data with the suitable ML algorithm for a given use case is crucial but challenging. This paper reviews the application of machine learning in the embodiment design phase addressing the challenge. The work focuses on ML applications in conventional product development and non-conventional additive manufacturing processes. Based on the literature review, the required knowledge to implement the ML algorithms has been derived and presented in a systematic approach. This work highlights the importance of an initial analysis of the existing knowledge in the engineering and additive manufacturing processes in order to implement the proper ML algorithms.

Keywords

Machine learningEmbodiment designDesign for Additive Manufacturing (DfAM)Knowledge
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2395 - 2404
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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