Knowledge-based data identification for machine learning use cases

DS 122: Proceedings of the Design Society: 24th International Conference on Engineering Design (ICED23)

Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad├Ęge Troussier
Author: Ebel, Helena; Ben Hassine, Sahar; Stark, Rainer
Series: ICED
Institution: Technische Universit├Ąt Berlin
Section: Design Methods
Page(s): 2395-2404
DOI number: https://doi.org/10.1017/pds.2023.240
ISBN: -
ISSN: -

Abstract

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 learning, Embodiment design, Design for Additive Manufacturing (DfAM), Knowledge

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