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TOWARDS AUTOMATED CLASSIFICATION OF PRODUCT DATA BASED ON MACHINE LEARNING

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

S. Schleibaum ,
S. Kehl ,
P. Stiefel  and
J. P. Müller
S. Schleibaum*
Affiliation:
Technische Universität Clausthal, Germany
S. Kehl
Affiliation:
Volkswagen AG, Germany
P. Stiefel
Affiliation:
Volkswagen AG, Germany
J. P. Müller
Affiliation:
Technische Universität Clausthal, Germany
*
*[email protected]

Abstract

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Modern machine learning methods have the potential to supply industrial product lifecycle management (PLM) with automated classification of product components. However, there is only little work in the literature on this topic. We propose to apply supervised machine learning on component meta-data. By analysing an industrial case study, we identify requirements and opportunities for automating classification, e.g. in part numbers and product structures. We validate our novel approach through a classification experiment comparing four machine learning methods on a realistic component dataset.

Keywords

product lifecycle management (PLM)machine learningclassificationproduct structureproduct data management (PDM)
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 375 - 382
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), 2020. Published by Cambridge University Press

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