Hostname: page-component-848d4c4894-mwx4w Total loading time: 0 Render date: 2024-06-20T03:20:08.843Z Has data issue: false hasContentIssue false
  • English
  • Français

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
*
*soeren.schleibaum@tu-clausthal.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Adolphy, S. et al. (2015), “Method for Automated Structuring of Product Data and its Applications”, Procedia CIRP, Vol. 38, pp. 153–158.10.1016/j.procir.2015.07.063CrossRefGoogle Scholar
Azur, M.J. et al. (2011), “Multiple Imputation by Chained Equations: What Is It and How Does It Work?”, International Journal of Methods in Psychiatric Research, Vol. 20 No. 1, pp. 40–49.10.1002/mpr.329CrossRefGoogle ScholarPubMed
Culler, D.E. and Anderson, N.D. (2016), “A Paradigm Shift towards Personalized and Scalable Product Development and Lifecycle Management Systems in the Aerospace Industry”, World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Vol. 10 No. 4, pp. 691–699.Google Scholar
Derrac, J. et al. (2011), “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and Evolutionary Computation, Vol. 1 No. 1, pp. 3–18.10.1016/j.swevo.2011.02.002CrossRefGoogle Scholar
Domingos, P. (2012), “A few useful things to know about machine learning”, Communications of the ACM, Vol. 55 No. 10, p. 78.10.1145/2347736.2347755CrossRefGoogle Scholar
Eigner, M., Hauff, M.V. and Schäfer, P.D. (2011), “Sustainable Product Lifecycle Management: A Lifecycle based Conception of Monitoring a Sustainable Product Development”, In: Hesselbach, J. and Herrmann, C. (Eds.), Glocalized Solutions for Sustainability in Manufacturing, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 501–506.10.1007/978-3-642-19692-8_87CrossRefGoogle Scholar
Eigner, M. and Stelzer, R. (2009), Product Lifecycle Management: Ein Leitfaden für Product Development und Life Cycle Management, VDI, 2., neu bearb. Aufl., Springer, Dordrecht.10.1007/b93672CrossRefGoogle Scholar
Eigner, M., Weidlich, R. and Zagel, M. (2005), The Conceptual The Conceptual Product Structure as Backbone of the Early Product Development Process, Science Days 2005, Darmstadt.Google Scholar
Fan, S., Lau, R.Y.K. and Zhao, J.L. (2015), “Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix”, Big Data Research, Vol. 2 No. 1, pp. 28–32.CrossRefGoogle Scholar
Huber, A.S. and Sendler, U. (2013), “Das Ziel Digital Enterprise: die professionelle digitale Abbildung von Produktentwicklung und Produktion”, In: Industrie 4.0, Xpert.press, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 111–124.Google Scholar
Kehl, S. (2019), Marken- und domänenübergreifendes Management industrieller Produktdaten, Springer Fachmedien Wiesbaden, Wiesbaden.10.1007/978-3-658-24449-1CrossRefGoogle Scholar
Kehl, S. et al. (2016), “Static Product Structures: An Industrial Standard on the Wane”, In: Harik, R., Rivest, L., Bernard, A., Eynard, B. and Bouras, A. (Eds.), Product Lifecycle Management for Digital Transformation of Industries, Springer International Publishing, Cham, pp. 69–78.CrossRefGoogle Scholar
Kehl, S., Stiefel, P. and Müller, J.P. (2015), “Changes on Changes: Towards an agent-based approach for managing complexity in decentralized product development”.Google Scholar
Komarek, P. (2004), Logistic Regression for Data Mining and High-Dimensional Classification, Pittsburgh, PA.Google Scholar
Louppe, G. (2014), “Understanding random forests: From theory to practice”, arXiv preprint arXiv:1407.7502.Google Scholar
Maillo, J. et al. (2017), “Exact fuzzy k-nearest neighbor classification for big datasets”, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 09.07.2017 - 12.07.2017, IEEE, pp. 1–6.Google Scholar
Morshedzadeh, I., Ng, A.H.C. and Amouzgar, K. (2019), “Management of Virtual Models with Provenance Information in the Context of Product Lifecycle Management. Industrial Case Studies”, In: Stark, J. (Ed.), Product Lifecycle Management (Volume 4): The Case Studies, Decision Engineering, Vol. 16, Springer International Publishing, Cham, pp. 153–170.10.1007/978-3-030-16134-7_13CrossRefGoogle Scholar
Schürr, A., Nagl, M. and Zündorf, A. (2008), Applications of Graph Transformations with Industrial Relevance, Vol. 5088, Springer Berlin Heidelberg, Berlin, Heidelberg.CrossRefGoogle Scholar
Stark, J. (2015), Product Lifecycle Management, Springer International Publishing, Cham.CrossRefGoogle Scholar
Tao, F. et al. (2018), “Digital twin-driven product design, manufacturing and service with big data”, The International Journal of Advanced Manufacturing Technology, Vol. 94 No. 9-12, pp. 3563–3576.CrossRefGoogle Scholar
Tekin, E. (2014), “A Method for Traceability and “As-built Product Structure” in Aerospace Industry”, Procedia CIRP, Vol. 17, pp. 351–355.10.1016/j.procir.2014.01.053CrossRefGoogle Scholar
Wu, X. et al. (2008), “Top 10 algorithms in data mining”, Knowledge and Information Systems, Vol. 14 No. 1, pp. 1–37.10.1007/s10115-007-0114-2CrossRefGoogle Scholar
Yiu Ip, C. and Regli, W.C. (2005), “Content-Based Classification of CAD Models with Supervised Learning”, Computer-Aided Design and Applications, Vol. 2 No. 5, pp. 609–617.10.1080/16864360.2005.10738325CrossRefGoogle Scholar