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Knowledge Documentation Based on Automatic Identification and Clustering of Change Intentions in CAD Data of Wiring Harnesses

Published online by Cambridge University Press:  26 May 2022

K. Eder ,
W. Herzog ,
M. M. Altner ,
O. Tas ,
J. Neckenich  and
K. Paetzold
K. Eder*
Affiliation:
Mercedes-Benz AG, Germany Technische Universität Dresden, Germany
W. Herzog
Affiliation:
Mercedes-Benz AG, Germany
M. M. Altner
Affiliation:
Mercedes-Benz AG, Germany Karlsruhe Institute of Technology, Germany
O. Tas
Affiliation:
Mercedes-Benz AG, Germany
J. Neckenich
Affiliation:
Mercedes-Benz AG, Germany
K. Paetzold
Affiliation:
Technische Universität Dresden, Germany
*
[email protected]

Abstract

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High amount of changes and increasing complexity in CAD design of wiring harnesses result in a lack of time for documentation and transfer of acquired knowledge. To be able to transfer the gained knowledge efficiently during development automating the identification, analyzation and documentation of changes is necessary. This paper shows a methodology to address this challenge for CAD data of wiring harnesses. Thus, it is shown how interrelated change elements can be combined or separated from each other according to their change intention.

Keywords

design knowledgeknowledge managementknowledge representationscomputer-aided design (CAD)
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 683 - 692
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), 2022.

References

Ball, A., Patel, M., McMahon, C., Culley, S., Green, S. and Clarkson, J. (2006), “A Grand Challenge: Immortal Information and Through Life Knowledge Management (KIM)”, The International Journal of Digital Curation, 2006Google Scholar
Bracewell, R., Wallace, K., Moss, M. and Knott, D. (2009), “Capturing design rationale”, Computer-Aided Design, Vol. 41 No. 3, pp. 173–186. 10.1016/j.cad.2008.10.005Google Scholar
Camba, J., Contero, M., Johnson, M. and Company, P. (2014), “Extended 3D annotations as a new mechanism to explicitly communicate geometric design intent and increase CAD model reusability”, Computer-Aided Design, Vol. 57, pp. 61–73. 10.1016/j.cad.2014.07.001CrossRefGoogle Scholar
Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horváth, I., Bernard, A., Harik, R.F. and Gao, W. (2013), “The evolution, challenges, and future of knowledge representation in product design systems”, Computer-Aided Design, Vol. 45 No. 2, pp. 204–228. 10.1016/j.cad.2012.08.006CrossRefGoogle Scholar
Cho, J., Vosgien, T. and Gerhard, D. (2017), “Engineering Knowledge Extraction for Semantic Interoperability Between CAD, KBE and PLM Systems”, Vol. 517, pp. 568–579. 10.1007/978-3-319-72905-3_50CrossRefGoogle Scholar
Cho, J., Vosgien, T., Prante, T. and Gerhard, D. (2016), KBE-PLM Integration Schema for Engineering Knowledge Re-use and Design Automation, Vol. 492, Springer International Publishing, Cham. 10.1007/978-3-319-54660-5Google Scholar
Colombo, G., Pugliese, D., Klein, P. and Lutzemnberger, J. (2014), “A study for neutral format to exchange and reuse engineering knowledge in KBE applications”, 2014 International Conference on Engineering, Technology and Innovation (ICE), pp. 1–10. 10.1109/ICE.2014.6871565Google Scholar
Dworschak, F., Kügler, P., Schleich, B. and Wartzack, S. (2021), “Model and Knowledge Representation for the Reuse of Design Process Knowledge Supporting Design Automation in Mass Customization”, Applied Sciences, Vol. 11 No. 21, p. 9825. 10.3390/app11219825Google Scholar
Eder, K., Tas, O., Zielbauer, U. and Paetzold, K. (2022), “A Knowledge Management Approach to Support Concurrent Engineering in Wiring Harness Development”, in Canciglieri Junior, O., Noël, F., Rivest, L. and Bouras, A. (Eds.), Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations, IFIP Advances in Information and Communication Technology, Vol. 640, Springer International Publishing, Cham, pp. 68–79. 10.1007/978-3-030-94399-8_6Google Scholar
Gorski, F., Zawadazki, P. and Hamrol, A. (2016), “Knowledge based engineering as a condition of effective mass production of configurable products by design automation”, Journal of Machine Engineering, Vol. 16, No. 4Google Scholar
Hoisl, F., Shea, K. and Helms, B. (2008), “Towards representing, evolving and networking engineering knowledge for computational design synthesis”, The 10th International Design Conference on Proceedings DESIGN 2008 Dubrovnik, CroatiaGoogle Scholar
International Organization for Standardization (ISO) (2020), ISO 10303-242: Industrial automation systems and integration - Product data representation and exchange - Part 242: Application protocol: Managed model-based 3D engineeringGoogle Scholar
Kim, J., Pratt, M.J., Iyer, R.G. and Sriram, R.D. (2008), “Standardized data exchange of CAD models with design intent”, Computer-Aided Design, Vol. 40 No. 7, pp. 760–777. 10.1016/j.cad.2007.06.014Google Scholar
Kuhn, M. and Nguyen, H. (2019), “The future of harness development and manufacturing - Results from an expert case study”, Bordnetze im Automobil - 7. Internationaler Fachkongress, 2019Google Scholar
Lundin, M., Lejon, E., Dagman, A., Näsström, M. and Jeppsson, P. (2017), “Efficient Design Module Capture and Representation for Product Family Reuse”, Journal of Computing and Information Science in Engineering, Vol. 17 No. 3. 10.1115/1.4035673CrossRefGoogle Scholar
Neckenich, J. (2017), “3D-Master-Leitungssatz–Konzept zur Entwicklung von Leitungssätzen als 3D-Master in einem realistischen, vollständigen DMU-Modell”Google Scholar
Neckenich, J., Zielbauer, U., Winter, R. and Vielhaber, M. (2016), “An integrated approach for an extended assembly-oriented design of automotive wiring harness using 3D Master Models”, DS 84: Proceedings of the DESIGN 2016 14th International Design ConferenceGoogle Scholar
Oliveira, A., Kohwalter, T., Kalinowski, M., Murta, L. and Braganholo, V. (2020), “XChange: A semantic diff approach for XML documents”, Information Systems, Vol. 94, p. 101610. 10.1016/j.is.2020.101610Google Scholar
prostep ivip and Verband der Automobilindustrie (VDA) (2018), “prostep ivip VDA Recommendation: Harness Description List (KBL) Version 2.5”Google Scholar
prostep ivip and Verband der Automobilindustrie (VDA) (2020), “Vehicle Electric Container (VEC). Version 1.2”Google Scholar
Ritchie, J.M., Sung, R.C.W., Rea, H.J., Lim, T., Corney, J.R., Salamon, C. and Howley, I. (2008), “Automated Knowledge Capture in 2D and 3D Design Environments”, 2nd International Workshop Virtual Manufacturing VirMan 08 as part of the 5th INTUITION International Conference: Virtual Reality in Industry and Society: From Research to ApplicationGoogle Scholar
Smith, N. (2015), “Enhancing Automotive Wire Harness Manufacturing Through Digital Continuity”, SAE 2015 World Congress & Exhibition. 10.4271/2015-01-0238.Google Scholar
Stokes, M. (Ed.) (2001), Managing engineering knowledge: MOKA: methodology for knowledge based engineering applications, London, Bury St. EdmundsGoogle Scholar
Sung, R.C., Ritchie, J.M., Robinson, G., Day, P.N., Corney, J.R. and Lim, T. (2009), “Automated design process modelling and analysis using immersive virtual reality”, Computer-Aided Design, Vol. 41 No. 12, pp. 1082–1094. 10.1016/j.cad.2009.09.006Google Scholar
Sung, R.C.W., Ritchie, J.M., Lim, T. and Kosmadoudi, Z. (2012), “Automated generation of engineering rationale, knowledge and intent representations during the product life cycle”, Virtual Reality, Vol. 16 No. 1, pp. 69–85. 10.1007/s10055-011-0196-8CrossRefGoogle Scholar
Szykman, S., Sriram, R.D. and Regli, W.C. (2001), “The Role of Knowledge in Next-generation Product Development Systems”, Journal of Computing and Information Science in Engineering, Vol. 1 No. 1, pp. 3–11. 10.1115/1.1344238CrossRefGoogle Scholar
Verhagen, W.J., Bermell-Garcia, P., van Dijk, R.E. and Curran, R. (2012), “A critical review of Knowledge-Based Engineering: An identification of research challenges”, Advanced Engineering Informatics, Vol. 26 No. 1, pp. 5–15. 10.1016/j.aei.2011.06.004Google Scholar