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Data Driven Product Portfolio Analysis of Electric Motors Based on Product Platforms Using Knowledge-Based Systems

Part of: Knowledge-based Engineering

Published online by Cambridge University Press:  26 July 2019

Johann Tüchsen ,
Adrian-Cornel Pop ,
Matthias Koch ,
Benjamin Schleich  and
Sandro Wartzack
Johann Tüchsen*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg; Brose Fahrzeugteile GmbH & Co. KG
Adrian-Cornel Pop
Affiliation:
Brose Fahrzeugteile GmbH & Co. KG
Matthias Koch
Affiliation:
Brose Fahrzeugteile GmbH & Co. KG
Benjamin Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
*
Contact: Tuechsen, Johann, Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design, Germany, tuechsen@mfk.fau.de

Abstract

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For a company it is necessary to know, which products can be configured using carry-over-parts or the same technology. This can become quite relevant in the context of automobile electrification, where complex mechatronic systems are used. Consisting of various mechanical components, these systems perform the required function while being actuated by electronically controlled motors. To solve this, a novel mechanism for data driven portfolio analysis based on product platforms using knowledge-based systems is proposed in this paper. Further, the mechanism is tested by applying it to an electrical motors' case study. Using this method, all possible combinations of a product platform are calculated and finally displayed in different product portfolios. Additionally, all the non-feasible motor designs are removed from the solutions portfolio using the acquired knowledge base and performing design checks. The latter are employed for penalising and eliminating from the pareto-front of the designs, which violate the thermal, mechanical and acoustic constraints. The generated product portfolio can be used further to expand the systems engineering collaboration and support decision-making.

Keywords

Knowledge-Based SystemsElectric MotorsPlatform strategiesPortfolio managementKnowledge management
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2537 - 2546
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) 2019

References

Breitsprecher, T. Kestel, P. Küstner, C. Spruegel, T. and Wartzack, S. (2015) “The Use of Data Mining in Modern Product Development Processes. Holistic Research for Engineers of Tomorrow”, ZWF Zeitschrift fuer wirtschaftlichen Fabrikbetrieb, Vol. 110 No. 11, pp. 744–750. http://doi.org/10.3139/104.111423Google Scholar
Brose Fahrzeugteile GmbH and Co. KG (2018), Presentation of Business Division Drives, Würzburg, Germany.Google Scholar
Forza, C. and Salvador, F. (2002), “Managing for variety in the order acquisition and fulfilment process: The contribution of product configuration systems”, Int. J. Production Economics, Vol. 76, pp. 87–98. http://doi.org/10.1016/S0925-5273(01)00157-8Google Scholar
Grünig, R. and Kühn, R. (2018), “Portfolio Analysis”, In: Grünig, R. and Kühn, R. (Eds.), The Strategy Planning Process: Analyses, Options, Projects, Springer, Berlin Heidelberg, pp. 111–136. http://doi.org/10.1007/978-3-662-56221-5Google Scholar
Haug, A. Hvam, L. and Mortensen, N. H. (2011), “The Impact Of Product Configurators On Lead Times In Engineering-oriented Companies”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 25, pp. 197–206. http://doi.org/10.1017/s0890060410000636Google Scholar
Jiao, J. R. Simpson, T.W. and Siddique, Z. (2007), “Product family design and platform-based product development: a state-of-the-art review”, Journal of Intelligent Manufacturing, Vol. 18, pp. 5–29. http://doi.org/10.1007/s10845-007-0003-2Google Scholar
Jinsong, Z. Qifu, W. Li, W. and Yifang, Z. (2005), “Configuration-oriented product modelling and knowledge management for made-to-order manufacturing enterprises”, Int J Adv Manuf Technol, Vol. 25, pp. 41–52. http://doi.org/10.1007/s00170-003-1871-zGoogle Scholar
Kissel, M.P. (2014), “Mustererkennung in komplexen Produktportfolios”, Dissertation, Department of Mechanical Engineering, Technical University of Munich.Google Scholar
Kumar, D. Chen, W. and Simpson, T.W. (2009), “A market-driven approach to product family design” International Journal of Production Research, Vol. 47 No. 1, pp. 71–104. http://doi.org/10.1080/00207540701393171Google Scholar
La Rocca, G. (1997), “Knowledge Based Engineering: Between AI and CAD: Review of a Language Based Technology to Support Engineering Design”, Advanced Engineering Informatics, Vol. 26, pp. 159–179. http://doi.org/10.1016/j.aei.2012.02.002Google Scholar
Meyer, M.H. and Lehnerd, A.P. (1997), The Power of Product Platforms: Building Value and Cost Leadership, The Free Press, New York.Google Scholar
Miller, T.J.E. (2002), SPEED's Electric Motors: An outline of some of the theory in the SPEED software for electric machine design, University of Glasgow.Google Scholar
Ortlieb, C. and Runge, T. (2017), “Assessment of Modular Platform Potential in Complex Product Portfolios of Manufacturing Companies”, Proceedings of ICED 2017 / 21st International Conference on Engineering Design, Vancouver, Canada, August 21-25, 2017, pp. 131–139.Google Scholar
Ponn, J. (2015), “Portfolio Management for Electric Drives in Powertools at Hilti: Challenges and Solution Approaches” Proceedings of ICED 2015 / 20th International Conference on Engineering Design, Milan, Italy, July 27–30, 2015, pp. 105–114.Google Scholar
Pop, A.C. Gyselinck, J.J.C. Pinto, D.E. and Vintiloiu, I. (2017), “Optimization of Low-Power Brushless PM-Machines for Automotive Applications with Focus on High Volume Mass Production”, IEEE Transactions on Industrial Electronics, Vol. 64 No. 12, pp. 9767–9775. http://doi.org/10.1109/TIE.2017.2698367Google Scholar
Renner, I. (2007), “Methodische Unterstützung funktionsorientierter Baukastenentwicklung am Beispiel Automobil”, Dissertation, Department of Mechanical Engineering, Technical University of Munich.Google Scholar
Roach, G.M. and Cox, J.J. (2006), “A Case Study of the Product Design Generator”, In: Springer Simpson, T. W., Siddique, Z. and Jiao, J. R. (Eds.), Product Platform and Product Family Design: Methods and Applications, Springer, New York, pp. 499–512. http://doi.org/10.1007/0-387-29197-0_21Google Scholar
Simpson, T. W. Maier, J. R. and Mistree, F. (1999), “A Product Platform Concept Exploration Method for Product Family Design”, Proceedings of DETC99 / ASME Design Engineering Technical Conferences, Las Vegas, September 12-15, 1999.Google Scholar
Studer, R. Benjamins, V. and Fensel, D. (1998), “Knowledge Engineering: Principles and Methods”, Data and Knowledge Engineering, Vol. 25, pp. 161–197. http://doi.org/10.1016/s0169-023x(97)00056-6Google Scholar
Styczynski, Z.A. Rudion, K. and Naumann, A. (2017), Einführung in Expertensysteme: Grundlagen, Anwendungen und Beispiele aus der elektrischen Energieversorgung, Springer, Berlin Heidelberg. http://doi.org/10.1007/978-3-662-53172-3Google Scholar
Tüchsen, J. Koch, M. Schleich, B. and Wartzack, S. (2018a), “Konzeption eines Assistenzsystems für die modulare und datengetriebene Entwicklung elektrischer Antriebe”, 19. VDI-Kongress SIMVEC - Simulation und Erprobung in der Fahrzeugentwicklung, Baden-Baden, Germany, November 20-21, 2018, VDI Wissensforum GmbH, VDI-Report 2333, pp. 801–811.Google Scholar
Tüchsen, J. Pop, A.C. Koch, M. Schleich, B. and Wartzack, S. (2018b), “Data Driven Design Selection and Generation - an Industrial Case Study on Electric Motors”, Proceedings of the DESIGN 2018 / 15th International Design Conference, Dubrovnik, Croatia, May 21 – 24, 2018, pp. 1709–1720. http://doi.org/10.21278/idc.2018.0223Google Scholar
Tüchsen, J. Richter, M. Heydenreich, J. Koch, M. Schleich, B. and Wartzack, S. (2018c), “Knowledge-based and Calculation-supported Concept Validation in the Early Stages of Product Development Using the Example of the Electric Motor”, Proceedings of the 4th NAFEMS DACH Conference, Bamberg, Germany, May 14-16, 2018, pp. 244–248.Google Scholar
Ulrich, K. (1995), “The Role of Product Architecture in the Manufacturing Firm”, Research Policy, Vol. 24, pp. 419–440.10.1016/0048-7333(94)00775-3Google Scholar
Walter, M. Breitsprecher, T. Gruber, G. and Wartzack, S. (2011), “Simulation Based Generation of an Initial Design Taking into Account Geometric Deviations and Deformations”, Proceedings of ICED 2011 / 18th International Conference on Engineering Design, Copenhagen, Denmark, August 15-18, 2011, pp. 78–90.Google Scholar
Wang, Y. Ionel, D. Dorrell, D.G. and Stretz, S. (2015), “Establishing the Power Factor Limitations for Synchronous Reluctance Machines”, IEEE Transactions on Magnetics, Vol. 51, pp. 1–4. http://doi.org/10.1109/TMAG.2015.2443713Google Scholar