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

Published online by Cambridge University Press:  26 July 2019

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.

Type
Article
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

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