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MODELLING AND VISUALIZING KNOWLEDGE ON THE REFERENCE SYSTEM AND VARIATIONS BASED ON THE MODEL OF PGE – PRODUCT GENERATION ENGINEERING FOR DECISION SUPPORT

Published online by Cambridge University Press:  27 July 2021

Felix Pfaff*
Affiliation:
Karlsruhe Institute of Technology (KIT)
Simon Rapp
Affiliation:
Karlsruhe Institute of Technology (KIT)
Albert Albers
Affiliation:
Karlsruhe Institute of Technology (KIT)
*
Pfaff, Felix, Karlsruhe Institute of Technology (KIT), IPEK Institute of Product Engineering, Germany, felix.pfaff@kit.edu

Abstract

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In 2019, Dyson had to cancel its ambitious electric car project after having already 500 Million pounds spent. This example shows how difficult it is to assess the consequences of decisions on development targets as cost, risk, and innovation potential. Knowledge about references, variation types and their impact on development targets can help to increase the maturity of the decision basis. The model of PGE - product generation engineering describes these interrelations using the reference system. This contribution deals with the question of how knowledge about the impact of variation types and characteristics of reference system elements on new product generations can be made usable through modelling and visualization. Therefore, characteristics of reference system elements and their impacts on common development targets are collected through literature research. To process this knowledge base in technical information systems, an Entity-Relationship data model is developed. Through the implementation of a VR visualization, the data model is validated and a first visualisation approach is shown. The findings of this work can be used to systematise research on impact factors in PGE and to develop further digital methods.

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), 2021. Published by Cambridge University Press

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