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Data-driven support for CAD parts modelling based on automated estimated production planning – approach and user research

Published online by Cambridge University Press:  16 May 2024

Martin Erler ,
Sebastian Langula ,
Christian Wölfel ,
Julia Schneider ,
Christiane Kunath  and
Michael Königs
Martin Erler
Affiliation:
Technische Universität Dresden, Germany
Sebastian Langula
Affiliation:
Technische Universität Dresden, Germany
Christian Wölfel*
Affiliation:
Technische Universität Dresden, Germany
Julia Schneider
Affiliation:
Technische Universität Dresden, Germany
Christiane Kunath
Affiliation:
Technische Universität Dresden, Germany
Michael Königs
Affiliation:
FVP Aachen, Germany
*
[email protected]

Abstract

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We present a data-driven approach to support decision-making in CAD modelling and to improve design for manufacturing. Based on automated estimated production planning, information is provided on possible design actions and their impact. A study was conducted on perspectives on and visualizations in CAD modelling. Requirements for a user interface of the described support system were identified. The results serve as basis for further research and development on the interaction of engineering designers with data-driven decision-making support in CAD modelling.

Keywords

design for x (DfX)data-driven design supportdecision making
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 573 - 582
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), 2024.

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