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New methodology for the characterization of 3D model reconstructions to meet conditions of input data and requirements of downstream application

Published online by Cambridge University Press:  16 May 2024

Robert Joost ,
Stephan Mönchinger  and
Kai Lindow
Robert Joost*
Affiliation:
Fraunhofer Institute for Production Systems and Design Technology IPK, Germany
Stephan Mönchinger
Affiliation:
Fraunhofer Institute for Production Systems and Design Technology IPK, Germany
Kai Lindow
Affiliation:
Fraunhofer Institute for Production Systems and Design Technology IPK, Germany
*
robert.joost@ipk.fraunhofer.de

Abstract

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In the field of 3D model reconstruction, manifold methods have been developed that derive CAD models from 3D scan data. Opposed to classical CAD modelling, where surface and solid modelling exist, a further diversification of modelling techniques is observed, caused by different methods to build up the geometry. This research introduces a new classification, the so-called Level of Complexities. It can be applied to the complete Reverse Engineering process chain and lays the foundation for further research on how to match requirements arising from all process steps and downstream applications.

Keywords

reverse engineeringcomputer-aided design (CAD)decision makingdesign methodologyprocess modelling

Information

Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 613 - 622
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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