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OPTIMIZING REQUIREMENTS FOR MAXIMUM DESIGN FREEDOM CONSIDERING PHYSICAL FEASIBILITY

Published online by Cambridge University Press:  19 June 2023

Eduardo Rodrigues Della Noce  and
Markus Zimmermann
Eduardo Rodrigues Della Noce*
Affiliation:
Technical University of Munich (TUM)
Markus Zimmermann
Affiliation:
Technical University of Munich (TUM)
*
Rodrigues Della Noce, Eduardo Technical University of Munich (TUM), Germany, eduardo.noce@tum.de

Abstract

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ABSTRACT

Solution spaces are sets of designs that meet all quantitative requirements of a given design problem, aiding requirement management. In previous works, ways of calculating subsets of the complete solution space as hyper-boxes, corresponding to a collection of permissible intervals for design variables, were developed. These intervals can be used to formulate independent component requirements with built-in tolerance. However, these works did not take physical feasibility into account, which has two disadvantages: first, solution spaces may be useless, when the included designs cannot be realized. Second, bad designs that are not physically feasible unnecessarily restrict the design space that can be used for requirement formulation.

In this paper, we present the new concept of a requirement space that is defined as the largest set of designs that (1) allows for decomposition (e.g., into intervals when it is box-shaped), (2) maximizes the useful design space (good and physically feasible), and (3) excludes the non-acceptable design space (bad and physically feasible). A small example from robot design illustrates that requirement spaces can be significantly larger than solution spaces and thus improve requirement decomposition.

Keywords

RequirementsComplexityConcurrent Engineering (CE)Solution SpacesDecomposition
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2865 - 2874
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), 2023. Published by Cambridge University Press

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