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PAVED: Pareto Front Visualization for Engineering Design

: Cibulski, Lena; Mitterhofer, Hubert; May, Thorsten; Kohlhammer, Jörn

Volltext urn:nbn:de:0011-n-5995987 (8.4 MByte PDF)
MD5 Fingerprint: 7539465b76221dd8ade4c02ab9733c6c
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Erstellt am: 25.11.2020

Computer graphics forum 39 (2020), Nr.3, S.405-416
ISSN: 0167-7055
ISSN: 1467-8659
Conference on Visualization (EuroVis) <22, 2020, Online>
European Commission EC
H2020; 768892; CloudiFacturing
Cloudification of Production Engineering for Predictive Digital Manufacturing
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IGD ()
Lead Topic: Digitized Work; Research Line: Computer graphics (CG); Research Line: Modeling (MOD); human-centered computing; Visual analytics; engineering

Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto-optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most-preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi-criteria alternatives. We reflect on our user-centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real-world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi-criteria optimization problems in engineering design or alternative domains.