Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T07:16:57.171Z Has data issue: false hasContentIssue false
  • English
  • Français

COMPARISON BETWEEN EXPERIMENTATION AND MULTIPHYSICS MODELLING TO IDENTIFY PRIORITY CONTRADICTION

Published online by Cambridge University Press:  19 June 2023

Sebastien Dubois ,
Hicham Chibane  and
Roland De Guio
Sebastien Dubois*
Affiliation:
INSA Strasbourg; Icube, CSIP
Hicham Chibane
Affiliation:
INSA Strasbourg; Icube, CSIP
Roland De Guio
Affiliation:
INSA Strasbourg; Icube, CSIP
*
Dubois, Sebastien, INSA Strasbourg, France, sebastien.dubois@insa-strasbourg.fr

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The contradictions of TRIZ are now widespread and recognized as an effective inventive design tool. They make it possible to find solution concepts to problems that cannot be solved by optimization approaches. However, many contradictions could be formulated and it could be difficult to choose the priority one. The authors propose here two methods to formulate the contradictions and identify the priority contradiction: an experimental approach on the one hand, and a multiphysics approach on the other hand. This analysis, illustrated through an example of 3D printing of parts, shows that these two approaches are similar in terms of result, and indeed make it possible to formulate contradictions taking into account all the complexity of a system.

Keywords

CreativityOptimisationDesign methodsCase study
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1017 - 1026
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

References

Altshuller, G. S., Creativity as an Exact Science, 1984, New York, (Gordon and Breach).CrossRefGoogle Scholar
Box, G. E. P. & Draper, N. R., Response Surfaces, Mixtures, and Ridge Analyses, 2nd Edition, 2007).CrossRefGoogle Scholar
Box, G. E. P., Hunter, J. S. & Hunter, W. G., Statistics for Experimenters: Design, Innovation, and Discovery, 2005).Google Scholar
Box, G. E. P. & Wilson, K. B., On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society. Series B (Methodological), 1951, 13(1), 1–45.Google Scholar
Box, J. F., R, -. A. Fisher, the life of a scientist, 1978, New York, (Wiley).Google Scholar
Chibane, H., Dubois, S. & De Guio, R., Innovation beyond optimization: Application to cutting tool design. Computers & Industrial Engineering, 2021, 154.CrossRefGoogle Scholar
Dewey, J., Logic: The Theory of Inquiry. Mind, 1939, 48(192), 527–536.Google Scholar
Dubois, S., Chibane, H., Hafer, L. & Trillat, S., A Global Approach to Point Out Priority Problems Out of Experts’ Qualitative Data. 2021 Cham. Springer International Publishing, 401–413.Google Scholar
Dubois, S., Eltzer, T. & De Guio, R., A dialectical based model coherent with inventive and optimization problems. Computers in Industry, 2009, 60(8), 575–583.CrossRefGoogle Scholar
Dubois, S., Lin, L., De Guio, R. & Rasovska, I. 2015. From Simulation to Invention, beyond the Pareto-Frontier. In: CHRISTIAN WEBER, S. H., MARCO, CANTAMESSA, GAETANO, CASCINI, DORIAN, MARJANOVIC, GRAZIOSI, SERENA (ed.) 20th International Conference on Engineering Design (ICED 15). Milan, Italy.Google Scholar
Fisher, R. A., Statistical Methods for Research Workers, 1958, Edinburgh, (Oliver and Boyd).Google Scholar
Fisher, R. A., The Design of Experiments, 1966, New York, (Hafner Publishing Company).Google Scholar
Kackar, R. N., Control, Off-Line Quality, Parameter Design, and the Taguchi Method. Journal of Quality Technology, 1985, 17(4), 176–188.CrossRefGoogle Scholar
Khomenko, N., De Guio, R., Lelait, L. & Kaikov, I., A framework for OTSM-TRIZ-based computer support to be used in complex problem management. International Journal of Computer Applications in Technology, 2007, 30((1) spécial issue Trends in computer aided innovation), 88–104.CrossRefGoogle Scholar
Kiefer, J., Optimum Designs in Regression Problems II. Annals of Mathematical Statistics, 1961, .CrossRefGoogle Scholar
Lin, L., Dubois, S., De Guio, R. & Rasovska, I., An exact algorithm to extract the generalized physical contradiction. International Journal on Interactive Design and Manufacturing (IJIDeM), 2014, 9(3), 185–191.CrossRefGoogle Scholar
Lin, L., Rasovska, I., De Guio, R. & Dubois, S., Algorithm for identifying generalized technical contradictions in experiments. Journal Européen des Systèmes Automatisés (JESA), 2013, 47(4-8), 563–588.CrossRefGoogle Scholar
Rousselot, F., Zanni-Merk, C. & Cavallucci, D., Towards a formal definition of contradiction in inventive design. Computers in Industry, 2012, 63(3), 231–242.CrossRefGoogle Scholar
Simon, H. A., The structure of ill-structured problems. Artificial Intelligence, 1973, 4181–4201.Google Scholar
Souchkov, V. V. 2010. Root Conflict Analysis (RCA+): Structured Problems and Contradictions Mapping. Available: http://www.xtriz.com/RootConflictAnalysisIntroduction.pdf.Google Scholar
Taguchi, G. & Organization, A. P., Introduction to Quality Engineering: Designing Quality Into Products and Processes, 1986, (Asian Productivity Organization).Google Scholar
Taguchi, G. & Wu, Y., Introduction to Off-line Quality Control, 1979, (Central Japan Quality Control Assoc.).Google Scholar