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ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

J. Sanchez ,
Z. Björkman  and
K. N. Otto
J. Sanchez
Affiliation:
Aalto University, Finland
Z. Björkman
Affiliation:
Aalto University, Finland
K. N. Otto*
Affiliation:
Aalto University, Finland
*
*[email protected]

Abstract

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Computer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.

Keywords

robust designcomputational design methodsdesign automationopen source design
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 365 - 374
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), 2020. Published by Cambridge University Press

References

Akihiro, T. et al. (2007), “A Conceptual Design Support Methodology Based on Structural Optimization Techniques Using Function-Oriented Elements”, Proceedings of the International Conference on Engineering Design, ICED’07, Paris, France, August 28-31, 2007.Google Scholar
Arena, M.V. et al. (2006), Historical Cost Growth of Completed Weapon System Programs, RAND Corporation, Santa Monica, CA.Google Scholar
Arvidsson, M., Gremyr, I. and Johansson, P. (2003), “Use and Knowledge of Robust Design Methodology: A Survey of Swedish Industry”, Journal of Engineering Design, Vol. 14 No. 2, pp. 129–143. https://doi.org/10.1080/0954482031000138192CrossRefGoogle Scholar
Arvidsson, M. and Gremyr, I. (2008), “Principles of Robust Design Methodology”, Quality and Reliability Engineering International, Vol. 24 No. 1, pp. 23–35. https://doi.org/10.1002/qre.864CrossRefGoogle Scholar
Baudin, M. (2015), pyDOE. https://github.com/tisimst/pyDOEGoogle Scholar
Chen, W., Jin, R. and Sudjianto, A. (2006), “Analytical Global Sensitivity Analysis and Uncertainty Propagation for Robust Design”, Journal of Quality Technology, Vol. 38 No. 4, pp. 333–348. https://doi.org/10.1080/00224065.2006.11918622CrossRefGoogle Scholar
Du, X. and Chen, W. (2001), “A most Probable Point-Based Method for Efficient Uuncertainty Analysis”, Journal of Design and Manufacturing Automation, Vol. 4 No. 1, pp. 47–66. https://doi.org/10.1080/15320370108500218CrossRefGoogle Scholar
Fang, K., Li, R. and Sudjianto, A. (2005), Design and Modelling for Computer Experiments, Chapman and Hall/CRC.CrossRefGoogle Scholar
Giunta, A., Wojtkiewicz, S. and Eldred, M. (2003), “Overview of Modern Design of Experiments Methods for Computational Simulations”, 41st Aerospace Sciences Meeting and Exhibit, Reno, NV, January 6-9, 2003. https://doi.org/10.2514/6.2003-649CrossRefGoogle Scholar
Goetz, S. et al. (2019), “Robustness Evaluation of Product Concepts based on Function Structures”, Proceedings of the Design Society: International Conference on Engineering Design. Cambridge University Press, Vol. 1 No. 1, pp. 3521–3530. https://doi.org/10.1017/dsi.2019.359CrossRefGoogle Scholar
Hasenkamp, T., Arvidsson, M. and Gremyr, I. (2009), “A Review of Practices for Robust Design Methodology”, Journal of Engineering Design, Vol. 20 No. 6, pp. 645–657. https://doi.org/10.1080/09544820802275557CrossRefGoogle Scholar
Herman, J. and Usher, W. (2017), “SALib: An Open-source Python Library for Sensitivity Analysis”, The Journal of Open Source Software, Vol. 2 No. 9. https://doi.org/10.21105/joss.00097CrossRefGoogle Scholar
Jin, R., Chen, W. and Simpson, T. (2001), “Comparative Studies of Metamodelling Techniques Under Multiple Modelling Criteria”, Structural and Multidisciplinary Optimization, Vol. 23 No. 1, pp. 1–13.CrossRefGoogle Scholar
Johansson, P. et al. (2006), “Variation Modes and Effect Analysis: A Practical Tool for Quality Improvement”, Quality and Reliability Engineering International, Vol. 22 No. 8, pp. 865–876. https://doi.org/10.1002/qre.773CrossRefGoogle Scholar
Kluyver, T. et al. (2016), “Jupyter Notebooks-a publishing format for reproducible computational workflows”, Proceedings of the 20th International Conference on Electronic Publishing. Amsterdam, pp. 87–90. https://doi.org/10.3233/978-1-61499-649-1-87CrossRefGoogle Scholar
Liu, S. and Han, J. (2017), “Energy efficient stochastic computing with Sobol sequences”, Proceedings of the Conference on Design, Automation & Test in Europe, European Design and Automation Association, Lausanne, Switzerland, March 27-31, pp. 650–653. https://doi.org/10.23919/DATE.2017.7927069CrossRefGoogle Scholar
Lundh, F. (2001), Python standard library, O'Reilly Media, Inc.Google Scholar
Matlab (2019), 2018. 9.7.0.1190202, The MathWorks Inc, Natick, Massachusetts.Google Scholar
Otto, K., Wang, J. and Uyan, T. (2019), “Using Open Source Code Libraries for Robust Design Analysis”, Proceedings of the International Conference on Engineering Design, pp. 1733–1742. https://doi.org/10.1017/dsi.2019.179CrossRefGoogle Scholar
Otto, K. and Sanchez, J. (2019), “Model Based Root Cause Analysis of Manufacturing Quality Problems Using Uncertainty Quantification and Sensitivity Analysis”, Proceedings of the Computers and Information in Engineering Conference, Anaheim, USA, August 18-21, Volume 1, pp. 18–21. https://doi.org/10.1115/DETC2019-97766CrossRefGoogle Scholar
Park, G. (2006), “Robust design: An overview”, AIAA Journal, Vol. 44 No. 1, pp. 181–191. https://doi.org/10.2514/1CrossRefGoogle Scholar
Robinson, T.J., Borror, C.M. and Myers, R.H. (2004), “Robust parameter design: A review”, Quality and Reliability Engineering International, Vol. 20, pp. 81–101. https://doi.org/10.1002/qre.602CrossRefGoogle Scholar
Saltelli, A. et al. (2008), Global Sensitivity Analysis: The Primer, John Wiley & Sons.Google Scholar
Sigurdarson, N., Eifler, T. and Ebro, M. (2019), “Functional Trade-offs in the Mechanical Design of Integrated Products-Impact on Robustness and Optimisability”, Proceedings of the 22nd International Conference on Engineering Design (ICED19), Delft, The Netherlands, 5-8 August 2019, Cambridge University Press, 1(1), pp. 3491–3500. https://doi.org/10.1017/dsi.2019.35CrossRefGoogle Scholar
Tan, J., Otto, K. and Wood, K. (2017), “Relative impact of early versus late design decisions in systems development”, Design Science, Vol. 3CrossRefGoogle Scholar
Taguchi, G., Chowdhury, S. and Taguchi, S. (2000), Robust Engineering, McGraw-Hill, New York. https://doi.org/10.1007/s13398-014-0173-7.2Google Scholar
Thornton, A.C. (1999), “Variation Risk Management using Modeling and Simulation”, ASME. Journal of Mechanical Design, Vol. 121 No. 2, pp. 297–304. https://doi.org/10.1115/1.2829457CrossRefGoogle Scholar
Ureili, I. (2010), Stirling Cycle Machine Analysis. www.ohio.edu/mechanical/stirling/Google Scholar
Viana, F.A. (2016), “A tutorial on Latin hypercube design of experiments”, Quality and Reliability Engineering International, Vol. 32 No. 5, pp. 1975–1985. https://doi.org/10.1002/qre.1924CrossRefGoogle Scholar
Vogel, S. et al. (2018), “Robust Design for Mechatronic Machine Elements-How Robust Design Enables the Application of Mechatronic Shaft-Hub Connection”, DS 92: Proceedings of the 15th International Design Conference, pp. 3033–3040. https://doi.org/10.21278/idc.2018.0203CrossRefGoogle Scholar