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THE INFLUENCE OF REPRESENTATION ON SYSTEM INTERPRETATION: A SEARCH FOR MOST COMMON SET PARTITIONS
Published online by Cambridge University Press: 19 June 2023
Abstract
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During engineering design, different representations are used to convey information about a systems' components, functionality, spatial layout, and interdependencies. These varying representations may have an impact on the interpretation of a system and consequently the decision-making process. This paper presents a research study that tries to capture these different interpretations by investigating how designers divide a system into subsystem clusters. These subsystem clusters can be considered partitions of a set-in combinatorial mathematics. Given designers' subsystem clusters for three products across three representation modalities, three different analysis methods for finding the most likely partition from observed data are presented. Analysis shows that the Variation of Information analysis method gives the most coherent and consistent results for the search of a most likely cluster. In addition, differences in clustering behaviour are observed based on representation modality. These results show that the way an engineer or designer chooses to represent a system impacts how that system is interpreted, which has implications for the decision-making process during engineering design.
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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
Abbott, D. and Lough, K.G. (2007) ‘Component Functional Templates as an Engineering Design Teaching Aid’, in 2007 ASEE Annual Conference & Exposition, pp. 12–388.CrossRefGoogle Scholar
Barlow, T. et al. (2021) ‘A System Design Optimization Model for Integrated Natural Resource Conservation and Development in an Agricultural Community’, Proceedings of the Design Society, 1, pp. 273–282. Available at: https://doi.org/10.1017/pds.2021.28.CrossRefGoogle Scholar
Buede, D.M. and Miller, W.D. (2016) ‘The Engineering Design of Systems: Models and Methods’.Google Scholar
Dieter, G.E. and Schmidt, L.C. (2009) Engineering Design. McGraw-Hill Higher Education Boston.Google Scholar
Erden, M.S. et al. (2008) ‘A Review of Function Modeling: Approaches and Applications’, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 22(2), pp. 147–169. Available at: https://doi.org/10.1017/S0890060408000103.CrossRefGoogle Scholar
Forte, S., Göbel, J.C. and Dickopf, T. (2021) ‘System of Systems Lifecycle Engineering Approach Integrating Smart Product and Service Ecosystems’, Proceedings of the Design Society, 1, pp. 2911–2920. Available at: https://doi.org/10.1017/pds.2021.552.CrossRefGoogle Scholar
Friedenthal, S., Alan, M. and Steiner, R. (2008) Practical Guide to SysML. Elsevier. Available at: https://doi.org/10.1016/B978-0-12-374379-4.X0001-X.Google Scholar
Gardner, M. (1978) ‘Bells-Versatile Numbers that can Count Partitions of a Set, Primes and Even Rhymes’, Scientific American, 238(5), pp. 24-+.CrossRefGoogle Scholar
Halmos, P. (1998) Naive Set Theory (Undergraduate Texts in Mathematics). New York: Springer.Google Scholar
Hirtz, J. et al. (2002) ‘A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts’, Research in engineering Design, 13(2), pp. 65–82.CrossRefGoogle Scholar
Kai, L.T. (1997) ‘Bell Numbers and Bell Numbers Modulo a Prime Number’, Mathematical Medley, 42(2), pp. 55–58.Google Scholar
Liem, A. (2017) ‘Extending System Design Tools to Incorporate User-and Contextual Elements in Developing Future Products and Services’, in DS 87-3 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 3: Product, Services and Systems Design, Vancouver, Canada, 21-25.08. 2017, pp. 161–170.Google Scholar
Meilă, M. (2007) ‘Comparing Clusterings—An Information Based Distance’, Journal of Multivariate Analysis, 98(5), pp. 873–895. Available at: https://doi.org/10.1016/j.jmva.2006.11.013.CrossRefGoogle Scholar
Murphy, A.R. et al. (2019) ‘Graduate Students’ Mental Models: An Investigation Into the Role of Function in Systems Understanding’, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, p. V007T06A024.Google Scholar
Murphy, A.R. et al. (2020) ‘Validation of a Mental Model Elicitation Instrument through Deployment of Control Groups in an Undergraduate Engineering Program’, in 2020 ASEE Virtual Annual Conference Content Access.Google Scholar
Murphy, A.R. (2021) Leveraging Design Knowledge: Strategies to Improve Systems Understanding. Georgia Institute of Technology.Google Scholar
Murphy, A.R. et al. (2022) ‘The Effects of Representation Modality on Subsystem Clustering’, in ASME 2022 International Design Engineering Technical Conferences. St. Louis, MO: ASME.CrossRefGoogle Scholar
Nagel, R.L. et al. (2015) ‘Improving Students’ Functional Modeling Skills: A Modeling Approach and a Scoring Rubric’, Journal of Mechanical Design, 137(5), p. 051102.CrossRefGoogle Scholar
Otto, K.N. and Wood, O.L. (2003) Product Design: Techniques in Reverse Engineering and New Product Development. 1st edn. Upper Saddle River Nj: Prentice Hall.Google Scholar
Pahl, G. et al. (2007) ‘Engineering Design: A Systematic Approach Third Edition’, Berlin, Springer Science+ Business Media Deutschland GmbH, 2007. 632 [Preprint].Google Scholar
Patel, A. et al. (2022) ‘Role of Representation in Subsystem Clustering: Effects of Distance Between Elements’, in ASME 2022 International Design and Technical Conferences. St. Louis, MO: ASME.CrossRefGoogle Scholar
Patel, A. and Summers, J.D. (2021) ‘Exploring the Effects of Individual Differences in Function Structure Modeling Behaviors’, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, p. V006T06A036.Google Scholar
Patrikainen, A. and Meila, M. (2006) ‘Comparing Subspace Clusterings’, IEEE Transactions on Knowledge and Data Engineering, 18(7), pp. 902–916. Available at: https://doi.org/10.1109/TKDE.2006.106.CrossRefGoogle Scholar
Qian, L. and Gero, J.S. (1996) ‘Function–Behavior–Structure Paths and Their Role in Analogy-Based Design’, AI EDAM, 10(4), pp. 289–312.Google Scholar
Rahman, M.H. et al. (2018) ‘Automatic Clustering of Sequential Design Behaviors’, in Volume 1B: 38th Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp. 1–14. Available at: https://doi.org/10.1115/DETC2018-86300.CrossRefGoogle Scholar
Rossi, G. (2011) ‘Partition Distances’, pp. 1–22. Available at: http://arxiv.org/abs/1106.4579.Google Scholar
Sanfeliu, A. and Fu, K.-S. (1983) ‘A Distance Measure Between Attributed Relational Graphs for Pattern Recognition’, IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(3), pp. 353–362. Available at: https://doi.org/10.1109/TSMC.1983.6313167.CrossRefGoogle Scholar
Shai, O. and Preiss, K. (1999) ‘Graph Theory Representations of Engineering Systems and their Embedded Knowledge’, Artificial Intelligence in Engineering, 13(3), pp. 273–285. Available at: https://doi.org/10.1016/S0954-1810(99)00002-3.CrossRefGoogle Scholar
Stauffer, M. et al. (2017) ‘A Survey on Applications of Bipartite Graph Edit Distance’, in Foggia, P., Liu, C.-L., and Vento, M. (eds) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer International Publishing (Lecture Notes in Computer Science), pp. 242–252. Available at: https://doi.org/10.1007/978-3-319-58961-9_22.Google Scholar
Stone, R.B. and Wood, K.L. (1999) ‘Development of a Functional Basis for Design’, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp. 261–275.CrossRefGoogle Scholar
Summers, J.D., Eckert, C. and Goel, A.K. (2017) ‘Function in Engineering: Benchmarking Representations and Models’, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 31(4), pp. 401–412. Available at: https://doi.org/10.1017/S0890060417000476.CrossRefGoogle Scholar
Wagner, R.A. and Fischer, M.J. (1974) ‘The String-to-String Correction Problem’, Journal of the ACM, 21(1), pp. 168–173. Available at: https://doi.org/10.1145/321796.321811.CrossRefGoogle Scholar
Waschull, S. et al. (2020) ‘Work Design in Future Industrial Production: Transforming Towards Cyber-Physical Systems’, Computers & Industrial Engineering, 139, p. 105679. Available at: https://doi.org/10.1016/j.cie.2019.01.053.CrossRefGoogle Scholar
Wichmann, R.L., Eisenbart, B. and Gericke, K. (2019) ‘The Direction of Industry: A Literature Review on Industry 4.0’, Proceedings of the Design Society: International Conference on Engineering Design, 1(1), pp. 2129–2138. Available at: https://doi.org/10.1017/dsi.2019.219.Google Scholar
Zeng, J. et al. (2020) ‘A Survey: Cyber-Physical-Social Systems and their System-Level Design Methodology’, Future Generation Computer Systems, 105, pp. 1028–1042. Available at: https://doi.org/10.1016/j.future.2016.06.034.CrossRefGoogle Scholar
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