Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-26T03:21:00.878Z Has data issue: false hasContentIssue false
  • English
  • Français

TEXT MINING OF RESILIENT OBJECTS ABSORBING CHANGE AND UNCERTAINTY

Published online by Cambridge University Press:  19 June 2023

Massimo Panarotto ,
Vito Giordano ,
Filippo Chiarello ,
Arindam Brahma ,
Inigo Alonso Fernández  and
Gualtiero Fantoni
Massimo Panarotto*
Affiliation:
Chalmers University of Technology;
Vito Giordano
Affiliation:
University of Pisa
Filippo Chiarello
Affiliation:
University of Pisa
Arindam Brahma
Affiliation:
Chalmers University of Technology;
Inigo Alonso Fernández
Affiliation:
Chalmers University of Technology;
Gualtiero Fantoni
Affiliation:
University of Pisa
*
Panarotto, Massimo, Chalmers University of Technology, Sweden, [email protected]

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 current ways of coping with uncertainty such as changes during product design or use have been through methods such as easy restructuring (e.g., modularity with buffer in interface definition), by overdesign and so on. The present investments on maintaining products in the economy for “as long as possible” is challenging these strategies from a cost and environmental perspective. Moreover, these strategies often lead to highly overdesigned products. An alternative strategy is to introduce features in a design, called “resilient objects”, which are able to absorb such uncertainties without wasteful overdesign of other parts. By applying a ‘text-mining’ approach on patents, this paper has identified 5,552 candidates for such resilient objects that can be recombined and inserted in regions of the product that are likely to be most affected by current and future uncertainties. The application of resilient objects is demonstrated on a case study (a cooling system for battery electric vehicles). The case study highlights the ability of these objects to 1) significantly increase protection against uncertainties without the need for restructuring, 2 ) reduce the risk for overdesign and 3) dampen effects of change propagation.

Keywords

Resilient objectsSemantic data processingTech miningProduct architectureUncertainty
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3325 - 3334
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

Altan, T. and Miller, R.A., 1990. Design for forming and other near net shape manufacturing processes. CIRP annals, 39(2), pp.609–620.CrossRefGoogle Scholar
Andel, D. and Walsh, J., Corp, Nordson, 1999. Self-cleaning pressure relief and bypass valve, dispensing apparatus and method. U.S. Patent 5,984,148.Google Scholar
Bordoloi, S.K., Cooper, W.W. and Matsuo, H., 1999. Flexibility, adaptability, and efficiency in manufacturing systems. Production and Operations Management, 8(2), pp.133–150.CrossRefGoogle Scholar
Brahma, A. and Wynn, D.C., 2020. Margin value method for engineering design improvement. Research in Engineering Design, 31, pp.353–381.CrossRefGoogle Scholar
Cansler, E.Z., White, S.B., Ferguson, S.M. and Mattson, C.A., 2016. Excess identification and mapping in engineered systems. Journal of Mechanical Design, 138(8).CrossRefGoogle Scholar
Cascini, G. and Russo, D., 2007. Computer-aided analysis of patents and search for TRIZ contradictions. International Journal of Product Development, 4(1-2), pp.52–67.CrossRefGoogle Scholar
Cascini, G. and Zini, M., 2008. Measuring patent similarity by comparing inventions functional trees. In Computer-Aided Innovation (CAI) IFIP 20th World Computer Congress, Proceedings of the Second Topical Session on Computer-Aided Innovation, WG 5.4/TC 5 Computer-Aided Innovation, September 7-10, 2008, Milano, Italy (pp. 31-42). Springer US.CrossRefGoogle Scholar
Chalupnik, M.J., Wynn, D.C. and Clarkson, P.J., 2013. Comparison of ilities for protection against uncertainty in system design. Journal of Engineering Design, 24(12), pp.814–829.CrossRefGoogle Scholar
Chiarello, F., Melluso, N., Bonaccorsi, A. and Fantoni, G., 2019, July. A text mining based map of engineering design: Topics and their trajectories over time. In Proceedings of the design society: International conference on engineering design (Vol. 1, No. 1, pp. 2765-2774). Cambridge University Press.CrossRefGoogle Scholar
Choi, S., Yoon, J., Kim, K., Lee, J.Y. and Kim, C.H., 2011. SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics, 88(3), pp.863–883.CrossRefGoogle Scholar
Christopher, M. and Rutherford, C., 2004. Creating supply chain resilience through agile six sigma. Critical eye, 7(1), pp.24–28.Google Scholar
Clarkson, P.J., Simons, C. and Eckert, C., 2004. Predicting change propagation in complex design. J. Mech. Des., 126(5), pp.788–797.CrossRefGoogle Scholar
Crawley, E., de Weck, O., Eppinger, S., Magee, C., Moses, J., Seering, W., Schindall, J., Wallace, D. and Whitney, D., 2004. Engineering systems monograph. Massachusetts Institute of Technology, Tech. Rep.Google Scholar
De Neufville, R. and Scholtes, S., 2011. Flexibility in engineering design. MIT Press.CrossRefGoogle Scholar
Eckert, C., Isaksson, O. and Earl, C., 2019. Design margins: a hidden issue in industry. Design Science, 5, p.e9.CrossRefGoogle Scholar
Eckert, C., Isaksson, O., Hallstedt, S., Malmqvist, J., Rönnbäck, A.Ö. and Panarotto, M., 2019, July. Industry trends to 2040. In Proceedings of the Design Society: International Conference on Engineering Design (Vol. 1, No. 1, pp. 2121-2128). Cambridge University Press.Google Scholar
Eckert, C., Isaksson, O., Lebjioui, S., Earl, C.F. and Edlund, S., 2020. Design margins in industrial practice. Design Science, 6, p.e30.CrossRefGoogle Scholar
Fahmy, R., Kona, R., Dandu, R., Xie, W., Claycamp, G. and Hoag, S.W., 2012. Quality by design I: application of failure mode effect analysis (FMEA) and Plackett–Burman design of experiments in the identification of “main factors” in the formulation and process design space for roller-compacted ciprofloxacin hydrochloride immediate-release tablets. AAPS pharmscitech, 13, pp.1243–1254.CrossRefGoogle ScholarPubMed
Fantoni, G., Apreda, R., Dell'Orletta, F., and Monge, M., 2013. Automatic extraction of function–behaviour–state information from patents. Advanced Engineering Informatics, 27(3), pp.317–334.CrossRefGoogle Scholar
Fernández, I.A., Panarotto, M. and Isaksson, O., 2022. Designing Multi-Technological Resilient Objects in Product Platforms. DS 118: Proceedings of NordDesign 2022, Copenhagen, Denmark, 16th-18th August 2022, pp.1–12.Google Scholar
Fiksel, J., 2007. Sustainability and resilience: Toward a systems approach. IEEE Engineering Management Review, 35(3), pp.5–5.CrossRefGoogle Scholar
Freen, P.D., Corp, Cooper Cameron, 1997. Turbocharged natural gas engine control system. U.S. Patent 5,622,053.Google Scholar
Giordano, V., Chiarello, F., Melluso, N., Fantoni, G. and Bonaccorsi, A., 2021. Text and dynamic network analysis for measuring technological convergence: A case study on defense patent data. IEEE Transactions on Engineering Management.Google Scholar
Gupta, V. and Lehal, G.S., 2009. A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), pp.60–76.CrossRefGoogle Scholar
Hirtz, J., Stone, R.B., McAdams, D.A., Szykman, S. and Wood, K.L., 2002. A functional basis for engineering design: reconciling and evolving previous efforts. Research in engineering Design, 13, pp.65–82.CrossRefGoogle Scholar
Hollnagel, E., Nemeth, C.P. and Dekker, S. eds., 2008. Resilience engineering perspectives: remaining sensitive to the possibility of failure (Vol. 1). Ashgate Publishing, Ltd..Google Scholar
Jarratt, T.A.W., Eckert, C.M., Caldwell, N.H. and Clarkson, P.J., 2011. Engineering change: an overview and perspective on the literature. Research in engineering design, 22, pp.103–124.CrossRefGoogle Scholar
Jones, D.A., Eckert, C. and Garthwaite, P., 2020, May. Managing Margins: Overdesign in Hospital Building Services. In Proceedings of the Design Society: DESIGN Conference (Vol. 1, pp. 215-224). Cambridge University Press.Google Scholar
Liu, Y. and Huang, Y., 2018. Research on construction of patent dynamic technology effect matrix. International Journal of Innovative Computing, Information and Control, 14(3), pp.1133–1139.Google Scholar
Miller, C.M. and Bell, D.A., Thought Preserve, LLC, 2019. Sweep-flow, oil-dehydration apparatus and method. U.S. Patent 10,407,321.Google Scholar
Nadeau, D. and Sekine, S., 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), pp.3–26.CrossRefGoogle Scholar
Nyman, B.G., Hultholm, S.E., Lilja, L.L. and Makitalo, V.J., Oyj, Outokumpu, 1993. Method and apparatus for mixing and separating two liquid phases while preventing aeration and emulsions using a mixer-settler. U.S. Patent 5,185,081.Google Scholar
Otto, K., Hölttä-Otto, K., Simpson, T.W., Krause, D., Ripperda, S. and Ki Moon, S., 2016. Global views on modular design research: linking alternative methods to support modular product family concept development. Journal of Mechanical Design, 138(7), p.071101.CrossRefGoogle Scholar
Pieroni, M.P., McAloone, T.C. and Pigosso, D.C., 2019. Business model innovation for circular economy and sustainability: A review of approaches. Journal of cleaner production, 215, pp.198–216.CrossRefGoogle Scholar
Ross, A.M., Rhodes, D.H. and Hastings, D.E., 2008. Defining changeability: Reconciling flexibility, adaptability, scalability, modifiability, and robustness for maintaining system lifecycle value. Systems engineering, 11(3), pp.246–262.CrossRefGoogle Scholar
Stahel, W.R., 2016. The circular economy. Nature, 531(7595), pp.435–438.CrossRefGoogle ScholarPubMed
Tackett, M.W., Mattson, C.A. and Ferguson, S.M., 2014. A model for quantifying system evolvability based on excess and capacity. Journal of Mechanical Design, 136(5), p.051002.CrossRefGoogle Scholar
Thunnissen, D.P., 2005. Propagating and mitigating uncertainty in the design of complex multidisciplinary systems. California Institute of Technology.Google Scholar
Ulrich, K., 1995. The role of product architecture in the manufacturing firm. Research policy, 24(3), pp.419–440.CrossRefGoogle Scholar
Yamagishi, T. and Ishikura, T., 2018. Development of Electric Powertrain for Clarity Plug-in Hybrid. SAE International Journal of Alternative Powertrains, 7(3), pp.323–334.Google Scholar
Yoon, J., Park, H. and Kim, K., 2013. Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis. Scientometrics, 94, pp.313–331.Google Scholar