Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-22T04:21:37.480Z Has data issue: false hasContentIssue false
  • English
  • Français

A DATA-DRIVEN APPROACH FOR CREATIVE CONCEPT GENERATION AND EVALUATION

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

J. Han ,
H. Forbes ,
F. Shi ,
J. Hao  and
D. Schaefer
J. Han*
Affiliation:
University of Liverpool, United Kingdom
H. Forbes
Affiliation:
University of Liverpool, United Kingdom
F. Shi
Affiliation:
Amazon Web Services, United Kingdom
J. Hao
Affiliation:
Beijing Institute of Technology, China
D. Schaefer
Affiliation:
University of Liverpool, United Kingdom
*
*[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.

Conceptual design, as an early phase of the design process, is known to have the highest impact on determining the innovation level of design results. Although many tools exist to support designers in conceptual design, additional knowledge, especially knowledge related to emerging technologies, is still often needed. In this paper the authors aim to propose a data-driven creative concept generation and evaluation approach to support designers in incorporating emerging technologies in the new product early development stage. The approach is demonstrated by means of an illustrated example.

Keywords

conceptual designdesign creativityevaluationdata-driven designidea generation
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 167 - 176
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

Alstott, J. et al. (2017), “Inventors’ Explorations Across Technology Domains”, Design Science, Vol. 3, p. e20. https://doi.org/10.1017/dsj.2017.21CrossRefGoogle Scholar
Bertola, P. and Teixeira, J.C. (2003), “Design as a knowledge agent: How design as a knowledge process is embedded into organizations to foster innovation”, Design Studies, Vol. 24 No. 2, pp. 181–194. https://doi.org/10.1016/S0142-694X(02)00036-4CrossRefGoogle Scholar
Boden, M.A. (2004), The creative mind: Myths and mechanisms, 2 ed., Routledge, London, UK.CrossRefGoogle Scholar
Boden, M.A. (2009), “Computer models of creativity”, AI Magazine, Vol. 30 No. 3, pp. 23–34. https://doi.org/10.1609/aimag.v30i3.2254CrossRefGoogle Scholar
Brown, D.C. (2015), “Computational Design Creativity Evaluation”, in Design Computing and Cognition ‘14, Cham, Springer International Publishing. pp. 207–224.Google Scholar
Camburn, B. et al. (2020), “Machine Learning Based Design Concept Evaluation”, Journal of Mechanical Design, In Press. https://doi.org/10.1115/1.4045126CrossRefGoogle Scholar
Camburn, B. et al. (2019), “Evaluating Crowdsourced Design Concepts With Machine Learning”, In Proceedings of ASME IDETC-CIE 2019. https://doi.org/10.1115/DETC2019-97285CrossRefGoogle Scholar
Carruthers, P. (2011), “Creative action in mind”, Philosophical Psychology, Vol. 24 No. 4, pp. 437–461.CrossRefGoogle Scholar
Cash, P. and Štorga, M. (2015), “Multifaceted assessment of ideation: using networks to link ideation and design activity”, Journal of Engineering Design, Vol. 26, pp. 391–415. https://doi.org/10.1080/09544828.2015.1070813CrossRefGoogle Scholar
Chakrabarti, A. and Shu, L.H. (2010), “Biologically inspired design”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 24 No. 4, pp. 453–454. https://doi.org/10.1017/S0890060410000326CrossRefGoogle Scholar
Chan, J., Dow, S.P. and Schunn, C.D. (2015), “Do the best design ideas (really) come from conceptually distant sources of inspiration?”, Design Studies, Vol. 36 No. Supplement C, pp. 31–58. https://doi.org/10.1016/j.destud.2014.08.001CrossRefGoogle Scholar
Chan, J. et al. (2011), “On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples”, Journal of Mechanical Design, Vol. 133 No. 8, pp. 081004–081004-11. https://doi.org/10.1115/1.4004396CrossRefGoogle Scholar
Chen, L. et al. (2019), “An artificial intelligence based data-driven approach for design ideation”, Journal of Visual Communication and Image Representation, Vol. 61, pp. 10–22. https://doi.org/10.1016/j.jvcir.2019.02.009CrossRefGoogle Scholar
Childs, P.R.N. (2018), Mechanical design engineering handbook, 2nd ed, Butterworth-Heinemann, Oxford, UK.Google Scholar
Eling, K., Griffin, A. and Langerak, F. (2014), “Using Intuition in Fuzzy Front-End Decision-Making: A Conceptual Framework”, Journal of Product Innovation Management, Vol. 31 No. 5, pp. 956–972. https://doi.org/10.1111/jpim.12136CrossRefGoogle Scholar
Fu, K. et al. (2014), “Bio-Inspired Design: An Overview Investigating Open Questions From the Broader Field of Design-by-Analogy”, Journal of Mechanical Design, Vol. 136 No. 11, pp. 111102–111102-18. https://doi.org/10.1115/1.4028289CrossRefGoogle Scholar
Goucher-Lambert, K. and Cagan, J. (2019), “Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation”, Design Studies, Vol. 61, pp. 1–29. https://doi.org/10.1016/j.destud.2019.01.001CrossRefGoogle Scholar
Han, J., Forbes, H. and Schaefer, D. (2019), “An Exploration of the Relations between Functionality, Aesthetics and Creativity in Design”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 259–268. https://doi.org/10.1017/dsi.2019.29Google Scholar
Han, J. et al. (2018a), “The Combinator – a computer-based tool for creative idea generation based on a simulation approach”, Design Science, Vol. 4, p. e11. https://doi.org/10.1017/dsj.2018.7CrossRefGoogle Scholar
Han, J. et al. (2018b), “A computational tool for creative idea generation based on analogical reasoning and ontology”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 32 No. 4, pp. 462–477. https://doi.org/10.1017/S0890060418000082CrossRefGoogle Scholar
Han, J. et al. (2018c), “The conceptual distances between ideas in combinational creativity”, in DS92: Proceedings of the DESIGN 2018 15th International Design Conference, pp. 1857–1866. https://doi.org/10.21278/idc.2018.0264CrossRefGoogle Scholar
Hao, J., Zhao, Q. and Yan, Y. (2017), “A function-based computational method for design concept evaluation”, Advanced Engineering Informatics, Vol. 32, pp. 237–247. https://doi.org/10.1016/j.aei.2017.03.002CrossRefGoogle Scholar
He, Y. et al. (2019), “Mining and Representing the Concept Space of Existing Ideas for Directed Ideation”, Journal of Mechanical Design, Vol. 141, p. 121101. https://doi.org/10.1115/1.4044399CrossRefGoogle Scholar
Helms, M., Vattam, S.S. and Goel, A.K. (2009), “Biologically inspired design: process and products”, Design Studies, Vol. 30 No. 5, pp. 606–622. https://doi.org/10.1016/j.destud.2009.04.003CrossRefGoogle Scholar
Howard, T.J., Culley, S. and Dekoninck, E.A. (2011), “Reuse of ideas and concepts for creative stimuli in engineering design”, Journal of Engineering Design, Vol. 22 No. 8, pp. 565–581. https://doi.org/10.1080/09544821003598573CrossRefGoogle Scholar
Hsu, W. and Liu, B. (2000), “Conceptual design: issues and challenges”, Computer-Aided Design, Vol. 32 No. 14, pp. 849–850. https://doi.org/10.1016/S0010-4485(00)00074-9CrossRefGoogle Scholar
Jonson, B. (2005), “Design ideation: the conceptual sketch in the digital age”, Design Studies, Vol. 26 No. 6, pp. 613–624. https://doi.org/10.1016/j.destud.2005.03.001CrossRefGoogle Scholar
Luo, J., Sarica, S. and Wood, K. (2019), “Computer-Aided Ideation Using InnoGPS”, in Proceedings of ASME IDETC-CIE 2019. https://doi.org/10.1115/DETC2019-97587CrossRefGoogle Scholar
Luo, J. et al. (2018), “Design Opportunity Conception Using Technology Space Map”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 32 No. 4, pp. 449–461. https://doi.org/10.1017/S0890060418000094CrossRefGoogle Scholar
Lopez, R., Linsey, J.S. and Smith, S.M. (2011), “Characterizing the Effect of Domain Distance in Design-by-Analogy”, in Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 141–151. https://doi.org/10.1115/DETC2011-48428CrossRefGoogle Scholar
Oman, S.K. et al. (2013), “A comparison of creativity and innovation metrics and sample validation through in-class design projects”, Research in Engineering Design, Vol. 24 No. 1, pp. 65–92. https://doi.org/10.1007/s00163-012-0138-9CrossRefGoogle Scholar
Ozkan, O. and Dogan, F. (2013), “Cognitive strategies of analogical reasoning in design: Differences between expert and novice designers”, Design Studies, Vol. 34 No. 2, pp. 161–192. https://doi.org/10.1016/j.destud.2012.11.006CrossRefGoogle Scholar
Rus, V. et al. (2013), “Semilar: The semantic similarity toolkit”, in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 163–168.Google Scholar
Sarica, S., Luo, J. and Wood, K.L. (2020), “TechNet: Technology Semantic Network”, Expert Systems with Applications, Vol. 142, p. 112995.CrossRefGoogle Scholar
Sarica, S., Song, B., Luo, J. (2019), “Technology Knowledge Graph for Design Exploration: Application to Designing the Future of Flying Cars”, in Proceedings of ASME IDETC-CIE 2019. https://doi.org/10.1115/DETC2019-97605CrossRefGoogle Scholar
Sarkar, P. and Chakrabarti, A. (2011), “Assessing design creativity”, Design Studies, Vol. 32 No. 4, pp. 348–383.CrossRefGoogle Scholar
Shah, J.J. et al. (2003), “Empirical Studies of Design Ideation: Alignment of Design Experiments With Lab Experiments”, in Proceedings of the ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 847–856. https://doi.org/10.1115/DETC2003/DTM-48679CrossRefGoogle Scholar
Shi, F. et al. (2017), “A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval”, Journal of Mechanical Design, Vol. 139 No. 11, pp. 111402–111402-14. https://doi.org/10.1115/1.4037649CrossRefGoogle Scholar
Speer, R. and Havasi, C. (2012), “Representing general relational knowledge in ConceptNet 5”, in Proceedings of the Eight International Conference on Language Resources and Evaluation.Google Scholar
Ullman, D.G. (2010), The mechanical design process: Part 1, McGraw-Hill, New York, USA.Google Scholar
Yilmaz, S. et al. (2016), “Evidence-based design heuristics for idea generation”, Design Studies, Vol. 46 No. Supplement C, pp. 95–124. https://doi.org/10.1016/j.destud.2016.05.001CrossRefGoogle Scholar
Zhang, Z.-J. et al. (2017), “A quantitative approach to design alternative evaluation based on data-driven performance prediction”, Advanced Engineering Informatics, Vol. 32, pp. 52–65. https://doi.org/10.1016/j.aei.2016.12.009CrossRefGoogle Scholar