Skip to main content
Log in

An evolutionary computation based method for creative design inspiration generation

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Product design is an important part of the manufacturing system from a macro point of view. In the design process, creative design is one of the important factors to survive the fierce competition. During creative design process, designers are the most critical component. However, design fixation often negatively influences the design outcomes. Many researches from cognitive science and design science reveal that the presentation of outer information can alleviate design fixation effectively. Among different kinds of outer information, language terms are proved to be effective. This work presents a method of automatically generating language terms as design inspirations based on 500,000 granted patents. This method adopts evolutionary computation as the fundamental algorithm to retrieve language terms from a vocabulary base. To implement the algorithm, the vocabulary is first encoded by high dimensional vectors through word embedding, which is a three layers neural network. Further, two metrics for measuring the inspiration potential of language terms are defined in a computable manner. This work also conducts experiments to validate the method, and the experimental results show that (1) the algorithm is efficient and has the potential to be extended to larger vocabulary; (2) the generated design inspirations have a positive influence on the design outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. The reader can see an example of calculating circular convolution of two vectors in “Appendix”.

  2. Because the length limitation, we will not show all data on the paper, and all the experimental data can be download from the url (https://www.dropbox.com/sh/gxyziztws9j69ew/AADorrFg3nFE01n11VEJaqmUa?dl=0).

  3. All the 20 groups of inspirations can be download from the url (https://www.dropbox.com/s/n3st1d39tsv489y/Design-Inspirations.pdf?dl=0).

References

  • Adams, R. S., Daly, S. R., Mann, L. M., & Dall’Alba, G. (2011). Being a professional: Three lenses into design thinking, acting, and being. Design Studies, 32(6), 588–607.

    Article  Google Scholar 

  • Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B. (2005). A functional representation for aiding biomimetic and artificial inspiration of new ideas. AIE EDAM, 19(02), 113–132.

    Google Scholar 

  • Chandra, C., & Kamrani, A. K. (2003). Knowledge management for consumer-focused product design. Journal of Intelligent Manufacturing, 14(6), 557–580.

    Article  Google Scholar 

  • Chandrasegaran, S. K., Ramani, K., Sriram, R. D., Horvth, I., Bernard, A., Harik, R. F., et al. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer Aided Design, 45(2), 204–228.

    Article  Google Scholar 

  • Cheng, P., Mugge, R., & Schoormans, J. P. L. (2014). A new strategy to reduce design fixation: Presenting partial photographs to designers. Design Studies, 35(4), 374–391.

    Article  Google Scholar 

  • Crawford, E., Gingerich, M., & Eliasmith, C. (2015). Biologically plausible, human-scale knowledge representation. Cognitive Science, 40(4), 412–417.

    Google Scholar 

  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  • Dorst, K. (2011). The core of ’design thinking’ and its application. Design Studies, 32(6), 521–532. doi:10.1016/j.destud.2011.07.006.

    Article  Google Scholar 

  • English, K., Naim, A., Lewis, K., Schmidt, S., Viswanathan, V., Linsey, J., et al. (2010). Impacting designer creativity through IT-enabled concept generation. Journal of Computing and Information Science in Engineering, 10(3), 031,007.

    Article  Google Scholar 

  • Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C., & Wood, K. (2013). The meaning of near and far: The impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design, 135(2), 021,007.

    Article  Google Scholar 

  • Gayler, R. W. (2004). Vector symbolic architectures answer Jackendoff’s challenges for cognitive neuroscience. arXiv preprint arXiv:cs/0412059.

  • Goldschmidt, G. (2011). Inspiring design ideas with texts. Design Studies, 32, 139–155.

    Article  Google Scholar 

  • Goldschmidt, G., & Smolkov, M. (2006). Variances in the impact of visual stimuli on design problem solving performance. Design Studies, 27(5), 549–569.

    Article  Google Scholar 

  • Gonalves, M., Cardoso, C., & Badke-Schaub, P. (2012). Find your inspiration: exploring different levels of abstraction in textual stimuli. In DS 73-1 proceedings of the 2nd international conference on design creativity (Vol. 1).

  • Gonçalves, M., Cardoso, C., & Badke-Schaub, P. (2016). Inspiration choices that matter: The selection of external stimuli during ideation. Design Science, 2(November), e10. doi:10.1017/dsj.2016.10.

    Article  Google Scholar 

  • Howard, T. J. (2008). Information management for creative stimuli in engineering design. PhD thesis, University of Bath.

  • Hundal, M. (1990). A systematic method for developing function structures, solutions and concept variants. Mechanism and Machine Theory, 25(3), 243–256.

    Article  Google Scholar 

  • Igwe, P. C., Knopf, G. K., & Canas, R. (2008). Developing alternative design concepts in VR environments using volumetric self-organizing feature maps. Journal of Intelligent Manufacturing, 19(6), 661–675.

    Article  Google Scholar 

  • Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3–11.

    Article  Google Scholar 

  • Jiao, J., Simpson, T. W., & Siddique, Z. (2007). Product family design and platform-based product development: A state-of-the-art review. Journal of Intelligent Manufacturing, 18(1), 5–29.

    Article  Google Scholar 

  • Jose, A., & Tollenaere, M. (2005). Modular and platform methods for product family design: Literature analysis. Journal of Intelligent Manufacturing, 16(3), 371–390.

    Article  Google Scholar 

  • Lawson, B., & Dorst, K. (2013). Design expertise. London: Routledge.

    Book  Google Scholar 

  • Li, Z., Raskin, V., & Ramani, K. (2008). Developing engineering ontology for information retrieval. Journal of Computing and Information Science in Engineering, 8(1), 504–505.

    Article  Google Scholar 

  • Liikkanen, L. A., & Perttula, M. (2010). Inspiring design idea generation: Insights from a memory-search perspective. Journal of Engineering Design, 21(5), 545–560.

    Article  Google Scholar 

  • Linsey, J. S., Tseng, I., Fu, K., Cagan, J., Wood, K. L., & Schunn, C. (2010). A Study of design fixation, its mitigation and perception in engineering design faculty. Journal of Mechanical Design, 132(4), 041,003.

    Article  Google Scholar 

  • Little, A., Wood, K., & McAdams, D. (1997). Functional analysis: A fundamental empirical study for reverse engineering, benchmarking and redesign. In Proceedings of the 1997 design engineering technical conferences (Vol. 97).

  • Lujun, Z. (2011). Design fixation and solution quality under exposure to example solution. In 2011 IEEE 2nd international conference on computing, control and industrial engineering (CCIE) (Vol. 1, pp. 129–132). IEEE

  • Mak, T. W., & Shu, L. H. (2008). Using descriptions of biological phenomena for idea generation. Research in Engineering Design, 19(1), 21–28. doi:10.1007/s00163-007-0041-y.

    Article  Google Scholar 

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (Vol. 26, pp. 3111–3119).

  • Murphy, J., Fu, K., Otto, K., Yang, M., Jensen, D., & Wood, K. (2014). Function based design-by-analogy: A functional vector approach to analogical search. Journal of Mechanical Design, 136(10), 101,102.

    Article  Google Scholar 

  • Nijstad, B. A., Stroebe, W., & Lodewijkx, H. F. (2003). Production blocking and idea generation: Does blocking interfere with cognitive processes? Journal of Experimental Social Psychology, 39(6), 531–548. doi:10.1016/S0022-1031(03)00040-4.

    Article  Google Scholar 

  • Pahl, G., & Beitz, W. (2013). Engineering design: A systematic approach. Berlin: Springer.

    Google Scholar 

  • Perttula, M., & Sipilä, P. (2007). The idea exposure paradigm in design idea generation. Journal of Engineering Design, 18(1), 93–102.

    Article  Google Scholar 

  • Qiu, Q. Y., Xue, C., Ji, Y., & Feng, P. E. (2013). Computer-aided innovative system of mechanical products based on patent knowledge. Computer Integrated Manufacturing Systems, 19(2), 354–361.

    Google Scholar 

  • Robbins, J. (2001). Engineers ask nature for design advice. The New York Times. p. F1

  • Rong, X. (2014). word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.

  • Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1–2), 159–216.

    Article  Google Scholar 

  • Stone, R. B., & Wood, K. L. (2000). Development of a functional basis for design. Journal of Mechanical Design, 122(4), 359–370.

    Article  Google Scholar 

  • Szykman, S., Racz, J. W., & Sriram, R. D. (1999). The representation of function in computer-based design. In Proceedings of the 1999 ASME design engineering technical conferences (11th international conference on design theory and methodology), Citeseer.

  • Tseng, H. E., & Li, R. K. (1999). A novel means of generating assembly sequences using the connector concept. Journal of Intelligent Manufacturing, 10(5), 423–435.

    Article  Google Scholar 

  • Tseng, I., Moss, J., Cagan, J., & Kotovsky, K. (2008). The role of timing and analogical similarity in the stimulation of idea generation in design. Design Studies, 29(3), 203–221.

    Article  Google Scholar 

  • Vasconcelos, L. A., & Crilly, N. (2016). Inspiration and fixation: Questions, methods, findings, and challenges. Design Studies, 42, 1–32.

    Article  Google Scholar 

  • Vattam, S. S., Helms, M. E., & Goel, A. K. (2010). A content account of creative analogies in biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24(04), 467–481.

    Article  Google Scholar 

  • Visser, W. (2006). The cognitive artifacts of designing. Mahwah: Lawrence Erlbaum Associates.

    Book  Google Scholar 

  • Yilmaz, S., Daly, S. R., Seifert, C. M., & Gonzalez, R. (2016). Evidence-based design heuristics for idea generation. Design Studies, 46, 95–124.

    Article  Google Scholar 

  • Zhou, Y., Xue, Q., Hao, J., & Liu, M. X. (2017). Research on the influence of abstract knowledge to the individual cognitive behavior and innovative design thinking. In K. S. Hale & K. M. Stanney (Eds.), Advances in neuroergonomics and cognitive engineering. Springer International Publishing.

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and thank the strong support provided by National Natural Science Foundation of China (NSFC 51505032), Beijing Natural Science Foundation (BJNSF 3172028) and National Ministries Projects of China (2015BAF18B01).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jia Hao.

Appendix: Example of circular convolution calculation

Appendix: Example of circular convolution calculation

Fig. 20
figure 20

A three dimensional example of novelty calculation

Suppose we have two vectors \(A=[1,2,3]\) and \(B=[1,4,3]\), the circular convolution can be calculated through the following steps.

Step 1::

Put vector \(A=[1,2,3]\) and \(B=[1,4,3]\) on two concentric circles, as shown in Fig. 20a.

Step 2::

Use the top element of the outer circle times every element on the inner circle, the get the first vector \(c_1\).

Step 3::

Rotate the outer circle by one step, as shown in Fig. 20b.

Step 4::

Use the top element of the outer circle times every element on the inner circle (put the result from second position), the get the second vector \(c_2\).

Step 5::

Rotate the outer circle by one step, as shown in Fig. 20c.

Step 6::

Use the top element of the outer circle times every element on the inner circle (put the result from the third position), the get the third vector \(c_3\).

Step 7::

Calculate circular convolution by sum \(c_1, c_2, c_3\), the result is [19, 15, 14].

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, J., Zhou, Y., Zhao, Q. et al. An evolutionary computation based method for creative design inspiration generation. J Intell Manuf 30, 1673–1691 (2019). https://doi.org/10.1007/s10845-017-1347-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1347-x

Keywords

Navigation