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.
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Notes
The reader can see an example of calculating circular convolution of two vectors in “Appendix”.
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).
All the 20 groups of inspirations can be download from the url (https://www.dropbox.com/s/n3st1d39tsv489y/Design-Inspirations.pdf?dl=0).
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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).
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Appendix: Example of circular convolution calculation
Appendix: Example of circular convolution 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::
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Put vector \(A=[1,2,3]\) and \(B=[1,4,3]\) on two concentric circles, as shown in Fig. 20a.
- Step 2::
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Use the top element of the outer circle times every element on the inner circle, the get the first vector \(c_1\).
- Step 3::
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Rotate the outer circle by one step, as shown in Fig. 20b.
- Step 4::
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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::
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Rotate the outer circle by one step, as shown in Fig. 20c.
- Step 6::
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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::
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Calculate circular convolution by sum \(c_1, c_2, c_3\), the result is [19, 15, 14].
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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
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DOI: https://doi.org/10.1007/s10845-017-1347-x