Grey Relational Analysis of Flybridge Yacht Modeling Based on Kansei Engineering

ZHANG Yang, SONG Lei, LI Xue-lin, LIN Hai-hua, SUN Hong-yuan

Packaging Engineering ›› 2023, Vol. 44 ›› Issue (16) : 180-187.

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Packaging Engineering ›› 2023, Vol. 44 ›› Issue (16) : 180-187. DOI: 10.19554/j.cnki.1001-3563.2023.16.018

Grey Relational Analysis of Flybridge Yacht Modeling Based on Kansei Engineering

  • ZHANG Yang1, SONG Lei1, LI Xue-lin2, LIN Hai-hua3, SUN Hong-yuan3
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Abstract

The work aims to explore the evolution trend of flybridge yacht modeling and the importance priority of various modeling elements and improve the efficiency of modeling design. A yacht modeling design method based on Kansei engineering and grey relational model was proposed. Through collection of flybridge yacht samples and perceptual vocabulary, factor analysis and cluster analysis were used to divide the flybridge yacht modeling evolution into different stages, and the structure lines of 27 newer samples in one stage were extracted and summarized, and then grey relational analysis was applied to obtain the priority ranking of various modeling elements of flybridge yacht under different perceptual vocabulary. Designers could decide the priority of design under different perceptual vocabulary according to the importance of each modeling element. User-oriented product design can provide designers with reference data based on this method. At the same time, the stage division of yacht modeling evolution based on Kansei engineering can avoid the impact of modeling that is not accepted by users on subsequent results. While improving the design efficiency of flybridge yacht, it can better meet the psychological needs of users.

Key words

industrial design; Kansei engineering; grey relational analysis; flybridge yacht; modeling element

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ZHANG Yang, SONG Lei, LI Xue-lin, LIN Hai-hua, SUN Hong-yuan. Grey Relational Analysis of Flybridge Yacht Modeling Based on Kansei Engineering[J]. Packaging Engineering. 2023, 44(16): 180-187 https://doi.org/10.19554/j.cnki.1001-3563.2023.16.018
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