IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5019588.html
   My bibliography  Save this article

Affective-Blue Design Methodology for Product Design Based on Integral Kansei Engineering

Author

Listed:
  • Wenwu Lian
  • Kun-Chieh Wang
  • Youchang Li
  • Hong-Yi Chen
  • Chi-Hsin Yang
  • M V A Raju Bahubalendruni

Abstract

Sustainable product designs always draw much attention. However, sustainable or green products are usually costly. This contradiction can be solved via blue design. The concept of blue design originates from the blue economy which is a popular strategy for providing sustainable, healthy, but cheap socioeconomic activities. This study innovatively implements the ideas of sustainability and economy from the blue economy, and the affection (or Kansei in Japanese) from the Kansei engineering into a product design process to become a novel affective-blue design methodology of a product form. The proposed methodology mainly contains three aspects. The first aspect is the merge of a novel Kansei blue model with the traditional Kansei engineering to deal with the semantic space and form decomposition issues encountered in the product form designing process. The second aspect is the adoption of proper data mining schemes to optimally trim and obtain the kernel information from the Kansei evaluation data of products. The third aspect is the usage of appropriate machine learning schemes to establish a precise relationship between product images and design elements from the kernel information. A case study was conducted for the form design of a computer-numerical-control lathe to evaluate the effectiveness of our proposed methodology. The verification results, that all predictive errors are within 4.5% for test samples, show that our blue-affective design methodology is quite satisfying. Through applying this proposed methodology, designers may correctly evaluate and easily catch the essential blue and affective design factors for designing a good industrial product, such as a computer-numerical-control machine tool.

Suggested Citation

  • Wenwu Lian & Kun-Chieh Wang & Youchang Li & Hong-Yi Chen & Chi-Hsin Yang & M V A Raju Bahubalendruni, 2022. "Affective-Blue Design Methodology for Product Design Based on Integral Kansei Engineering," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, April.
  • Handle: RePEc:hin:jnlmpe:5019588
    DOI: 10.1155/2022/5019588
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5019588.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5019588.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/5019588?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:5019588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.