A posterior preference articulation approach to Kansei engineering system for product form design

Yongfeng Li, Meng-Dar Shieh, Chih Chieh Yang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Understanding the needs of consumers is essential to the success of product design. Affective responses are a reflection of affective needs, often encompassing many aspects. Therefore, the process of designing products capable of satisfying multiple affective responses (MARs) falls into the category of multi-objective optimization (MOO). To solve the MOO problem, most existing approaches require the information for decision-making before or during the solving process, which limits their usefulness to designers or consumers. This paper proposes a posterior preference articulation approach to Kansei engineering system aimed at optimizing product form design to deal with MARs simultaneously. Design analysis is first used to identify design variables and MARs. Based on these results, a MOO model that involves maximizing MRAs is constructed. An improved version of the strength Pareto evolutionary algorithm (SPEA2) is applied to solve this MOO model so as to obtain Pareto solutions. After that, the Choquet fuzzy integral, which has the ability to take into account the interaction among the MARs, is employed to determine the optimal design from the Pareto solutions in accordance with the consumer preference. A case study involving the design of a vase form was conducted to illustrate the proposed approach. The results demonstrate that this approach can effectively obtain the optimal design solution, and be used as a universal approach for optimizing product form design concerning MARs.

Original languageEnglish
Pages (from-to)3-19
Number of pages17
JournalResearch in Engineering Design
Volume30
Issue number1
DOIs
Publication statusPublished - 2019 Jan 9

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Systems engineering
Multiobjective optimization
Product design
Evolutionary algorithms
Decision making
Optimal design

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Architecture
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

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title = "A posterior preference articulation approach to Kansei engineering system for product form design",
abstract = "Understanding the needs of consumers is essential to the success of product design. Affective responses are a reflection of affective needs, often encompassing many aspects. Therefore, the process of designing products capable of satisfying multiple affective responses (MARs) falls into the category of multi-objective optimization (MOO). To solve the MOO problem, most existing approaches require the information for decision-making before or during the solving process, which limits their usefulness to designers or consumers. This paper proposes a posterior preference articulation approach to Kansei engineering system aimed at optimizing product form design to deal with MARs simultaneously. Design analysis is first used to identify design variables and MARs. Based on these results, a MOO model that involves maximizing MRAs is constructed. An improved version of the strength Pareto evolutionary algorithm (SPEA2) is applied to solve this MOO model so as to obtain Pareto solutions. After that, the Choquet fuzzy integral, which has the ability to take into account the interaction among the MARs, is employed to determine the optimal design from the Pareto solutions in accordance with the consumer preference. A case study involving the design of a vase form was conducted to illustrate the proposed approach. The results demonstrate that this approach can effectively obtain the optimal design solution, and be used as a universal approach for optimizing product form design concerning MARs.",
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A posterior preference articulation approach to Kansei engineering system for product form design. / Li, Yongfeng; Shieh, Meng-Dar; Yang, Chih Chieh.

In: Research in Engineering Design, Vol. 30, No. 1, 09.01.2019, p. 3-19.

Research output: Contribution to journalArticle

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