Abstract
Miryoku engineering is a design concept based on customer preferences, with the goal of creating attractive products or spaces. However, traditional Miryoku engineering faces two main issues: (1) the upper Kansei factor ranks the weights by the number of mentions, but it does not represent the importance of customers; (2) the mapping connection between the upper Kansei factor and the lower specific conditions adopts a statistical analysis method, which easily leads to the omission of key information. With the development of computer-based artificial intelligence, it repeatedly simulates human thinking with simple calculation rules, which has the advantages of fewer errors and faster speed. Therefore, on the three-level evaluation grid diagram platform established by Miryoku engineering, this paper first uses grey relationship analysis to comprehensively evaluate the priority order of Kansei words. Secondly, for the key Kansei factors, a morphological deconstruction table that connects the original reasons and specific conditions is established. Orthogonal design is used to screen representative combinations of design elements and create sample models by using the 3D software. Finally, the neural network was used to establish a mapping function between the key Kansei factors and the representative product design elements, and based on this, the most perceptually attractive product design was discovered. As a case study, the automobile booth was used to validate the effectiveness of the proposed method and significantly improve exhibitor design decisions and attendees' satisfaction.
Highlights
The advantages of Miryoku engineering, grey relationship analysis (GRA), and neural network (NN) are combined to develop an attractive product design. e proposed research framework is shown in Figure 2. e study is divided into three phases: First, the expert group goes through the interview process of Miryoku engineering for a 3-level evaluation grid chart that captures high-level abstract reasons, median original evaluation items, and low-level specific design conditions
In the fierce global auto sales market, an excellent automobile booth design can distinguish its own products from many homogeneous competitions, attract more visitors to stay, and increase product sales. e main purpose of this study is to develop an attractive automobile booth design combined with GRA and NN on the evaluation structure map platform established by Miryoku engineering
In artificial intelligence technology, GRA is used to replace the traditional frequency summing method, and the key Kansei factors are obtained by using the calculation method of the grey relationship degree
Summary
Expert interview Kansei evaluation Function evaluation Filter product design elements Synthesis is paper Wu and Cheng [16] Wu and Kang [23] Kang et al [5] Kang et al [8] Kang et al [24] Chen and Li [10] Shen [13] Zhang et al [15] Wang and Hsueh [25] Wang [26] Wang [27]. AHP, Kano model AHP, TRIZ TRIZ, FCPR. FWARM: fuzzy weighted association rule mining; TRIZ: theory of inventive problem solving; DEMATEL: decision-making trial and evaluation laboratory; RST: rough set theory; FCPR: fuzzy cognitive pairwise rating
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
