The success of a new product is usually determined not by whether it includes high-end technology, but by whether it meets consumer expectations, especially key Kansei demands. This article aims to evaluate attractive factors (Kansei words) and convert them to design elements to make products stand out in the global competition. The evaluation grid method (EGM) is an important research method of Miryoku engineering. The method can build qualitative relations among consumers’ attractive factors and design elements. The quality function deployment (QFD) is a quantitative method which converts customer requirements into engineering characteristics using the House of Quality Matrix. The QFD together with the concept of fuzziness can objectively measure questionnaires made by experts. Accordingly, this paper proposes a systematic approach that integrates the EGM together with the fuzzy QFD for the development of new products. The fuzzy Kano model combined with the fuzzy analytic hierarchy process (AHP) is developed to determine the priority of the development of attractive factors. This empirical study uses minicars as an example to verify the feasibility and validity of the approach. The results are expected to help designers to increase design efficiency and improve consumer satisfaction of new products.

The fuzzy Kano model (FKM) adopts a linear scoring system that cannot accurately reflect the relationship between users' needs and satisfaction and may underestimate the importance of users' evaluation and needs. In this paper, we propose a preference-based evaluation-fuzzy-quantification method to determine the priority of the development of attractive factors of an electric scooter. In the evaluation analysis stage, the evaluation grid diagrams of all the interviewees are systemized. In the fuzzy computing stage, the continuous fuzzy Kano model (C-FKM) combined with the fuzzy analytic hierarchy process (FAHP) is developed to determine the priority of the development of attractive factors. By processing the ambiguity of users' needs, the C-FKM can obtain a more accurate representation of users' needs than the FKM. The opinions of 20 experts are integrated using the similarity aggregation method (SAM); then the FAHP is applied to calculate the weight of each evaluation criterion. Lastly, we use quantitative analysis to discover the important and specific characteristics that influence the attractiveness of an electric scooter. Research shows that Kansei images with attractive qualities are reliable and may be the first choice for satisfying the perceptual demands of consumers and can provide reference for the related research.

The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers’ perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design.

Product serialization design is an effective method for product family development. To explore the development law of product serialization, the kansei image change law within the series, and the interaction mechanisms of kansei image and form between the series, this paper proposes an evolutionary design method of product form inspired by Spider-webs. Inspired by the special structure and mechanical properties of Spider-webs in nature, at the macro level, we use the structure of the Spider-webs to describe the relationship between products in the product family. At the micro level, based on the mechanical properties of spiderwebs, we analyzed the development law of the product form within and between the series in the product family and then proposed a calculation method for the crossover coefficient and variation coefficient (in the genetic algorithm) for product form evolution. This method provides a scientific basis for determining the crossover coefficient and the variation coefficient from a new perspective. The case study results show that this model can effectively simulate the changing laws of design cognition and the evolution laws of product form, thereby providing a theoretical basis for the intelligent design of product form in a product family.

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