Xinxin Zhang

Existing Kansei-oriented product design studies often rely on simplified styling–color associations or weighted matching of perceptual imagery, making it difficult to capture users’ multi-affective requirements. To address this limitation, this study proposes a product form innovation design method based on a multi-domain coupling network model and establishes a mapping framework from target Kansei images to design solutions. Taking electric mopeds as a case study, the KJ method, k-means clustering, fuzzy Kano analysis, probabilistic hesitant fuzzy entropy weighting, and game-theoretic weighting were employed to identify the target Kansei image. Product form was then decomposed into styling, color, and material domains, and a multi-domain coupling network was constructed based on a form-element co-occurrence matrix. Through topological analysis and the LH-index, 14 key form elements associated with the target image were identified. Quantification Theory Type I was subsequently applied to establish the mapping relationship between form elements and Kansei images, while the Bee Evolutionary Genetic Algorithm (BEGA) was used to generate optimal design solutions. The results indicate that “elegance” is the dominant Kansei image for electric mopeds. The proposed model achieved strong predictive performance (R² = 0.923), and the optimized design obtained an 85% user recognition rate, showing high consistency across multiple validation methods. The proposed framework effectively reveals the coupling effects of styling, color, and material and provides a systematic approach for data-driven product form innovation.

While BioTRIZ is widely employed in biomimetic design to facilitate creative ideation and standardize workflows, accurately formulating domain conflicts and assessing design schemes during critical stages—such as initial concept development and scheme evaluation—remains a significant challenge. To address these issues, this study proposes an advanced BioTRIZ method. Firstly, the theory of technological evolution is integrated into the domain conflict identification stage, resulting in the development of a prompt framework based on patent analysis to guide large language models (LLMs) in verifying the laws of technological evolution (LTE). Building on these insights, domain conflicts encountered throughout the design process are formulated, and inventive principles with heuristic value, alongside standardized biological knowledge, are derived to generate conceptual solutions. Subsequently, a main parameter of value (MPV) model is constructed through mining user review data, and the evaluation of conceptual designs is systematically performed via the integration of orthogonal design and the fuzzy analytic hierarchy process to identify the optimal combination of component solutions. The optimization case study of a floor scrubber, along with the corresponding experimental results, demonstrates the efficacy and advancement of the proposed method. This study aims to reduce the operational difficulty associated with implementing BioTRIZ in product development processes, while simultaneously enhancing its accuracy.

In order to solve the problems that the combination mode of pattern elements in the decorative pattern design of grottoes was not flexible enough, the layout and adjustment of pictures in the design will be time-consuming and the overlapping of pattern primitives, a pattern generation method based on geometric similarity features is proposed. Firstly, it defined the pattern attributes and basic concepts. Secondly, Freeman chain code was used to describe the contour information of pattern primitives, and the longest common subsequence (LCS) was used to calculate the geometric similarity between pattern primitives. Based on the calculation results, the layout of pattern primitives was designed to complete the generation of subpatterns. Finally, the subpatterns were expanded according to the spatial distribution patterns detected and recognized. Experiments were carried out with decorative pattern primitives in Xiangtang Grottoes. The experimental results showed that the proposed method not only solves the problem of pattern primitive combination optimization, but also realizes the rapid generation and design of complex patterns. In addition, the matching time of the proposed method is 19.2 s, which is superior to 36.1 s of the artificial method.

Product Kansei image design is one of the research hotspot of product emotional design. Due to the subjectivity, low efficiency, and low level of intelligence in the existing product form innovation design methods in the mining of design goals. This study combines the semantic dictionary of Tongyici Cilin with Kansei engineering theory and uses clustering analysis algorithm, semantic difference method, and word similarity calculation method to realize product Kansei image design. Tongyici Cilin is a computable Chinese semantic dictionary. In this study, we innovatively introduced Tongyici Cilin into the image word similarity calculation in product image design. First, the product image design process based on Tongyici Cilin is proposed. Then, we establish a model of image word similarity calculation using the common distance, difference distance, common adjustment parameter, and differential adjustment parameter. By comparing with international standard data, it is confirmed that the image word similarity calculation model proposed in this article is effective and efficient. Using the sedan image design of middle-aged, middle-income men as an example, the sedan form style of each target image was successfully derived from the Internet-based questionnaire. Based on the case studies, we determined that it is effective to use the Tongyici Cilin semantic dictionary to determine the target image and improve the efficiency of product image design.

Product market competitiveness is positively influenced by the aesthetic value of product form, which is closely related to product complexity. By measuring the cognitive complexity of the product, this research establishes the relationship between the complexity and aesthetics of the product using an artificial neural network. Hence the prediction of product beauty is achieved, which guides design decisions. In this article, the complexity of product form is first measured through a combination of hesitant‐fuzzy theory and information axiom. Afterward, the result is weighted by exponential entropy and dimensionally compressed. This method makes data more suitable for the prediction with small samples, obtaining an accuracy improvement of up to 40% compared with traditional approaches. Finally, the importance order of the design elements which affect morphological complexity is acquired. Results show that three of the six complexity features (element number, object intelligence, and object detail) are more significant, impacting the aesthetic feeling of product form. The method increases the attractiveness of products to customers, providing valuable design support for enterprises and designers in the early days when a new product is designed, and reducing research and development risks.

With the great progress of product development technology, product forms have been greatly enriched by cognitive differences; users and designers have formed a “cognitive friction” phenomenon in the product evaluation process, which results in designers being unable to grasp user emotions accurately and risks of product development failure. This paper aims to balance the cognitive differences between cognitive subjects (users and designers) and evaluates the product image form. First, image entropy is used to evaluate and extract the weight of the product target image. Second, fuzzy Theil entropy is used to evaluate the cognitive friction between cognitive subjects, and its existence and size are visually presented. Then, a cognitive friction balance model is built by combining game theory, the comprehensive evaluation weight between cognitive subjects is obtained, and the product image form is ranked and optimized. Finally, all the research steps are described in the form of a household hair dryer. The results show that fuzzy Theil entropy and game theory have significant advantages in the evaluation and balance of cognitive friction in product design. Thus, the cognitive friction evaluation and balance model constructed from the fuzzy Theil entropy and game theory do not only enable different cognitive subjects to achieve cognitive symmetry, but also screen out product forms that meet the cognitive needs of users. This finding provides the theoretical basis and practical significance for the establishment of a closed-loop model in cognitive friction balance and the reduction of cognitive differences between cognitive subjects in the entire process of product design. It also introduces new ways of thinking and methods for cognitive science research.