Product designers need to fully understand consumers’ emotional preferences and responses for product forms to improve products. However, users and designers have different understandings and concepts in the product evaluation process, which will lead to cognitive asymmetry in the product design and evaluating process. This phenomenon prevents designers to grasp users’ needs, increasing the risk of product development failure. To this end, this paper proposes a product evaluation method that combines natural language processing techniques and fuzzy multi-criteria decision-making into a new integrated way to reduce the cognitive difference between users and designers, so as to solve the problem of cognitive asymmetry. This was done firstly by obtaining the review data of products from users on the Internet, based on a web crawler, and then constructing word vectors based on natural language processing techniques to realize the parametric expression of the Kansei image. Secondly, by using a statistical method to extract the product scheme that meets the preferences of users and designers, and then quantifying the relationship between the product form and Kansei image based on a grey relational analysis (GRA). Finally, by calculating the indicator weight based on the Entropy method and using the fuzzy TOPSIS method to explore the prioritization of the product design alternatives in view of the Kansei needs of users. Taking the smart capsule coffee machine as an example, the feasibility and effectiveness of this method are verified. In particular, the method proposed in this research can not only enable different cognitive subjects to achieve cognitive symmetry, but also filter out product forms that meet the cognitive needs of users. Moreover, this study provides a theoretical basis and practical significance for reducing the cognitive differences between cognitive subjects in the whole process of product design, and provides a systematic framework for the industry to effectively connect customer needs and product design decisions. At the same time, this study has introduced a new method for Kansei engineering.

With the popularization of the concept of sustainability in traditional wickerwork, wickerwork lamps have become the most popular production. When customers purchase wickerwork lamp products, the Kansei consensus has become a key factor influencing the communication between manufacturers and customers. Therefore, the purpose of this paper is to explore the product design solutions for wickerwork lamps that meet the emotional satisfaction of users. Firstly, a three-level evaluation grid diagram driven by user attractiveness through Miryoku Engineering is established. Secondly, this paper uses grey relational analysis (GRA) to extract the priority order and its weight values in the perceptual vocabulary to identify the key user needs in product design. In order to effectively deal with the uncertain product evaluation information, the fuzzy quality function deployment (QFD) is used to construct the “emotional demand-design parameter” transformation model and derive the optimal design parameters in the mapping process, thus effectively reducing the ambiguity and uncertainty in the demand transformation process. Based on the experimental results, it is found that the best combination of Texture light transmittance, Simple wickerwork material, Wickerwork primary colours, Cascaded type and Pastoral style could be preferred by customers, thus this proposed method can effectively reduce the ambiguity and uncertainty in the design process. The results of study enable designers to accurately grasp customers’ perceptions of wickerwork lamp products and obtain the best design parameters for wickerwork lamp products.

No abstract

Traditional craftsmanship and culture are facing a transformation in modern science and technology development, and the cultural industry is gradually stepping into the digital era, which can realize the sustainable development of intangible cultural heritage with the help of digital technology. To innovatively generate wickerwork pattern design schemes that meets the user’s preferences, this study proposes a design method of wickerwork patterns based on a style migration algorithm. First, an image recognition experiment using residual network (ResNet) based on the convolutional neural network is applied to the Funan wickerwork patterns to establish an image recognition model. The experimental results illustrate that the optimal recognition rate is 93.37% for the entire dataset of ResNet50 of the pattern design images, where the recognition rate of modern patterns is 89.47%, while the recognition rate of traditional patterns is 97.14%, the recognition rate of wickerwork patterns is 95.95%, and the recognition rate of personality is 90.91%. Second, based on Cycle-Consistent Adversarial Networks (CycleGAN) to build design scheme generation models of the Funan wickerwork patterns, CycleGAN can automatically and innovatively generate the pattern design scheme that meets certain style characteristics. Finally, the designer uses the creative images as the inspiration source and participates in the detailed adjustment of the generated images to design the wickerwork patterns with various stylistic features. This proposed method could explore the application of AI technology in wickerwork pattern development, and providing more comprehensive and rich new material for the creation of wickerwork patterns, thus contributing to the sustainable development and innovation of traditional Funan wickerwork culture. In fact, this digital technology can empower the inheritance and development of more intangible cultural heritages.

Protecting and inheriting local traditional handicrafts and developing them into characteristic handicraft industries plays a certain role in maintaining social harmony and stability. This study proposes an innovative design method for wickerwork patterns to achieve the sustainable development of wickerwork handicraft culture. In order to accurately grasp the emotional perception law of wickerwork handicraft patterns and creatively generate wickerwork pattern design schemes in accordance with the user’s emotional preference, a wickerwork pattern design method based on deep learning is proposed. Firstly, the image recognition model of the Funan wickerwork patterns is established by using the ResNet. The experimental results show that the best recognition rate of ResNet34 for the whole pattern design image dataset is 94.36%, the recognition rate of modern patterns is 95.92%, and the recognition rate of traditional wickerwork patterns is 93.45%. Secondly, based on deep convolution generative adversarial network (DCGAN), a design scheme generation model of Funan wickerwork patterns is built. DCGAN can automatically and creatively generate pattern design schemes that can effectively stimulate consumers’ emotional feelings. Finally, the designer uses creative pictures as a source of inspiration, innovates the design of the generated images, and designs wickerwork patterns with exquisite personality. This proposed method will increase the diversity of patterns and promote the sustainable development of traditional wickerwork techniques. Moreover, this proposed method can help design companies identify customers’ psychological needs and support designers in innovatively and efficiently creating new cultural innovation design solutions.