Emotion-Driven Design of New Energy Vehicle Wheel Hubs: Integrating Kansei Engineering and Generative Adversarial Networks

Wheel hubs have undergone new form changes as an essential part of New Energy Vehicles (NEV). At the same time, consumers' emotional preferences for the wheel hub design of NEV differ from those of traditional vehicles. However, designers' original knowledge and experience cannot be fully applied, which has limitations. Generative algorithms have been widely used in product design. Therefore, this paper proposes a form design method that combines Kansei Engineering (KE) and Generative Adversarial Networks (GAN) to investigate consumers' emotional preferences for wheel hub form design of NEV and provide designers and manufacturers with design insights. First, a wheel hub dataset is established by collecting and processing images from the website to train GAN and generate various design alternatives. Second, the experts deconstruct the wheel hub form into main features and screen several representative samples. Third, a Kansei questionnaire is distributed to investigate users' emotional preferences and satisfaction with the NEV wheel hub form. Next, the questionnaire data is analyzed and visualized to obtain the relationship between emotional preferences and design features to form a design guide. Finally, Design solutions are selected from the generated wheel hub alternatives according to the design guidelines to realize design automation. This study proposes a systematic method for NEV wheel hub form design, which provides theoretical and practical support for designers to meet user needs more efficiently.