A novel product shape design method integrating Kansei engineering and whale optimization algorithm

The focus of consumer desire transitions from product functionality to emotional resonance in experience economy era, wherein emotional needs of users increasingly become a critical factor in product design. However, traditional approaches to product shape design often rely heavily on the designer’s intuition and experience, sometimes neglecting to incorporate emotional and humanistic elements into the product’s shape, thus resulting in inconsistencies in design results and quality. To address this challenge, this study introduces a novel method for emotionally driven product shape design that integrates Kansei engineering and the Whale Optimization Algorithm (WOA). This approach aims to fulfill consumer emotional demands related to product form and enhance overall user satisfaction. Firstly, the process utilized Python web crawlers to collect online product review texts and product images from e-commerce platforms. Next, Latent Dirichlet Allocation (LDA) and Analytic Hierarchy Process (AHP) were employed to extract representative emotional vocabularies, while representative samples were defined and deconstructed through clustering and morphological analysis. Then, semantic Differential (SD) questionnaires were distributed to collect consumer evaluations of product shape imagery, leading to the development of a user emotional prediction model for product shape. Then, WOA was introduced to optimize the performance of Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. Finally, Particle Swarm Optimization (PSO) and Seagull Optimization Algorithm (SOA) were employed to improve the prediction model, and the effect of these models was compared by the error method. This analysis explored the accuracy of these non-linear models in order to identify the optimal model for application in product form design cases. The scientific validity and effectiveness of this method were demonstrated utilizing whiskey bottle shape design as a case study. The results exhibit that under the WOA-BPNN model, the average error rates for four sets of perceptual words were 3.08%, 2.60%, 6.53%, and 5.70%, respectively. The WOA-based BPNN model outperformed other models in predictive ability, suggesting its utility as a valuable tool for designers during the front-end development stage of emotionally driven product form design.