A Data-Driven Methodology for Industrial Design Optimization and Consumer Preference Modeling: An Application of Computer-Aided Design in Sustainable Refrigerator Design Research

Addressing the insufficient identification of key consumer requirements in refrigerator design and the current limitations in understanding the impacts and underlying mechanisms of product design on sustainability, this study develops an interdisciplinary methodological framework that synergizes industrial design principles with advanced computer-aided design techniques and deep neural network approaches. Initially, consumer decision preferences concerning essential product attributes and sustainability indicators are systematically elucidated through semi-structured interviews and multi-source data fusion, with a particular emphasis on user sensitivity to energy efficiency ratings, based on a high-quality sample of 303 respondents. Subsequently, a latent diffusion model alongside a ControlNet architecture is employed to intelligently generate design solutions, followed by comprehensive multi-attribute optimization screening using an integrated decision-making model. The empirical evidence reveals that the synergistic interplay between functional rationality and design coordination plays a critical role in determining the overall competitiveness of the design solutions. Furthermore, by incorporating established industrial design practices, prototypes of mini desktop and vehicle-mounted multifunctional refrigerators—derived from neural network-generated design features—are developed and assessed. Finally, a nonlinear predictive mapping model is constructed to delineate the relationship between industrial design characteristics and consumer appeal. The experimental results show that the proposed support vector regression model achieves a root mean square error of 0.0719 and a coefficient of determination of 0.8480, significantly outperforming the Bayesian regularization backpropagation neural network baseline. These findings validate the model’s predictive accuracy and its applicability in small-sample, high-dimensional, and nonlinear industrial design scenarios. This research provides a data-driven, intelligent analytical approach that bridges industrial design with computer-aided design, thereby optimizing product market competitiveness and sustainable consumer value while promoting both theoretical innovation and practical advancements in sustainable design practices.