Convolutional Neural Network Models Combined with Kansei Engineering in Product Design

Abstract

This study aims to combine deep learning technology and user perception to propose an efficient design method that can meet the perceptual needs of users and enhance the competitiveness of products in the market. Firstly, the application development of sensory engineering and the research on sensory engineering product design by related technologies are discussed, and the background is provided. Secondly, the Kansei Engineering theory and the algorithmic process of the convolutional neural network (CNN) model are discussed, and theoretical and technical support is provided. A perceptual evaluation system is established for product design based on the CNN model. Finally, taking a picture of the electronic scale as an example, the testing effect of the CNN model in the system is analyzed. The relationship between product design modeling and sensory engineering is explored. The results show that the CNN model improves the “logical depth” of perceptual information of product design and gradually increases the abstraction degree of image information representation. There is a correlation between the user perception impression of electronic weighing scales of different shapes and the design effect of product design shapes. In conclusion, the CNN model and perceptual engineering have in‐depth application significance in the image recognition of product design and the perceptual combination of product design modeling. Combined with the CNN model of perceptual engineering, product design is studied. From the perspective of product modeling design, perceptual engineering has been deeply explored and analyzed. In addition, the product perception based on the CNN model can accurately analyze the correlation between product design elements and perceptual engineering and reflect the rationality of the conclusion.