Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Abstract

The market scale of electric shavers in China has reached ¥ 26.3 billion in 2021. Consumers currently place an increasing emphasis on the Kansei image conveyed by products rather than just concerning with functional satisfaction. To meet consumers’ expectations, the emotional message conveyed by product design is essential under multisensory channels. This research first collected 230 electric shavers samples and 135 pairs of consumers’ Kansei words, then reduced them into 34 representative samples using multidimensional scale and clustering analysis, with 4 groups of representative Kansei words selected via the expert group. Moreover, consumers’ Kansei images were evaluated via questionnaire using the semantic differential scales, with 416 valid samples acquired in total. Meanwhile, design elements of the samples (including item and category) were classified by ways of morphological analysis and audio software. At last, the prediction models of the electric shavers were established between the overall design elements and user’s Kansei evaluation under the multisensory channel of visual model and auditory audio taking advantage of Quantification Theory Type I , back propagation neural network, and genetic algorithm-based BPNN. The proposed models can provide defined design indexes and references in multisensory design, facilitating designers to design in a logical and scientific manner rather than designing as per experience.