Styling Imagery of Self-Propelled Harvester Based on Kansei Engineering

To enhance the design methodology of agricultural machinery and better align it with users' needs, Kansei engineering is utilized to analyze the internal correlation of the perceptual imagery of self-propelled harvesters and the impact of styling elements on this imagery.The SD (semantic difference) scale is employed to investigate the perceptual imagery of typical samples.Principal component analysis (PCA) is conducted using SPSS software to identify the primary factors influencing the appearance styling of self-propelled harvesters: style factor and utility factor.The styling characteristics of the harvester are derived from three perspectives: product structure, part proportion, and level of detail richness.The relationship between styling elements and perceptual imagery is established through the application of quantification theory type I.By examining the styling image of the self-propelled harvester, designers can determine the optimal styling direction based on user demand, thereby enhancing the design process in terms of rigor and efficiency.