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Research On Dermal Sensation In Products Design Based On Kansei Engineering

Posted on:2022-06-23Degree:MasterType:Thesis
Country:ChinaCandidate:N WangFull Text:PDF
GTID:2492306560464224Subject:Industrial design engineering
Abstract/Summary:PDF Full Text Request
Dermal sensation is a sense form with the largest receptor among the five senses of the human body.Design at the level of dermal sensation plays an important role in improving consumer experience.This topic hopes to explore the design elements related to human dermal sensation in product design,make up for the lack of research on dermal sensation related elements in current product design field,and provide research ideas for reference for the research on other sensory forms in the design field.Taking a laptop CMF design sample material as the carrier of research,applying Kansei Engineering research method,the dermal kansei experiment is designed.The Evaluative Grid Method is used for dermal kansei image survey,the Semantic Difference Method is used for dermal kensei quantitative measurement,experimental measurement is used for material physical parameter quantitative measurement.Finally,the relationship network of physical parameters,physiological dermal kansei and psychological dermal kansei was constructed,and the mapping model of dermal kansei evaluation and physical parameters was established by using Multiple Linear Regression method,and the model of dermal kansei engineering was evaluated and verified.The verification results show that the generated Kansei Engineering model is relatively reliable and can be used to predict dermal kansei in product design.The research conclusions of this paper can drive the development of product design to improve the multi-sensory experience,provide a new research method and prediction model for considering the factors related to dermal kansei in product design.,and provide a reference direction for CMF design research.
Keywords/Search Tags:Kansei Engineering, Dermal Kansei, Semantic Differential Method, Multiple Linear Regression
PDF Full Text Request
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