Lei Fu, Yiling Lei, Ling Zhu, Yuqi Yan, Jiufang Lv
{"title":"Integrating Kansei engineering with hesitant fuzzy quality function deployment for rosewood furniture design","authors":"Lei Fu, Yiling Lei, Ling Zhu, Yuqi Yan, Jiufang Lv","doi":"10.15376/biores.19.3.6403-6426","DOIUrl":null,"url":null,"abstract":"To enhance the scientific rigor of design decisions and develop new rosewood furniture that aligns with user emotions, this study integrates the strengths of the Hesitant Fuzzy Analytic Hierarchy Process (HFAHP) and Hesitant Fuzzy Quality Function Deployment (HFQFD) within the framework of Kansei Engineering (KE). This method accurately translates Consumer Requirements (CRs) into Engineering Characteristics (ECs). First, the KJ Method was used to screen and categorize Kansei words, create product sample images, and deconstruct the form of rosewood furniture using morphological analysis. Second, after collecting valid questionnaires using a 7-point Likert scale, Factor Analysis (FA) was employed to extract three key Kansei factors. Third, HFAHP was utilized to calculate the weights of the Kansei words. Fourth, HFQFD was applied to construct a hesitant fuzzy correlation matrix between CRs and ECs, determining the priority of design elements for rosewood furniture. Finally, using a square table as an example in the design practice, the optimal Scheme No. 9, which highly meets consumer emotional needs and features harmonious form combinations, was selected. This study enhances the emotional value of rosewood furniture, optimizes the design decision-making process, and improves contemporary consumer satisfaction.","PeriodicalId":9172,"journal":{"name":"Bioresources","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioresources","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.15376/biores.19.3.6403-6426","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, PAPER & WOOD","Score":null,"Total":0}
引用次数: 0
Abstract
To enhance the scientific rigor of design decisions and develop new rosewood furniture that aligns with user emotions, this study integrates the strengths of the Hesitant Fuzzy Analytic Hierarchy Process (HFAHP) and Hesitant Fuzzy Quality Function Deployment (HFQFD) within the framework of Kansei Engineering (KE). This method accurately translates Consumer Requirements (CRs) into Engineering Characteristics (ECs). First, the KJ Method was used to screen and categorize Kansei words, create product sample images, and deconstruct the form of rosewood furniture using morphological analysis. Second, after collecting valid questionnaires using a 7-point Likert scale, Factor Analysis (FA) was employed to extract three key Kansei factors. Third, HFAHP was utilized to calculate the weights of the Kansei words. Fourth, HFQFD was applied to construct a hesitant fuzzy correlation matrix between CRs and ECs, determining the priority of design elements for rosewood furniture. Finally, using a square table as an example in the design practice, the optimal Scheme No. 9, which highly meets consumer emotional needs and features harmonious form combinations, was selected. This study enhances the emotional value of rosewood furniture, optimizes the design decision-making process, and improves contemporary consumer satisfaction.
期刊介绍:
The purpose of BioResources is to promote scientific discourse and to foster scientific developments related to sustainable manufacture involving lignocellulosic or woody biomass resources, including wood and agricultural residues. BioResources will focus on advances in science and technology. Emphasis will be placed on bioproducts, bioenergy, papermaking technology, wood products, new manufacturing materials, composite structures, and chemicals derived from lignocellulosic biomass.