Yang Yan-Pu, Chen Deng-Kai, Gu Rong, Gu Yu-Feng, Yu Sui-Huai
School of Construction Machinery, Chang'an University, Xi'an 710064, China.
Department of Industrial Design, Northwestern Polytechnical University, Xi'an 710072, China.
Comput Intell Neurosci. 2016;2016:5083213. doi: 10.1155/2016/5083213. Epub 2016 Aug 18.
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.
消费者的感性需求反映了他们对产品的认知，且总是由大量形容词构成。降低这些需求的维度复杂性以提取主要词汇，不仅能使目标产品得到明确的定位，还为从事设计工作的设计师提供了便利的设计基础。因此，本研究通过对传统的设计结构矩阵（DSM）进行参数化并集成遗传算法来寻找最优的感性聚类，采用了数值设计结构矩阵（NDSM）。在构建NDSM时，应用四点量表法为每两个感性形容词的链接权重赋值作为单元格的值。使用遗传算法对感性NDSM进行聚类并找到最优聚类。此外，还介绍了所提方法的流程。通过电动滑板车感性需求聚类的实例说明了所提方法的细节。案例研究表明，所提方法在产品情感设计中对感性需求形容词进行聚类方面具有前景。
