QFD customer needs mining driven by product review data

被引：0
|
作者：
Hu Y. [1 ]
Xiao R. [1 ]
Zhang W. [1 ]
机构：
[1] School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Customer needs; Product design; Quality function deployment; Review data;
D O I：
10.13196/j.cims.2022.01.018
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the existing Quality Function Deployment (QFD) customer needs and their weights contained many subjective factors, which led to the poor objectivity of QFD analysis results, a mining method for QFD customer needs driven by product review data was proposed. A topic extraction based on attention latent Dirichlet allocation was proposed, and the customer needs collection was formed by Word2Vec similarity matching. The standardized customer needs expression was formed through the needs mapping model based on TRIZ. The improved proportional importance was cited and two thresholds were added to improve the single-value weight conversion rule of rough numbers, so as to obtain the final weight of customer needs. The normalized customer needs and their weights were input into the house of quality model to achieve data-driven QFD analysis. The feasibility and effectiveness of the proposed method was verified through examples. © 2022, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：184 / 196
页数：12
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