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Knowledge extraction and visualization of digital design process

Authors
Yang, JiwonKim, EunjiHur, MinhoeCho, SungzoonHan, MyungbinSeo, Iksang
Issue Date
Feb-2018
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Knowledge extraction; Visualization; Digital design
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.92, pp 206 - 215
Pages
10
Journal Title
EXPERT SYSTEMS WITH APPLICATIONS
Volume
92
Start Page
206
End Page
215
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63913
DOI
10.1016/j.eswa.2017.09.002
ISSN
0957-4174
1873-6793
Abstract
After digitally designing components of vehicles, a design team creates a virtual manufacturing environment that resembles actual manufacturing facilities. During this digital pre-assembly process, a review team examines each component, and records its problems and requirements in part verification reports. Once these reports are delivered to specific design team responsible for each part, the design team can make appropriate adjustments to their designs. This digital pre-assembly process can evaluate and prevent flaws in design prior to actual manufacturing, improving production quality and reducing manufacturing cost. As these reports are written in free text form, they, however, are not fully utilized for understanding problems arising from the design process. This paper proposes a method of applying text mining techniques on verification reports to extract insights for quality improvement. In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights. The proposed method is applied on approximately 140,000 reports, and is validated through the quality of the answers obtained for the questions posed by the domain experts. The proposed method successfully extracts useful information from the text database, and provides intuitive graphical interface, thereby satisfying the need of the domain experts. This paper proposes a systematic framework of transforming huge amount of raw text data into intuitive visualization. Through this framework, meaningful knowledge can be extracted, analyzed and shared to improve the quality of the products. Main contribution of our paper is that it proposes a framework for knowledge extraction from pre-assembly process. Not only does it systematically arrange the data, but it also combines various data sources and creates a knowledge system to improve efficiency of the design process. (C) 2017 Elsevier Ltd. All rights reserved.
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