Paper Info
Title
Tacit Knowledge Representation and Classification Based on Statistical Data for Production Process
Abstract
Tacit knowledge is considered as an important information in many kinds of fields, essentially in manufacturing. The tacit knowledge has the ability to improve production efficiency and ensure production quality for production process. However, it is difficulty to represent and apply tacit knowledge. In this paper, a novel knowledge representation model based on statistical data is proposed to visually describe tacit knowledge in production process, which consists of three elements, including attribute, input and output element. Moreover, based on the proposed representation model, a knowledge classification method is developed, which considers the collaboration of statistical data. For solution to classification, a collaborative multi-objective optimization algorithm, the Collaborative Non-dominated Sorting Genetic Algorithm. (CNSGA-II), is designed to optimize classification of tacit knowledge. Furthermore, an industrial case study is reported to show details of the proposed model and method. Finally, an experimental comparison demonstrates that the algorithm can achieve efficient optimized classification with collaborative objectives.
Year
DOI
Venue
2021
10.1109/CSCWD49262.2021.9437840
PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD)
Keywords
DocType
Citations 
tacit knowledge, representation and classification, statistical data, collaborative optimization, production process
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Qihao Wan101.01
Heming Zhang29728.48
Yiran Wang300.34