Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2021 , Vol. 55 >Issue 5: 544 - 556

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.241

Dynamic Knowledge Graph Modeling Method for Ship Block Manufacturing Process

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  • College of Mechanical Engineering, Donghua University, Shanghai 201620, China

Received date: 2020-07-29

  Online published: 2021-06-01

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Abstract

In the dynamic and discrete ship block manufacturing process, lack of effective process resource organization and transparency in product processing leads to the problem of high cost and low efficiency for managers to acquire knowledge. A method for dynamic generation and updating of knowledge graph based on processing beat data flow is proposed. The definition of the processing beat data information model is defined by analyzing the processing flow and the station data characteristics of the ship blocks. The graph mapping steps, models, and fusion connection algorithms are proposed for static resources and processing beat data to realize the semantic association of station dynamic time series data and knowledge graphs. Based on the relationship between station process and product structure, the generation of workshop-level dynamic knowledge graph is realized. Taking the production process of a ship block as an example, the knowledge graph visualization prototype system is designed, developed, and verified. The results show that the proposed method is beneficial to the organization, acquisition, and reuse of knowledge in the process of ship block manufacturing.

Key words： knowledge graph; time series data; information model; knowledge organization; ship block manufacturing

Cite this article

SONG Dengqiang, ZHOU Bin, SHEN Xingwang, BAO Jinsong, ZHOU Yaqin . Dynamic Knowledge Graph Modeling Method for Ship Block Manufacturing Process[J]. Journal of Shanghai Jiaotong University, 2021 , 55(5) : 544 -556 . DOI: 10.16183/j.cnki.jsjtu.2020.241

References

[1] 李杨梅, 王冬梅, 卢光星, 等. 船体分段车间制造过程信息流分析 [C]//2018年数字化造船学术交流会议. 广州: 中国造船工程学会, 2018: 196-200.
[1] LI Yangmei, WANG Dongmei, LU Guangxing, et al. Information flow analysis of the manufacturing process of the ship block workshop [C]//2018 Digital Shipbuilding Academic Exchange Conference. Guangzhou: The Chinese Society of Naval Architect and Marine Engineers, 2018: 196-200.
[2] 邸立强, 杨剑征, 赵川. 国外数字化造船技术发展趋势研究[J]. 舰船科学技术, 2015, 37(7):1-4.
[2] DI Liqiang, YANG Jianzheng, ZHAO Chuan. Development of foreign digital shipbuilding technique[J]. Ship Science and Technology, 2015, 37(7):1-4.
[3] 代风, 翟翔, 施国强, 等. 面向航天产品研制的知识网络本体建模方法[J]. 浙江大学学报(工学版), 2018, 52(10):2023-2034.
[3] DAI Feng, ZHAI Xiang, SHI Guoqiang, et al. Modeling ontological knowledge network for aerospace equipment development[J]. Journal of Zhejiang University (Engineering Science), 2018, 52(10):2023-2034.
[4] XU L D, WANG C G, BI Z M, et al. Object-oriented templates for automated assembly planning of complex products[J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(2):492-503.
[5] ZHANG H, ZHU B C, LI Y P, et al. Development and utilization of a Process-oriented Information Model for sustainable manufacturing[J]. Journal of Manufacturing Systems, 2015, 37:459-466.
[6] 梁峰, 江志斌, 陶俐言. 基于元资源的制造资源建模方法研究[J]. 计算机集成制造系统, 2008, 14(12):2306-2311.
[6] LIANG Feng, JIANG Zhibin, TAO Liyan. Model building approach of manufacturing resource based on meta-resource[J]. Computer Integrated Manufacturing Systems, 2008, 14(12):2306-2311.
[7] 张勇为, 顾新建, 胡恒杰, 等. 工艺设计知识资源网络的元数据模型[J]. 浙江大学学报(工学版), 2009, 43(10):1828-1832.
[7] ZHANG Yongwei, GU Xinjian, HU Hengjie, et al. Metadata model of process planning knowledge resource network[J]. Journal of Zhejiang University (Engineering Science), 2009, 43(10):1828-1832.
[8] WEST L A, HESS T J. Metadata as a knowledge management tool: Supporting intelligent agent and end user access to spatial data[J]. Decision Support Systems, 2002, 32(3):247-264.
[9] 施昭, 曾鹏, 于海斌. 基于本体的制造知识建模方法及其应用[J]. 计算机集成制造系统, 2018, 24(11):2653-2664.
[9] SHI Zhao, ZENG Peng, YU Haibin. Ontology-based modeling method for manufacturing knowledge and its application[J]. Computer Integrated Manufacturing Systems, 2018, 24(11):2653-2664.
[10] LIANG J S. An ontology-oriented knowledge methodology for process planning in additive layer manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2018, 53:28-44.
[11] HUANG Z Y, JOWERS C, DEHGHAN-MANSHADI A, et al. Smart manufacturing and DVSM based on an Ontological approach[J]. Computers in Industry, 2020, 117:103189.
[12] 董晨阳, 郑小云, 余建波. 基于过程挖掘与复杂网络集成的制造过程资源建模与关键加工节点识别[J]. 机械工程学报, 2019, 55(3):169-180.
[12] DONG Chenyang, ZHENG Xiaoyun, YU Jianbo. Resource modeling of manufacturing process and critical nodes recognition based on the inte gration of process mining and complex network[J]. Journal of Mechanical Engineering, 2019, 55(3):169-180.
[13] NICKEL M, MURPHY K, TRESP V, et al. A review of relational machine learning for knowledge graphs[J]. Proceedings of the IEEE, 2016, 104(1):11-33.
[14] CHEN H N, LUO X W. An automatic literature knowledge graph and reasoning network modeling framework based on ontology and natural language processing[J]. Advanced Engineering Informatics, 2019, 42:100959.
[15] NOY N, GAO Y Q, JAIN A, et al. Industry-scale knowledge graphs[J]. Communications of the ACM, 2019, 62(8):36-43.
[16] MA B, JIANG T H, ZHOU X, et al. A novel data integration framework based on unified concept model[J]. IEEE Access, 2017, 5:5713-5722.
[17] 蒋秉川, 万刚, 许剑, 等. 多源异构数据的大规模地理知识图谱构建[J]. 测绘学报, 2018, 47(8):1051-1061.
[17] JIANG Bingchuan, WAN Gang, XU Jian, et al. Geographic knowledge graph building extracted from multi-sourced heterogeneous data[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(8):1051-1061.
[18] XU C J, NAYYERI M, ALKHOURY F, et al. Temporal knowledge graph completion based on time series Gaussian embedding [C]//19th International Semantic Web Conference. Athens, Greece: ISWC, 2020: 654-671.
[19] LU Y Q, ASGHAR M R. Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing[J]. Journal of Manufacturing Systems, 2020, 55:348-359.
[20] ŠORMAZ D, SARKAR A. SIMPM-Upper-level ontology for manufacturing process plan network generation[J]. Robotics and Computer-Integrated Manufacturing, 2019, 55:183-198.
[21] SARKAR A, ŠORMAZ D. Ontology model for process level capabilities of manufacturing resources[J]. Procedia Manufacturing, 2019, 39:1889-1898.
[22] 李秀玲, 张树生, 黄瑞, 等. 基于工艺知识图谱的异构CAM模型结构化建模方法[J]. 计算机辅助设计与图形学学报, 2018, 30(7):1342-1355.
[22] LI Xiuling, ZHANG Shusheng, HUANG Rui, et al. Structural modeling of heterogeneous CAM model based on process knowledge graph[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(7):1342-1355.
[23] DING Y W, XU W J, LIU Z H, et al. Robotic task oriented knowledge graph for human-robot collaboration in disassembly[J]. Procedia CIRP, 2019, 83:105-110.
[24] CHHIM P, CHINNAM R B, SADAWI N. Product design and manufacturing process based ontology for manufacturing knowledge reuse[J]. Journal of Intelligent Manufacturing, 2019, 30(2):905-916.
[25] 白海燕, 梁冰. 利用D2R实现关系数据库与关联数据的语义模式映射[J]. 现代图书情报技术, 2011(Z1):1-7.
[25] BAI Haiyan, LIANG Bing. Semantic pattern mapping between RDBMS and linked data based on open source software[J]. New Technology of Library and Information Service, 2011(Z1):1-7.
[26] 鲁明羽, 陆玉昌. 基于OEM模型的半结构化数据的模式抽取[J]. 清华大学学报(自然科学版), 2004, 44(9):1264-1267.
[26] LU Mingyu, LU Yuchang. OEM-based schema extraction of semi-structured data[J]. Journal of Tsinghua University (Science and Technology), 2004, 44(9):1264-1267.
[27] TIXIER A J P, HALLOWELL M R, RAJAGOPALAN B, et al. Construction safety clash detection: Identifying safety incompatibilities among fundamental attributes using data mining[J]. Automation in Construction, 2017, 74:39-54.
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