J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2020 , Vol. 25 >Issue 5: 578 - 588

DOI: https://doi.org/10.1007/s12204-020-2179-y

Assembly Information Model Based on Knowledge Graph

Expand
  • (College of Mechanical Engineering, Donghua University, Shanghai 201600, China)

Online published: 2020-09-11

Fold

Abstract

There are heterogeneous problems between the CAD model and the assembly process document. In
the planning stage of assembly process, these heterogeneous problems can decrease the efficiency of information
interaction. Based on knowledge graph, this paper proposes an assembly information model (KGAM) to integrate
geometric information from CAD model, non-geometric information and semantic information from assembly
process document. KGAM describes the integrated assembly process information as a knowledge graph in the
form of “entity-relationship-entity” and “entity-attribute-value”, which can improve the efficiency of information
interaction. Taking the trial assembly stage of a certain type of aero-engine compressor rotor component as an
example, KGAM is used to get its assembly process knowledge graph. The trial data show the query and update
rate of assembly attribute information is improved by more than once. And the query and update rate of assembly
semantic information is improved by more than twice. In conclusion, KGAM can solve the heterogeneous problems
between the CAD model and the assembly process document and improve the information interaction efficiency.

Key words： knowledge graph; assembly process; information model; integrating

Cite this article

CHEN Zhiyu, BAO Jinsong, ZHENG Xiaohu, LIU Tianyuan . Assembly Information Model Based on Knowledge Graph[J]. Journal of Shanghai Jiaotong University(Science), 2020 , 25(5) : 578 -588 . DOI: 10.1007/s12204-020-2179-y

References

[1] TIAN F J, TIAN X T, GENG J H, et al. Model-based definition process information modeling and application[J]. Computer Integrated Manufacturing Systems,2012, 18(5): 913-919 (in Chinese).
[2] XU, 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.
[3] ZHANG H, ZHU B, LI Y, et al. Development and utilization of a Process-oriented Information Model for sustainable manufacturing [J]. Journal of Manufacturing Systems, 2015, 37: 459-466.
[4] KHABBAZI M R, WIKANDER J, ONORI M, et al.Object-oriented design of product assembly feature data requirements in advanced assembly planning [J].Assembly Automation, 2018, 38(1): 97-112.
[5] LI G Z, ZHANG L X, GAO Q F. A Virtual Assemblyoriented Multi-views information model and its XML description [C]//IEEE Chinese Control and Decision Conference. Xuzhou, China: IEEE, 2010: 1294-1298.
[6] ZHANG Y N, YANG Z J, DING H, et al. Virtual assembly information expression based on XML technology and its application [J]. Machinery Design & Manufacture,2014(9): 205-207(in Chinese).
[7] BAO J S, WU D L, CHENG Q H, et al. Information modeling and visualization of assembly fat model for large-scale product [J]. Key Engineering Materials,2013, 579/580: 711-718.
[8] HU H S, ZHOU M C. A petri net-based discrete-event control of automated manufacturing systems with assembly operations [J]. IEEE Transactions on Control Systems Technology, 2015, 23(2): 513-524.
[9] YANG L, JIAO Z G, LIN H B. Modeling and applied research in Petri net of virtual assembly program control [J]. Advanced Materials Research, 2012,482/483/484: 264-269.
[10] YANG X Q, HAN J H, PAN Y. Virtual training system of assembly and disassembly based on Petri net [C]//Proceeding of International Conference on Soft Computing Techniques and Engineering Application. New Delhi: Springer, 2014: 205-212.
[11] FIORENTINI X, GAMBINO I, LIANG V C, et al. An ontology for assembly representation: NISTIR 7436 [S]. Gaithersburg, MD, USA: NIST, 2007.
[12] QIAO L H, QIE Y F, ZHU Z W, et al. An ontologybased modelling and reasoning framework for assembly sequence planning [J]. The International Journal of Advanced Manufacturing Technology, 2018,94(9/10/11/12): 4187-4197.
[13] SAYED M S, LOHSE N. Ontology-driven generation of Bayesian diagnostic models for assembly systems [J]. The International Journal of Advanced Manufacturing Technology, 2014, 74(5/6/7/8): 1033-1052.
[14] GRUHIER E, DEMOLY F, DUTARTRE O, et al. A formal ontology-based spatiotemporal mereotopology for integrated product design and assembly sequence planning [J]. Advanced Engineering Informatics, 2015,29(3): 495-512.
[15] HULLIYAH K, KUSUMA H T. Application of knowledge graph for making Text Summarization (analizing a text of educational issues) [C]//International Conference on Information and Communication Technology for the Moslem World. Jakarta, Indonesia: IEEE,2010: 79-83.
[16] 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.
[17] DUBEY M, BANERJEE D, CHAUDHURI D, et al. EARL: Joint entity and relation linking for question answering over knowledge graphs [C]//17th International Semantic Web Conference. Monterey, CA,USA:ISWC Organising Committee, 2018: 108-126.
[18] ZHOU H, YOUNG T, HUANG M L, et al. Commonsense knowledge aware conversation generation with graph attention [C]//27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: International Joint Conferences on Artificial Intelligence Organization, 2018: 4623-4629.
[19] LI X L, ZHANG S S, HUANG R, et al. Structural modeling of heterogeneous CAM model based on process knowledge graph [J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(7): 168-181(in Chinese).
[20] VYAS P, RICKLI J L. Automatic extraction and synthesis of disassembly information from CAD assembly STEP file [C]//International Design Engineering Technical Conference & Computers and Information in Engineering. Charlotte, NC, USA: ASME, 2016:DETC2016-59577.

Options
Outlines

/

〈 〉