An automatic machining process decision-making system based on knowledge graph

被引:18
|
作者
Guo, Liang [1 ]
Yan, Fu [1 ]
Lu, Yuqian [2 ]
Zhou, Ming [1 ]
Yang, Tao [1 ]
机构
[1] Southwest Petr Univ, Sch Mechatron Engn, Chengdu, Peoples R China
[2] Univ Auckland, Dept Mech Engn, Auckland, New Zealand
关键词
Knowledge graph; process reasoning; automatic decision-making; knowledge base; DESIGN RATIONALE; ONTOLOGY; MODEL; METHODOLOGY; FRAMEWORK; REPRESENTATION; INFORMATION; SELECTION; BEHAVIOR; TOOLS;
D O I
10.1080/0951192X.2021.1972461
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Automatic process decision-making is a key module in intelligent process design(IPD), which determines the intelligence degree of IPD and affects the quality of product design. The traditional process decision-making method fails to solve the problem of knowledge expression, especially the integration of enterprise manufacturing resources and process knowledge. What's more, heterogeneous knowledge also leads to the application of traditional knowledge mainly in keyword retrieval. So the process reasoning is mainly applied to the feature level, but the reasoning ability for the part level is weak. To overcome the above problems, the Knowledge Graph(KG) is introduced into the automatic machining process decision-making system. Firstly, a three-level information model is built to reorganize part information, process knowledge, and equipment resources based on KG. Secondly, the process reasoning framework based on KG is established, which is composed of process knowledge graph(PKG) information and process reasoning algorithm. Thirdly, to integrate process reasoning based on PKG, a hybrid reasoning algorithm based on semantic analysis(SA) and attributes weighting(AW) is built, which solved the problem of heterogeneity among process knowledge when making decisions. Finally, a prototype system was developed, and the aero-engine cone gear axis was tested to verify the effectiveness of the proposed system.
引用
收藏
页码:1348 / 1369
页数:22
相关论文
共 50 条
  • [1] Decision-Making System for the Diagnosis of Syndrome Based on Traditional Chinese Medicine Knowledge Graph
    Yang, Rui
    Ye, Qing
    Cheng, Chunlei
    Zhang, Suhua
    Lan, Yong
    Zou, Jing
    [J]. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE, 2022, 2022
  • [2] Decision-making in process design based on failure knowledge
    Dai, Wei
    Yang, Jun
    [J]. 2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), 2011, : 1505 - 1509
  • [3] Printed Circuit Board Assemble Process Decision-Making System Based on Knowledge
    Kong, Xianguang
    Wang, Yongxiang
    Ma, Binqiang
    [J]. ADVANCES IN ENGINEERING DESIGN AND OPTIMIZATION, PTS 1 AND 2, 2011, 37-38 : 899 - +
  • [4] Distribution Network Fault Assistant Decision-making Based on Knowledge Graph
    Wang, Jundong
    Yang, Jun
    Pei, Yangzhou
    Zhan, Xiangpeng
    Zhou, Ting
    Xie, Peiyuan
    [J]. Dianwang Jishu/Power System Technology, 2021, 45 (06): : 2101 - 2112
  • [5] INTELLIGENT AUTOMATIC DECISION-MAKING SYSTEM
    KRINITSKII, NA
    FEDOTOVA, DE
    KRINITSKII, VN
    [J]. PROGRAMMING AND COMPUTER SOFTWARE, 1992, 18 (06) : 247 - 254
  • [6] THE DECISION-MAKING SYSTEM AND PROCESS
    RENAUDSALIS, JL
    [J]. BULLETIN DU CANCER, 1980, 67 (04) : 365 - 368
  • [7] AN ARTIFICIAL INTELLIGENCE AND KNOWLEDGE-BASED SYSTEM TO SUPPORT THE DECISION-MAKING PROCESS IN SALES
    Baierle, I. C.
    Sellitto, M. A.
    Frozza, R.
    Schaefer, J. L.
    Habekost, A. F.
    [J]. SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING, 2019, 30 (02) : 17 - 25
  • [8] Building decision trees based on production knowledge as support in decision-making process
    Matuszny, Marcin
    [J]. PRODUCTION ENGINEERING ARCHIVES, 2020, 26 (02) : 36 - 40
  • [9] Graph Mining Based Knowledge Discovery in Designing Decision-Making Context Models
    Jiang, Hao
    Liu, Jihong
    Zhao, Zhenjie
    [J]. 2014 2ND INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI), 2014, : 948 - 953
  • [10] An automatic method for constructing machining process knowledge base from knowledge graph
    Guo, Liang
    Yan, Fu
    Li, Tian
    Yang, Tao
    Lu, Yuqian
    [J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2022, 73