Computer Integrated Manufacturing System ›› 2024, Vol. 30 ›› Issue (3): 879-892.DOI: 10.13196/j.cims.2023.0377

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Dynamic data flow-driven knowledge graph construction method for remanufacturing disassembly process

JIANG Zhigan1,XIE Bin1,ZHU Shuo2+,ZHANG Hua3,YAN Wei3,DAI Mingren4   

  1. 1.Key Laboratory of Metallurgical Equipment and Control Technology,Ministry of Education,Wuhan University of Science and Technology
    2.Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology
    3.Academy of Green Manufacturing Engineering,Wuhan University of Science and Technology
    4.Gucheng Wanli Foundry Co.,Ltd.
  • Online:2024-03-31 Published:2024-04-02
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.52075396,51905392).

动态数据流驱动的再制造拆解工艺知识图谱构建方法

江志刚1,谢彬1,朱硕2+,张华3,鄢威3,代明仁4   

  1. 1.武汉科技大学冶金装备及其控制教育部重点实验室
    2.武汉科技大学机械传动与制造工程湖北省重点实验室
    3.武汉科技大学绿色制造工程研究院
    4.谷城万利铸造有限公司
  • 基金资助:
    国家自然科学基金资助项目(52075396,51905392)。

Abstract: Efficient and stable remanufacturing disassembly is an important prerequisite for the implementation of large-scale remanufacturing.However,the large differences in the internal structure and damage characteristics of used parts require frequent adjustments to the disassembly process,which results in low efficiency and poor quality stability of remanufacturing disassembly,and severely restricts the benefits of remanufacturing scale-up.In this respect,a knowledge graph construction method was proposed to extract personalized disassembly process knowledge from dynamic data streams and to improve the efficiency and quality of remanufacturing disassembly using efficient updating and reuse of knowledge.The dynamic temporal characteristics of remanufacturing disassembly process data were analyzed,and the dynamic data flow model was established with the disassembly stations as the data analysis nodes.The disassembly process knowledge was classified based on the data of each workstation,the ontology model containing disassembly process knowledge,resource knowledge and feature knowledge were constructed,and the disassembly process knowledge was automatically extracted by using the ALBERT-BiLSTM-CRF model and natural language processing method.Further,a dynamic data flow-driven graph update mechanism was proposed to realize the fast response of knowledge graph to the adjustment of disassembly process.The effectiveness of the proposed method was verified by taking the dismantling of a certain type of used power battery pack as an example.

Key words: remanufacturing disassembly, disassembly process, knowledge graph, graph update

摘要: 高效稳定的再制造拆解是实施规模化再制造的重要前提。然而,废旧零部件的内部结构、损伤特征等存在较大差异,需要对拆解工艺进行频繁调整,从而造成再制造拆解效率低、质量稳定性差,严重制约了再制造规模化效益。为此,提出一种从动态数据流中提取个性化拆解工艺知识的知识图谱构建方法,利用知识的高效更新与重用提升再制造拆解效率与质量。首先,分析再制造拆解工艺数据的动态时序性特点,以拆解工位为数据分析节点,建立动态数据流模型。其次,基于工位数据对拆解工艺知识进行分类,构建包含拆解过程知识、资源知识和特征知识的本体模型,并利用命名实体识别模型(ALBERT-BiLSTM-CRF)和自然语言处理方法自动抽取拆解工艺知识。进而,提出一种动态数据流驱动的图谱更新机制,实现知识图谱对拆解工艺调整的快速响应。最后,以某型号废旧动力电池包拆解为例,验证所提方法的有效性。

关键词: 再制造拆解, 拆解工艺, 知识图谱, 图谱更新

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