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A context-aware recommendation system for improving manufacturing process modeling

Author

Listed:
  • Jiaxing Wang

    (Zhejiang University of Technology)

  • Sibin Gao

    (Research Institute of CETHIK Group)

  • Zhejun Tang

    (Zhejiang University/University of Illinois at Urbana-Champaign (ZJU-UIUC) Institute)

  • Dapeng Tan

    (Zhejiang University of Technology
    Ministry of Education & Zhejiang Province
    Zhejiang Province & Ministry of Education)

  • Bin Cao

    (Zhejiang University of Technology)

  • Jing Fan

    (Zhejiang University of Technology)

Abstract

Process recommendation is an essential technique to help process modeler effectively and efficiently model a manufacturing process from scratch. However, the current process recommendation methods suffer from the following problems: (1) To extract all the execution paths from a manufacturing process, the behavior-based methods may occur a state space explosion problem when unfolding a process with multiple parallel patterns, resulting in low efficiency. (2) Current structure-based methods are inefficient since too many expensive computations of the graph edit distance are involved. (3) Most of the existing methods manually design their process similarity metrics with several features, which can only be applied in specific situations. (4) Few works provide visualization tools for process modeling assistance. To resolve these problems, this paper proposes a context-aware recommendation system for improving manufacturing process modeling. First, the independent paths and P,Q-grams are efficiently extracted from the manufacturing processes in the repository to represent their typical behavior and structure. Then, the process recommendation problem is transformed into the word prediction problem in natural language processing, where the serialization of an independent path/P,Q-gram and a node in it are separately regarded as a sentence and a word. The Word2vec model is introduced to automatically learn the relationships among nodes from independent paths and P,Q-grams and generate the vectors with hundreds of context-aware features for nodes in the repository. After that, the top-k similar nodes are recommended for the target node in the process fragment under construction based on the k-nearest neighbors algorithm. Finally, a visualization tool is provided for process modelers to efficiently design a new manufacturing process. Experimental evaluations show that the proposed method can perform similar or even better than the baseline methods in terms of recommending quality.

Suggested Citation

  • Jiaxing Wang & Sibin Gao & Zhejun Tang & Dapeng Tan & Bin Cao & Jing Fan, 2023. "A context-aware recommendation system for improving manufacturing process modeling," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1347-1368, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01854-4
    DOI: 10.1007/s10845-021-01854-4
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    References listed on IDEAS

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