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Digital twin-based process reuse and evaluation approach for smart process planning

  • Jinfeng LiuEmail author
  • Honggen Zhou
  • Guizhong Tian
  • Xiaojun Liu
  • Xuwen Jing
ORIGINAL ARTICLE

Abstract

With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dimensional process knowledge is widely accepted as an effective strategy for improving competitiveness of enterprises. However, there was little research on how to reuse and evaluate process knowledge with dynamical changing machining status. In this paper, we propose a novel digital twin-based approach for reusing and evaluating process knowledge. First, the digital twin-based process knowledge model which contains the geometric information and real-time process equipment status is introduced to represent the purpose and requirement of machining planning. Second, the process big data is constructed based on the three-layer and its association rules for accumulating process knowledge. Moreover, the similarity calculation algorithm of the scene model is proposed to filter the unmatched process knowledge. For accurately reusing the process knowledge, the process reusability evaluation approach of the candidate knowledge set is presented based on the real-time machining status and the calculated confidence. Finally, the diesel engine parts are applied in the developed prototype module to verify the effectiveness of the proposed method. The proposed method can promote the development and application of the smart process planning.

Keywords

Digital twin Process knowledge Process big data Feature vector Reusability evaluation 

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Notes

Funding information

This study was supported by the National Natural Science Foundation of China (No. 51605204) and the China Postdoctoral Science Foundation Funded Project (No. 2018M630536).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Jinfeng Liu
    • 1
    • 2
    • 3
    Email author
  • Honggen Zhou
    • 1
    • 2
  • Guizhong Tian
    • 1
    • 2
  • Xiaojun Liu
    • 4
  • Xuwen Jing
    • 1
    • 2
  1. 1.School of Mechanical EngineeringJiangsu University of Science and TechnologyZhenjiangChina
  2. 2.Jiangsu Provincial Key Laboratory of Advanced Manufacturing for Marine Mechanical EquipmentJiangsu University of Science and TechnologyZhenjiangChina
  3. 3.Hudong Heavy Machinery Co., Ltd.ShanghaiChina
  4. 4.School of Mechanical EngineeringSoutheast UniversityNanjingChina

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