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

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


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.


Digital twin Process knowledge Process big data Feature vector Reusability evaluation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


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).


  1. 1.
    Peng G, Wang H, Zhang H, Zhao Y, Johnson AL (2017) A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model. Adv Eng Inform 33:314–329Google Scholar
  2. 2.
    Li Z, Zhou X, Liu W, Kong C (2015) A geometry search approach in case-based tool reuse for mould manufacturing. Int J Adv Manuf Technol 79(5):757–768Google Scholar
  3. 3.
    Li M, Zhang YF, Fuh JYH, Qiu ZM (2009) Toward effective mechanical design reuse: CAD model retrieval based on general and partial shapes. J Mech Des 131(1):1–8Google Scholar
  4. 4.
    Marefat MM, Pitta C (2007) Similarity-based retrieval of CAD solid models for automated reuse of machining process plans. In: 3rd IEEE International Conference on Automation Science and Engineering, IEEE CASE 2007, September 22, 2007 - September 25, 2007 312–317. Institute of Electrical and Electronics Engineers Inc., ScottsdaleGoogle Scholar
  5. 5.
    Hoque ASM, Halder PK, Parvez MS, Szecsi T (2013) Integrated manufacturing features and design-for-manufacture guidelines for reducing product cost under CAD/CAM environment. Comput Ind Eng 66(4):988–1003Google Scholar
  6. 6.
    Ma Y-S, Chen G, Thimm G (2008) Paradigm shift: unified and associative feature-based concurrent and collaborative engineering. J Intell Manuf 19(6):625–641Google Scholar
  7. 7.
    Li JX, Chen ZN, Yan XG (2014) Automatic generation of in-process models based on feature working step and feature cutter volume. Int J Adv Manuf Technol 71(1-4):395–409Google Scholar
  8. 8.
    Kumar SPL, Jerald J, Kumanan S (2014) An intelligent process planning system for micro turn-mill parts. Int J Prod Res 52(20):6052–6075Google Scholar
  9. 9.
    Kumar SPL, Jerald J, Kumanan S (2015) Feature-based modelling and process parameters selection in a CAPP system for prismatic micro parts. Int J Comput Integr Manuf 28(10):1046–1062Google Scholar
  10. 10.
    Zhu J, Kato M, Tanaka T, Yoshioka H, Saito Y (2015) Graph based automatic process planning system for multi-tasking machine. J Adv Mech Des Syst Manuf 9(3):1–14Google Scholar
  11. 11.
    Jong WR, Lai PJ, Chen YW, Ting YH (2015) Automatic process planning of mold components with integration of feature recognition and group technology. Int J Adv Manuf Technol 78(5-8):807–824Google Scholar
  12. 12.
    Bensmaine A, Dahane M, Benyoucef L (2014) A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. Int J Prod Res 52(12):3583–3594Google Scholar
  13. 13.
    Sormaz Dusan N, Chandu T (2010) Recognition of interacting volumetric features using 2D hints. Assem Autom 30(2):131–141Google Scholar
  14. 14.
    Rahmani K, Arezoo B (2007) A hybrid hint-based and graph-based framework for recognition of interacting milling features. Comput Ind 58(4):304–312Google Scholar
  15. 15.
    Marchetta MG, Forradellas RQ (2010) An artificial intelligence planning approach to manufacturing feature recognition. Comput Aided Des 42(3):248–256Google Scholar
  16. 16.
    Huang W, Hu Y, Cai L (2012) An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts. Int J Adv Manuf Technol 62(9–12):1219–1232Google Scholar
  17. 17.
    Yu M, Zhang Y, Chen K, Zhang D (2015) Integration of process planning and scheduling using a hybrid GA/PSO algorithm. Int J Adv Manuf Technol 78(1–4):583–592Google Scholar
  18. 18.
    Zhang XZ, Nassehi A, Safaieh M, Newman ST (2013) Process comprehension for shopfloor manufacturing knowledge reuse. Int J Prod Res 51(23-24):7405–7419Google Scholar
  19. 19.
    Lee HJ, Ahn HJ, Kim JW, Park SJ (2006) Capturing and reusing knowledge in engineering change management: a case of automobile development. Inf Syst Front 8(5):375–394Google Scholar
  20. 20.
    Huang R, Zhang SS, Xu CH, Zhang XM, Zhang CC (2015) A flexible and effective NC machining process reuse approach for similar subparts. Comput Aided Des 62:64–77Google Scholar
  21. 21.
    Ip CY, Regli WC (2005) Content-based classification of CAD models with supervised learning. Comput Aided Des Appl 2(5):609–617Google Scholar
  22. 22.
    Cochrane S, Young R, Case K, Harding J, Gao J, Dani S, Baxter D (2008) Knowledge reuse in manufacturability analysis. Robot Comput Integr Manuf 24(4):508–513Google Scholar
  23. 23.
    Glaessgen E, Stargel D. (2012) The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/ AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA 1818Google Scholar
  24. 24.
    Zhang H, Liu Q, Chen X, Zhang D, Leng J (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5(1):26901–26911Google Scholar
  25. 25.
    Hochhalter J Leser WP, Newman JA (2014) Coupling damage sensing particles to the digital twin concept. NASA Technical Reports Server 1(1):1–9Google Scholar
  26. 26.
    Söderberg R, Wärmefjord K, Carlson JS, Lindkvist L (2017) Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann 66(1):137–140Google Scholar
  27. 27.
    Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9–12):3563–3576Google Scholar
  28. 28.
    Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. Ieee Access 6:3585–3593Google Scholar
  29. 29.
    Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96(1–4):1149–1163Google Scholar
  30. 30.
    Tao F, Zhang M, Cheng J, Qi Q (2017) Digital twin workshop: a new paradigm for future workshop. Comput Integr Manuf Syst 23(1):1–9Google Scholar
  31. 31.
    Tao F, Liu W, Liu J, Liu X, Liu Q et al (2018) Digital twin and its potential application exploration. Comput Integr Manuf Syst 24(1):1–18Google Scholar
  32. 32.
    Liu J, Liu X, Cheng Y, Ni Z (2017) An approach to mapping machining feature to manufacturing feature volume based on geometric reasoning for process planning. Proc Inst Mech Eng B J Eng Manuf 231(7):1204–1216Google Scholar
  33. 33.
    Liu J, Liu X, Cheng Y, Ni Z (2016) A systematic method for the automatic update and propagation of the machining process models in the process modification. Int J Adv Manuf Technol 82(1–4):473–487Google Scholar
  34. 34.
    Chen WL, Xie SQ, Zeng FF, Li BM (2011) A new process knowledge representation approach using parameter flow chart. Comput Ind 62(1):9–22Google Scholar
  35. 35.
    Werner DC, Weidlich R, Guenther B, Blaurock JE (2004) Engineers’ CAx education—it’s not only CAD. Comput Aided Des 36(14):1439–1450Google Scholar
  36. 36.
    Mawussi KB, Tapie L (2011) A knowledge base model for complex forging die machining. Comput Ind Eng 61(1):84–97Google Scholar
  37. 37.
    Mokhtar A, Xu X, Lazcanotegui I (2009) Dealing with feature interactions for prismatic parts in STEP-NC. J Intel Manuf 20(4):431–445Google Scholar
  38. 38.
    Wang W., Li Y.G, (2014) Drive geometry construction method of machining features for aircraft structural part numerical control machining. Proc Inst Mech Eng B J Eng Manuf 228(10):1214–1225Google Scholar
  39. 39.
    Zheng Y, Mohd TJ, Tap MM (2012) Decomposition of interacting machining features based on the reasoning on the design features. Int J Adv Manuf Technol 58(1–4):359–377Google Scholar
  40. 40.
    Liu C, Li Y, Shen W (2014) Integrated manufacturing process planning and control based on intelligent agents and multi-dimension features. Int J Adv Manuf Technol 75(9):1457–1471Google Scholar
  41. 41.
    Liu J, Zhou H, Xiaojun L, Jing X (2017) A flexible process information reuse method for similar machining feature. Int J Adv Manuf Technol 92(1–4):217–229Google Scholar

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

Personalised recommendations