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A STEP-NC complaint and feature-based solution for intelligent process planning

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Abstract

Computer-aided process planning, as the link between designing and machining, is expected to move towards being of more automation and intelligence with the current trend of intelligent manufacturing. Hence, a solution with STEP-NC and feature-based technology for intelligent process planning is proposed based on the analysis of the mainstream enabling technologies in process planning, making full use of the data advantage of the STEP-NC standard and integrating process planning unit module from the content. More automated operations will be implemented through taking machining features as planning units and develo** integrated built-in models as well as algorithms to aid decision-making. The solution is implemented by develo** a process planning prototype system, wherein key implementation techniques and model-based planning algorithms are introduced. To verify the feasibility of the solution, two test parts of the industrial application level are applied to the prototype system. The application results show that the solution has promising application prospects and reference value for improving the automation and intelligence of the whole planning process.

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Algorithm 1
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Algorithm 2
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Data Availability

All data generated or analyzed during this study are included in this published article and available at the corresponding author.

Abbreviations

ACO:

Ant colony algorithm

AI:

Artificial intelligence

CAD:

Computer-aided design

CAM:

Computer-aided manufacturing

CAPP:

Computer-aided process planning

CAPP:

Computer-aided process planning

CNC:

Computer numerical control

ES:

Expert system

FBM:

Feature-based modeling

GA:

Genetic algorithm

GPP:

Generative process planning

HWSG:

Hardware-dependent workingstep sequence graph

IPP:

Intelligent process planning

NWSG:

Neutral workingstep sequence graph

OCC:

OpenCascade

OSG:

OpenSceneGraph

STEP:

Standard for the Exchange of Product Model Data

STEP-NC:

Standard for the Exchange of Product Model Data-Numerical Computer

VPP:

Variant process planning

References

  1. Xu X, Wang L, Newman ST (2011) Int J Comput Integr Manuf 24(1):1. https://doi.org/10.1080/0951192x.2010.518632

    Article  Google Scholar 

  2. Shin SJ, Woo J, Rachuri S, Seo W (2019) Comput Aided Des 110:92. https://doi.org/10.1016/j.cad.2018.12.009

    Article  Google Scholar 

  3. Nassehi A, Newman ST, Allen RD (2006) Robot Comput-Integr Manuf 22(5–6):456. https://doi.org/10.1016/j.rcim.2005.11.003

    Article  Google Scholar 

  4. Li P, Hu T, Zhang C (2011) Procedia Eng 15:834. https://doi.org/10.1016/j.proeng.2011.08.155

    Article  Google Scholar 

  5. Wang L, Wang W, Liu D (2017) CIRP Annals 66(1):441. https://doi.org/10.1016/j.cirp.2017.04.015

    Article  Google Scholar 

  6. Ye Y, Hu T, Yang Y, Zhu W, Zhang C (2018) J Intell Manuf 31(7):1751. https://doi.org/10.1007/s10845-018-1401-3

    Article  Google Scholar 

  7. Liu Q, Li X, Gao L (2020) J Intell Manuf 32(3):781. https://doi.org/10.1007/s10845-020-01703-w

    Article  Google Scholar 

  8. Li J, Zhou G, Zhang C (2021) International Journal of Production Research 1–18. https://doi.org/10.1080/00207543.2021.1951869

  9. Kumar SPL (2017) Eng Appl Artif Intell 65:294. https://doi.org/10.1016/j.engappai.2017.08.005

    Article  Google Scholar 

  10. Besharati-Foumani H, Lohtander M, Varis J (2019) Procedia Manuf 38:156. https://doi.org/10.1016/j.promfg.2020.01.021

    Article  Google Scholar 

  11. Wu D, Rosen DW, Wang L, Schaefer D (2015) Comput Aided Des 59:1. https://doi.org/10.1016/j.cad.2014.07.006

    Article  Google Scholar 

  12. Lu Y, Xu X, Wang L (2020) J Manuf Syst 56:312. https://doi.org/10.1016/j.jmsy.2020.06.010

    Article  Google Scholar 

  13. **ao W, Zheng L, Huan J, Lei P (2015) Robot Comput-Integr Manuf 31:1. https://doi.org/10.1016/j.rcim.2014.06.003

    Article  Google Scholar 

  14. Allen RD, Harding JA, Newman* ST (2007) Int J Prod Res 43(4):655. https://doi.org/10.1080/00207540412331314406

  15. Chung DH, Suh SH (2008) Comput Aided Des 40(5):521. https://doi.org/10.1016/j.cad.2008.01.009

    Article  Google Scholar 

  16. Zhang Y, Yu X, Sun J, Zhang Y, Xu X, Gong Y (2022) J Manuf Syst 62:62. https://doi.org/10.1016/j.jmsy.2021.11.002

    Article  Google Scholar 

  17. Wang H, Liu G, Zhang Q, Mu W (2019) Int J Adv Manuf Technol 103(9–12):3557. https://doi.org/10.1007/s00170-019-03482-y

    Article  Google Scholar 

  18. ISO 14649-10 (2004) Industrial automation systems and integration - physical device control - data model for computerized numerical controllers - part 10: General process data

  19. ISO 14649-11 (2004) Industrial automation systems and integration - physical device control - data model for computerized numerical controllers - part 11: Process data for milling

  20. ISO 14649-111 (2010) Industrial automation systems and integration - physical device control - data model for computerized numerical controllers - part 111: Tools for milling machines

  21. ISO 14649-201 (2011) Industrial automation systems and integration - physical device control - data model for computerized numerical controllers - part 201: Machine tool data for cutting processes

  22. Kong L, Wang L, Li F, Lv X, Li J, Ma Y, Chen B, Guo J (2021) J Manuf Syst 61:223. https://doi.org/10.1016/j.jmsy.2021.09.008

    Article  Google Scholar 

  23. Deja M, Siemiatkowski MS (2018) J Manuf Syst 48:49. https://doi.org/10.1016/j.jmsy.2018.06.001

    Article  Google Scholar 

  24. Kumar SPL (2018) Int J Prod Res 57(15–16):4766. https://doi.org/10.1080/00207543.2018.1424372

    Article  Google Scholar 

  25. Naranje V, Kumar S (2014) Expert Syst Appl 41(4):1419. https://doi.org/10.1016/j.eswa.2013.08.041

    Article  Google Scholar 

  26. Kashkoush M, ElMaraghy H (2015) J Manuf Syst 34:43. https://doi.org/10.1016/j.jmsy.2014.10.004

  27. Li Y, Liu X, Gao JX, Maropoulos PG (2012) CIRP Ann 61(1):167. https://doi.org/10.1016/j.cirp.2012.03.085

  28. Liu C, Li Y, Li Z (2018) J Manuf Syst 49:16. https://doi.org/10.1016/j.jmsy.2018.08.003

    Article  Google Scholar 

  29. Shi P, Qi Q, Qin Y, Scott PJ, Jiang X (2022) Robot Comput-Integr Manuf 73. https://doi.org/10.1016/j.rcim.2021.102260

  30. Um J, Suh SH, Stroud I (2016) Int J Comput Integr Manuf 29(10):1058. https://doi.org/10.1080/0951192x.2015.1130264

    Article  Google Scholar 

  31. Milosevic M, Lukic D, Antic A, Lalic B, Ficko M, Simunovic G (2017) J Manuf Syst 42:210. https://doi.org/10.1016/j.jmsy.2016.12.010

    Article  Google Scholar 

  32. Zhao G, Cheng K, Wang W, Liu Y, Dan Z (2022). Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-022-08964-0

    Article  Google Scholar 

  33. Cheng K, Zhao G, Wang W, Liu Y (2022) Int. J. Adv. Manuf. Technol. 123(1–2):627. https://doi.org/10.1007/s00170-022-10194-3

    Article  Google Scholar 

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Acknowledgements

The author would like to thank the Intelligent Computing for Aerospace Technology Laboratory.

Funding

This work was supported by the National Natural Science Foundation of China [61972011] and [52175213]. The National Natural Science Foundation of China [62102011] also supported the article.

Author information

Authors and Affiliations

  1. Chinese Aeronautical Establishment, Bei**g, 100029, China

    Kang Cheng

  2. School of Mechanical Engineering & Automation, Beihang University, Bei**g, 100191, China

    Gang Zhao, Wei Wang & Yazui Liu

  3. China Aero-Polytechnology Establishment, Aviation Industry Corporation of China, 100028, Bei**g, China

    Deyu Hu

Authors
  1. Kang Cheng
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  2. Gang Zhao
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  3. Wei Wang
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  4. Yazui Liu
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  5. Deyu Hu
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Contributions

Kang Cheng: writing, original draft preparation, software, validation. Gang Zhao: supervision, methodology, reviewing. Wei Wang: methodology, reviewing, editing. Yazui Liu: structure, reviewing, editing. Deyu Hu: experimental support.

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Correspondence to Kang Cheng.

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Cheng, K., Zhao, G., Wang, W. et al. A STEP-NC complaint and feature-based solution for intelligent process planning. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-14064-y

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