Skip to main content
Log in

Similarity measurement of the geometry variation sequence of intermediate process model

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The reuse of machining process, by which the process for a new mechanical part is determined by referencing to the existing and matured processes, is an effective way of improving manufacturing and supporting innovation. To conduct the effective reuse, it is necessary to express and retrieve a specific process. A kernel technique of the expression and the retrieval is to measure the similarity of part’s geometry variation during the machining. To address this problem, a general framework of measuring the similarity between parts is proposed in this work. The geometry variation sequence of intermediate process model was established, and then a method to measure its similarity was invented. Two case studies are rendered and the result reveals that the proposed method is effective and can provide the support for the process retrieval and reuse in industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. H. C. Chang, L. Dong, F. X. Liu and W. F. Lu, Indexing and retrieval in machining process planning using case-based reasoning, Artificial Intelligence in Engineering, 14(1) (2000) 1–13.

    Article  Google Scholar 

  2. M. EI-Mehalawi and R. A. Miller, A database system of mechanical components based on geometric and topological similarity: part II. Indexing, retrieval, matching, and similarity assessment, Computer Aided Design, 35(1) (2003) 95–105.

    Article  Google Scholar 

  3. J. C. Cuillière, V. François, K. Souaissa, A. Benamara and H. Belhadjsalah, Automatic comparison and remeshing applied to CAD model modification, Computer Aided Design, 43(12) (2011) 1545–1560.

    Article  Google Scholar 

  4. C. Zhang and T. Chen, Indexing and retrieval of 3D models aided by active learning, Proc. of ACM Multimedia, Ottawa, Ontario, Canada (2001) 615–616.

  5. S. Hou, K. Lou and K. Ramani, Svm-based semantic clustering and retrieval of a 3d model database, Computer-Aided Design and Applications, 2(1–4) (2005) 155–164.

    Article  Google Scholar 

  6. R. Ohbuchi and J. Kobayashi, Unsupervised learning from a corpus for shape-based 3D model retrieval, Proc. of ACM Multimedia, Santa Barbara, California, USA (2006) 163–172.

  7. S. Biundo, D. Dengler and J. Koehler, Deductive planning and plan reuse in a command language environment, Proc. of European Conference on Artificial Intelligence, Saarbrücken Saarland, Germany (1992) 628–632.

  8. S. Kambhampati, Mapping and retrieval during plan reuse: a validation structure based approach, Proc. of AAAI Conference (1990) 170–175.

  9. S. Liu, Z. Zhang and X. Tian, A typical process route discovery method based on clustering analysis, International J. of Advanced Manufacturing Technology, 35(1–2) (2007) 186–194.

    Article  Google Scholar 

  10. Z. Jiang, Y. Jiang, Y. Wang, H. Zhang, H. Cao and G. Tian, A hybrid approach of rough set and case-based reasoning to remanufacturing process planning, J. of Intelligent Manufacturing, 30(1) (2019) 19–32.

    Article  Google Scholar 

  11. M. Alemanni, F. Destefanis and E. Vezzetti, Model-based definition design in the product lifecycle management scenario, International J. of Advanced Manufacturing Technology, 52(1–4) (2011) 1–14.

    Article  Google Scholar 

  12. R. Huang, S. Zhang and X. Bai, Multi-level structuralized model-based definition model based on machining features for manufacturing reuse of mechanical parts, International J. of Advanced Manufacturing Technology, 75(5-8) (2014) 1035–1048.

    Article  Google Scholar 

  13. X. Zhang, C. Liang and W. Y. Li, Automatic process intermediate model generation in process planning, Proc. of Advanced Materials Research, Guangzhou, Guangdong, China (2013) 1436–1443.

  14. X. Zhang, C. Liang, T. Si and D. Ding, Machining feature modeling and process intermediate model generation in process planning, Proc. of ASME Design Engineering Technical Conference, Portland, Oregon, USA (2013) V03BT03A010.

  15. H. Zhu, M. C. Zhou and R. Alkins, Group role assignment via a Kuhn-Munkres algorithm-based solution, IEEE Transactions on Systems, Man, and Cybernetics: Part A. Systems and Humans, 42(3) (2012) 739–750.

    Article  Google Scholar 

  16. Z. W. Yuan and H. Zhang, Research on application of Kuhn-Munkres algorithm in emergency resources dispatch problem, Proc. of International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China (2012) 2774–2777.

  17. S. F. Altschul, W. Gish, W. Miller, E. W. Myers and D. J. Lipman, Basic local aligment search tool, J. Molecular Biology, 215(3) (1990) 403–410.

    Article  Google Scholar 

  18. C. Li, R. Mo, Z. Chang, H. Yahng, N. Wan and Y. Xiang, A multifactor decision-making method for process route planning, International J. of Advanced Manufacturing Technology, 90(5–8) (2017) 1789–1808.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2019JQ-896) and the Key Research and Development Program of Shaanxi (Program No. 2019GY-091), P.R. China.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chunlei Li.

Additional information

Chunlei Li is a Lecturer in Mechanical Engineering, Baoji University of Arts and Sciences, Baoji Shaanxi, P. R. China. He received his Ph.D. in Aeronautical and Astronautical Manufacturing Engineering from Northwestern Polytechnical University. His research interests include digital design and manufacturing of complex products, intelligent process design and knowledge engineering.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Li, L. & Wang, X. Similarity measurement of the geometry variation sequence of intermediate process model. J Mech Sci Technol 35, 3089–3100 (2021). https://doi.org/10.1007/s12206-021-0631-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-021-0631-z

Keywords

Navigation