iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s10845-015-1181-y
Rule and branch-and-bound algorithm based sequencing of machining features for process planning of complex parts | Journal of Intelligent Manufacturing Skip to main content
Springer Nature Link
Log in

Rule and branch-and-bound algorithm based sequencing of machining features for process planning of complex parts

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The machining sequence of machining features is vital to achieve efficient and high quality manufacturing of complex NC machining parts. In most feature-based process planning system, the machining features are sequenced as the lowest level unit. However, a single machining feature of complex parts such as aircraft structural parts is usually machined by multiple machining operations. The one-to-many mappings between the machining features and the machining operations cause the increase of the non-cutting tool path. In order to solve this problem, some types of machining features of complex parts are decomposed into several sub-machining features that are associated with a single machining operation individually according to the rules which are abstracted from the machining process of complex parts. Benefitting from the decomposition, the sub-machining features from different machining feature can be assembled into a sub-machining feature in order to avoid the cutting tool marks. The different types of sub-machining features are sequenced in the light of some rules which are also extracted from the machining process of complex parts. And the branch-and-bound algorithm are employed to sequence the same type sub-machining features to minimum the non-cutting tool path. A pilot feature-based process planning system has been developed based on this research, and has been used in some aircraft manufacturers in China.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Britton, G., Thimm, G., Beng, T. S., & Jiang, F. (2002). A graph representation scheme for process planning of machined parts. The International Journal of Advanced Manufacturing Technology, 20(6), 429–438.

    Article  Google Scholar 

  • Deja, M., & Siemiatkowski, M. S. (2012). Feature-based generation of machining process plans for optimised parts manufacture. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0633-x.

  • Gao, S., & Shah, J. J. (1998). Automatic recognition of interacting machining features based on minimal condition subgraph. Computer-Aided Design, 30(9), 727–739.

    Article  Google Scholar 

  • Gopala Krishna, A., & Mallikarjuna Rao, K. (2006). Optimisation of operations sequence in CAPP using an ant colony algorithm. The International Journal of Advanced Manufacturing Technology, 29(1–2), 159–164.

    Article  Google Scholar 

  • Guo, Y. W., Mileham, A. R., Owen, G. W., Maropoulos, P. G., & Li, W. D. (2009). Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(5), 485–497.

    Article  Google Scholar 

  • ISO (2006). Automation systems and integration — Product data representation and exchange. In: AP 224, Mechanical product definition for process plans using machining features: ISO.

  • Li, Y. G., Ding, Y. F., Mou, W. P., & Guo, H. (2010). Feature recognition technology for aircraft structural parts based on a holistic attribute adjacency graph. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224(2), 271–278.

  • Liu, X.-J., Yi, H., & Ni, Z.-H. (2010). Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing, 24(1), 1–13.

    Article  Google Scholar 

  • Liu, Z., & Wang, L. (2007). Sequencing of interacting prismatic machining features for process planning. Computers in Industry, 58(4), 295–303.

    Article  Google Scholar 

  • Reddy, S. V. B. (1999). Operation sequencing in CAPP using genetic algorithms. International Journal of Production Research, 37(5), 1063–1074.

    Article  Google Scholar 

  • Salehi, M., & Bahreininejad, A. (2011). Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. Journal of Intelligent Manufacturing, 22(4), 643–652.

    Article  Google Scholar 

  • Tseng, Y.-J., & Joshi, S. B. (1998). Recognition of interacting rotational and prismatic machining features from 3-D mill-turn parts. International Journal of Production Research, 36(11), 3147–3165.

    Article  Google Scholar 

  • Waiyagan, K., & Bohez, E. L. J. (2009). Intelligent feature based process planning for five-axis mill-turn parts. Computers in Industry, 60(5), 296–316.

    Article  Google Scholar 

  • Wong, T. N., Chan, L. C. F., & Lau, H. C. W. (2003). Machining process sequencing with fuzzy expert system and genetic algorithms. Engineering with Computers, 19(2–3), 191–202.

    Article  Google Scholar 

  • Wu, C.-C., Hsu, P.-H., Chen, J.-C., Wang, N.-S., & Wu, W.-H. (2010). Branch-and-bound and simulated annealing algorithms for a total weighted completion time scheduling with ready times and learning effect. The International Journal of Advanced Manufacturing Technology, 55(1–4), 341–353.

    Google Scholar 

  • Yan, X., Yamazaki, K., & Liu, J. (2000). Recognition of machining features and feature topologies from NC programs. Computer-Aided Design, 32(10), 605–616.

    Article  Google Scholar 

Download references

Acknowledgments

This work is funded by the National Science and Technology Major Project of China (2012ZX04010041).

Author information

Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Yudao Street, Nanjing, Jiangsu, China

    Wei Wang, Yingguang Li & Lingling Huang

Authors
  1. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingguang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingling Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingguang Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, W., Li, Y. & Huang, L. Rule and branch-and-bound algorithm based sequencing of machining features for process planning of complex parts. J Intell Manuf 29, 1329–1336 (2018). https://doi.org/10.1007/s10845-015-1181-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1181-y

Keywords

Search

Navigation