Skip to main content
Log in

An adaptive-group-based differential evolution algorithm for inspecting machined workpiece path planning

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In the precision manufacturing process, accuracy and precision are crucial when designing a workpiece inspection system. An efficient system minimizes inefficiencies caused by workpieces failing to meet customer needs and delays caused by slow workpiece inspection. In this study, a workpiece inspection system for measuring path planning is proposed that uses the given coordinate of inspection points discerned from 3D images. Then, an adaptive-group-based differential evolution (AGDE) algorithm is used to optimize the measuring path. The AGDE algorithm incorporates the grouping concept into conventional differential evolution, and this improves local search ability through referencing the direction of the best solution in each group. By using the proposed method, the shortest non-colliding measuring path is obtained. Moreover, the proposed workpiece inspection system shortens the workpiece inspection time and achieves faster performance than manual measuring path planning under multiple workpiece inspection points.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Robins M (2006) Quality innovations: a scanning revolution in five axes. Qual Mag

  2. Chen Y, Ma Z, Xu H (Aug. 2009) Key technologies of 3D surface inspection for complex workpiece using OMP60 probe. IEEE Int Conf Autom Log 223–227

  3. Zakharov OV, Balaev AF, Kochetkov AV (2017) Modeling optimal path of touch sensor of coordinate measuring machine based on traveling salesman problem solution. Proc Eng 206:1458–1463

    Article  Google Scholar 

  4. Lu CG, Morton D, Wu MH, Myler P (1999) Genetic algorithm modelling and solution of inspection path planning on a coordinate measuring machine (CMM). Int J Adv Manuf Technol 15:409–416

    Article  Google Scholar 

  5. Duzn X, Xu Y, Wang X, Liu W, Huo Y, Ma H (2008) Application of on-line inspection probe of machining center in free curve inspecting. World Congress on Intelligent Control and Automation, pp. 6036–6040

  6. Lin ZC, Chen CC (1997) Measuring-sequence planning by the nearest neighbour method and the refinement method. Int J Adv Manuf Technol 13(4):271–281

    Article  Google Scholar 

  7. Lin YJ, Murugappan P (1999) A new algorithm for determining a collision-free path for a CMM probe. Int J Mach Tools Manuf 39(9):1397–1408

    Article  Google Scholar 

  8. Limaiem A, Eimaraghy HA (1998) Automatic path planning for coordinate measuring machines. IEEE Int Conf Robot Autom 1:887–892

    Article  Google Scholar 

  9. Knuth DE (1977) A generalization of Dijkstra’s algorithm. Inf Process Lett 6(1):1–5

    Article  MathSciNet  Google Scholar 

  10. Xia R, Lu R (2011) Inspection path planning of on-machine vision inspection for CNC milling machines. J Electron Meas Instrum 35:722–727

    Article  Google Scholar 

  11. Snydera LV, Daskinb MS (2006) A random-key genetic algorithm for the generalized traveling salesman problem. Eur J Oper Res 174:38–53

    Article  MathSciNet  Google Scholar 

  12. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. J Comput 6(2):154–160

    MATH  Google Scholar 

  13. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  14. Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948

    Google Scholar 

  15. Holland JH (1992) Genetic algorithms. J Article 267(1):66–73

    Google Scholar 

  16. Peng H, Guo Z, Deng C, Wu Z (2018) Enhancing differential evolution with random neighbors based strategy. J Comput Sci 26:501–511

    Article  MathSciNet  Google Scholar 

  17. Cai Y, Liao J, Wang T, Chen Y, Tian H (2018) Social learning differential evolution. Inf Sci 433-434:464–509

    Article  MathSciNet  Google Scholar 

Download references

Funding

The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 107-2221-E-167-023

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cheng-Jian Lin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, CJ., Lin, CH. An adaptive-group-based differential evolution algorithm for inspecting machined workpiece path planning. Int J Adv Manuf Technol 105, 2647–2657 (2019). https://doi.org/10.1007/s00170-019-04521-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04521-4

Keywords

Navigation