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

  • Cheng-Jian LinEmail author
  • Chun-Hui Lin
ORIGINAL ARTICLE
  • 23 Downloads

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.

Keywords

Path planning Workpiece inspection Differential evolution Grouping Measurement 

Notes

Funding information

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

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Robins M (2006) Quality innovations: a scanning revolution in five axes. Qual MagGoogle Scholar
  2. 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 223–227Google Scholar
  3. 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 206:1458–1463CrossRefGoogle Scholar
  4. 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 15:409–416CrossRefGoogle Scholar
  5. 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. 6036–6040Google Scholar
  6. 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):271–281CrossRefGoogle Scholar
  7. 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):1397–1408CrossRefGoogle Scholar
  8. 8.
    Limaiem A, Eimaraghy HA (1998) Automatic path planning for coordinate measuring machines. IEEE Int Conf Robot Autom 1:887–892CrossRefGoogle Scholar
  9. 9.
    Knuth DE (1977) A generalization of Dijkstra’s algorithm. Inf Process Lett 6(1):1–5MathSciNetCrossRefGoogle Scholar
  10. 10.
    Xia R, Lu R (2011) Inspection path planning of on-machine vision inspection for CNC milling machines. J Electron Meas Instrum 35:722–727CrossRefGoogle Scholar
  11. 11.
    Snydera LV, Daskinb MS (2006) A random-key genetic algorithm for the generalized traveling salesman problem. Eur J Oper Res 174:38–53MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. J Comput 6(2):154–160zbMATHGoogle Scholar
  13. 13.
    Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948Google Scholar
  15. 15.
    Holland JH (1992) Genetic algorithms. J Article 267(1):66–73Google Scholar
  16. 16.
    Peng H, Guo Z, Deng C, Wu Z (2018) Enhancing differential evolution with random neighbors based strategy. J Comput Sci 26:501–511MathSciNetCrossRefGoogle Scholar
  17. 17.
    Cai Y, Liao J, Wang T, Chen Y, Tian H (2018) Social learning differential evolution. Inf Sci 433-434:464–509MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Chin-Yi University of TechnologyTaichungTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan

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