Skip to main content
Log in

Laser cutting path optimization using simulated annealing with an adaptive large neighborhood search

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

Abstract

A simulated annealing algorithm combined with an adaptive large neighborhood search (ALNS) has been proposed in this paper to minimize the laser cutting path in a two-dimensional cutting process. The proposed algorithm was capable of finding a near-optimum cutting path from a given layout of cut profiles. In this study, the layout was taken from an image, in which image processing algorithms were employed to extract cut profiles from the input image and to assign coordinates to the contours’ pixels. The optimization algorithm was based on generalized traveling salesman problem (GTSP), where all pixels of the input image were considered as the potential piercing locations. A laser beam made a single visit and then did a complete cut of each profile consecutively. The simulation results revealed that the proposed algorithm can successfully solve several datasets from GTSP-Lib database with a good solution quality. A compromise between the path distance and computing time was achievable by considering only 30% of the total pixels of the input image examined in this study. In addition, the cutting path generated by the proposed method was shorter than that recommended by the commercial CAM software and other previous works in terms of path distance with the same profile sample.

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. Anand KV, Babu AR (2015) Heuristic and genetic approach for nesting of two-dimensional rectangular shaped parts with common cutting edge concept for laser cutting and profile blanking processes. Comput Ind Eng 80:111–124. https://doi.org/10.1016/j.cie.2014.11.018

    Article  Google Scholar 

  2. Wu ZY, Ling H, Li L, Wu LH, Liu NB (2017) Research on cutting path optimization of sheet metal parts based on ant colony algorithm. IOP Conf Series: Materials Science and Engineering 242:012099. https://doi.org/10.1088/1757-899X/242/1/012099

    Article  Google Scholar 

  3. Lee M-K, Kwon K-B (2006) Cutting path optimization in CNC cutting processes using a two-step genetic algorithm. Int J Prod Res 44(24):5307–5326. https://doi.org/10.1080/00207540600579615

    Article  MATH  Google Scholar 

  4. Manber U, Israni S (1984) Pierce point minimization and optimal torch path determination in flame cutting. J Manuf Syst 3(1):81–89. https://doi.org/10.1016/0278-6125(84)90024-4

    Article  Google Scholar 

  5. Chen JC, Zhong TX (2002) A hybrid-coded genetic algorithm based optimization of non-productive paths in CNC Machining. Int J Adv Manuf Technol 20(3):163–168. https://doi.org/10.1007/s001700200139

    Article  MathSciNet  Google Scholar 

  6. Han G-C, Na S-J (1998) Global torch path generation for 2-D laser cutting process using simulated annealing. Intelligent Automation & Soft Computing 4(2):97–108. https://doi.org/10.1080/10798587.1998.10750725

    Article  Google Scholar 

  7. Kim Y, Gotoh K, Toyosada M (2004) Global cutting-path optimization considering the minimum heat effect with microgenetic algorithms. J Mar Sci Technol 9(2):70–79. https://doi.org/10.1007/s00773-004-0176-8

    Article  Google Scholar 

  8. Dewil R, Vansteenwegen P, Cattrysse D (2015) Sheet metal laser cutting tool path generation: dealing with overlooked problem aspects. Key Eng Mater 639:517–524. https://doi.org/10.4028/www.scientific.net/KEM.639.517

    Article  Google Scholar 

  9. Petunin AA, Stylios C (2016) Optimization model of tool path problem for CNC Sheet metal cutting machines. IFAC-PapersOnline 49(12):023–028. https://doi.org/10.1016/j.ifacol.2016.07.544

    Article  Google Scholar 

  10. Chentsov AG, Chentsov PA, Petunin AA, Sesekin AN (2018) Model of megalopolises in the tool path optimization for CNC plate cutting machine. Int J Prod Res 56:4819–4830. https://doi.org/10.1080/00207543.2017.1421784

    Article  Google Scholar 

  11. Oysu C, Bingul Z (2009) Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining. Eng Appl Artif Intell 22:389–396. https://doi.org/10.1016/j.engappai.2008.10.005

    Article  Google Scholar 

  12. Du HQ, Qi JB (2010) Application of a hybrid algorithm based on genetic algorithm and hill-climbing algorithm to tool path optimization in CNC machining. Adv Mater Res 102(104):681–685. https://doi.org/10.4028/www.scientific.net/AMR.102-104.681

    Article  Google Scholar 

  13. Liu X, Hong Y, Zhonghua N, Jianchang Q (2013) Process planning optimization of hole-making operations using ant colony algorithm. Int J Adv Manuf Technol 69(1-4):753–769. https://doi.org/10.1007/s00170-013-5067-x

    Article  Google Scholar 

  14. Dewil R, Kucukoglu I, Luteyn C, Cattrysse D (2018) A critical review of multi-hole drilling path optimization. Arch Comput Meth Eng 26:449–459. https://doi.org/10.1007/s11831-018-9251-x

    Article  Google Scholar 

  15. Zhang C, Han F, Zhang W (2018) A cutting sequence optimization method based on tabu search algorithm for complex parts machining. J Eng Manuf 233:745–755. https://doi.org/10.1177/0954405417752527

    Article  Google Scholar 

  16. Laporte G, Mercure H (1987) Generalized traveling salesman problem through n sets of nodes, the asymmetrical case. Discret Appl Math 18:185–197. https://doi.org/10.1016/0166-218X(87)90020-5

    Article  MATH  Google Scholar 

  17. Fischetti M, Gonzales JJS, Toth P (1997) A branch-and-cut algorithm for the symmetric generalized traveling salesman problem. Oper Res 45(3):378–394. https://doi.org/10.1287/opre.45.3.378

    Article  MathSciNet  MATH  Google Scholar 

  18. Gutin G, Karapetyan D (2010) A memetic algorithm for the generalized traveling salesman problem. Nat Comput 9:47–60. https://doi.org/10.1007/s11047-009-9111-6

    Article  MathSciNet  MATH  Google Scholar 

  19. GTSP-Lib datasets. http://www.cs.nott.ac.uk/~pszdk/gtsp.html. Accessed 24 July 2012

  20. Karapetyan D, Gutin G (2011) Lin-Kernighan heuritic adaptation for the generalized traveling salesman problem. Eur J Oper Res 208(3):221–232. https://doi.org/10.1016/j.ejor.2010.08.011

    Article  MATH  Google Scholar 

  21. Hesgaun K (2015) Solving the equality generalized traveling salesman problem using the Lin-Kernighan-Helsgaun algorithm. Math Program Comput 7(3):269–287. https://doi.org/10.1007/s12532-015-0080-8

    Article  MathSciNet  MATH  Google Scholar 

  22. Smith SL, Imeson F (2017) GLNS: an effective large neighborhood search heuristic for the generalized traveling salesman problem. Comput Oper Res 8:1–19. https://doi.org/10.1016/j.cor.2017.05.010

    Article  MathSciNet  MATH  Google Scholar 

  23. Reihaneh M, Karapetyan D (2012) An efficient hybrid ant colony system for the generalized traveling salesman problem. Algorithmic Operations Research 7:22–29

    MathSciNet  MATH  Google Scholar 

  24. Pintea PCP, Chira C (2017) The generalized traveling salesman problem solved with ant algorithm. Complex Adaptive Systems Modeling 5:8. https://doi.org/10.1186/s40294-017-0048-9

    Article  Google Scholar 

  25. Shi XH, Liang YC, Lee HP, Lu C, Wang QX (2007) Particle swarm optimization-based algorithms for TSP and generalized TSP. Inf Process Lett 103:169–176. https://doi.org/10.1016/j.ipl.2007.03.010

    Article  MathSciNet  MATH  Google Scholar 

  26. Ardalan Z, Karimi S, Poursabri O, Naderi B (2015) A novel imperialist competitive algorithm for generalized traveling salesman problems. Appl Soft Comput 26:546–555. https://doi.org/10.1016/j.asoc.2014.08.033

    Article  Google Scholar 

  27. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680. https://doi.org/10.1142/9789812799371_0035

    Article  MathSciNet  MATH  Google Scholar 

  28. Gogna A, Tayal A (2013) Metaheuristics: review and application. J Exp Theor Artif Intell 25(4):503–526. https://doi.org/10.1080/0952813X.2013.782347

    Article  Google Scholar 

  29. Talbi E-G (2009) Simulated annealing. In: Metaheuristics from Design to Implementation. Wiley

  30. Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp Sci 40(4):455–472. https://doi.org/10.1287/trsc.1050.0135

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Petchra Pra Jom Klao Ph.D. Research Scholarship from King Mongkut’s University of Technology Thonburi. The authors acknowledge the financial support provided by King Mongkut’s University of Technology Thonburi through the “KMUTT 55th Anniversary Commemorative Fund.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Viboon Tangwarodomnukun.

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

Hajad, M., Tangwarodomnukun, V., Jaturanonda, C. et al. Laser cutting path optimization using simulated annealing with an adaptive large neighborhood search. Int J Adv Manuf Technol 103, 781–792 (2019). https://doi.org/10.1007/s00170-019-03569-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-03569-6

Keywords

Navigation