Skip to main content
SpringerLink
Log in

Comparison of Heuristic Algorithms for Path Planning in 3D Printing with Multistage Experimentation System

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

Included in the following conference series:

  • 1740 Accesses

Abstract

The objective of this work is to present the implemented algorithms to solving path planning in 3D printing problem. The algorithms have been compared on the basis of the obtained results of simulation experiments. As the indices of algorithm’s performance the total distance, the energy cost, and the time cost are taken into account. The designed and implemented two-stage experimentation system is described. The simulation experiments have been carried on along with own experiment design. Short analysis of the obtained results allowed for recommendation of the most promising algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Campbell, T., Williams, Ch., Ivanova, O, Garrett, B.: Could 3D printing change the world? Technologies, potential, and implications of additive manufacturing, Strategic Foresight Report, The Atlantic Council 2011(1) (2011)

    Google Scholar 

  2. Gebhardt, A., Fateri, M.: 3D printing and its applications. RTE J. Forum Rapid Technol. (2013)

    Google Scholar 

  3. Rybicki, F.J., Grant, G.T.: 3D Printing in Medicine: A Practical Guide for Medical Professionals. Springer, AG (2017). https://doi.org/10.1007/978-3-319-61924-8

    Book  Google Scholar 

  4. Wojcik, M., Koszalka, L., Pozniak-Koszalka, I., Kasprzak, A.: MZZ-GA algorithm for solving path optimization in 3D printing. In: Proceedings of the Tenth International Conference on Systems (ICONS), pp. 30–35 (2015)

    Google Scholar 

  5. Lechowicz, P., Koszalka, L., Pozniak-Koszalka, L., Kasprzak, A.: Path optimization in 3D printer: algorithms and experimentation system. In: Proceedings of the 4th International Symposium on Computational and Business Intelligence (ISCBI), pp. 137–142 (2016)

    Google Scholar 

  6. https://en.org/wiki/RepRap_Project. Accessed Nov 2018

  7. https://en.org/wiki/MakerBot. Accessed Nov 2018

  8. https://www.engineering.com/3DPrinting/3DPrintingArticles/ArticleID/11847/Atlas-V-Rocket-Soars-from-Earth-with-3D-Printed-Plastic-Parts.aspx. Accessed Nov 2018

  9. Ngo, T.D., Kashani, A., Imbalzano, G., Nguyen, K.T.Q., Hui, D.: Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos. Part B Eng. 143, 172–196 (2018)

    Article  Google Scholar 

  10. https://3dprintingindustry.com/news/whats-next-3d-printing-2018-additive-manufacturing-expert-perspective-126680/. Accessed Dec 2018

  11. Castelino, K., D’Souza, R., Wright, P.K.: Toolpath optimization for minimizing airtime during machining. J. Manuf. Syst. 22(3), 173–180 (2003)

    Article  Google Scholar 

  12. Wah, P.K., Murty, K.G., Joneja, A., Chiu, L.C.: Tool path optimization in layered manufacturing. IIE Trans. 34(4), 335–347 (2002)

    Google Scholar 

  13. Suh, S.-H., Shin, Y.-S.: Neural network modeling for tool path planning of the rough cut in complex pocket milling. J. Manuf. Syst. 15(5), 295–304 (1996)

    Article  Google Scholar 

  14. Cormen, T.H., Leiserson, C.E., Rivest, L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  15. Engels, C., Manthey, B.: Average-case approximation ratio of the 2-opt algorithm for the TSP. Oper. Res. Lett. 37(2), 83–84 (2009)

    Article  MathSciNet  Google Scholar 

  16. McGovern, S.M., Gupta, S.M.: Disassembly Line: Balancing and Modeling with Greedy 2-Opt Hybrid. McGraw-Hill, New York (2011)

    Google Scholar 

  17. Yadav, A., Yadav, N., Kim, J.H.: A study of harmony search algorithms: exploration and convergence ability. In: Kim, J.H., Geem, Z.W. (eds.) Harmony Search Algorithm. AISC, vol. 382, pp. 53–62. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-47926-1_6

    Chapter  Google Scholar 

  18. Dowsland, K., Thompson, J.: Simulated annealing. In: Rozenberg, G., Back, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 1623–1655. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-540-92910-9_49

    Chapter  Google Scholar 

  19. Geng, X., Chen, Z., Yang, W., Shi, D., Zhao, K.: Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search. Appl. Soft Comput. 11, 3680–3689 (2011)

    Article  Google Scholar 

  20. Hudziak, M., Pozniak-Koszalka, I., Koszalka, L., Kasprzak, A.: Multi-agent pathfinding in the crowded environment with obstacles: algorithms and experimentation system. J. Intell. Fuzzy Syst. 32(2), 1561–1573 (2017)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland

    Martyna Poslednik, Iwona Pozniak-Koszalka, Leszek Koszalka & Andrzej Kasprzak

Authors
  1. Martyna Poslednik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iwona Pozniak-Koszalka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leszek Koszalka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrzej Kasprzak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iwona Pozniak-Koszalka .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Poslednik, M., Pozniak-Koszalka, I., Koszalka, L., Kasprzak, A. (2019). Comparison of Heuristic Algorithms for Path Planning in 3D Printing with Multistage Experimentation System. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-28377-3_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation