Production Engineering

, Volume 11, Issue 2, pp 195–203 | Cite as

Improving the laser cutting process design by machine learning techniques

  • Hasan Tercan
  • Toufik Al Khawli
  • Urs Eppelt
  • Christian Büscher
  • Tobias Meisen
  • Sabina Jeschke
Production Process

Abstract

In the field of manufacturing engineering, process designers conduct numerical simulation experiments to observe the impact of varying input parameters on certain outputs of the production process. The disadvantage of these simulations is that they are very time consuming and their results do not help to fully understand the underlying process. For instance, a common problem in planning processes is the choice of an appropriate machine parameter set that results in desirable process outputs. One way to overcome this problem is to use data mining techniques that extract previously unknown but valuable knowledge from simulation results. This paper presents a hybrid machine learning approach for applying clustering and classification techniques in a laser cutting planning process. In a first step, a clustering algorithm is used to divide large parts of the simulation data into groups of similar performance values and select those groups that are of major interest (e.g. high cut quality results). Next, classification trees are used to identify regions in the multidimensional parameter space that are related to the found groups. The evaluation shows that the models accurately identify multidimensional relationships between the input parameters and the output values of the process. In addition to that, a combination of appropriate visualization techniques for clustering with interpretable classification trees allows designers to gain valuable insights into the laser cutting process with the aim of optimizing it through visual exploration.

Keywords

Clustering Classification tree Artificial intelligence Hybrid machine learning Data mining Manufacturing 

Notes

Acknowledgements

The approaches presented in this paper are supported by the German Research Foundation (DFG) within the Cluster of Excellence “Integrative Production Technologies for High-Wage Countries” at RWTH Aachen University.

References

  1. 1.
    Brecher C (ed) (2012) Integrative production technology for high-wage countries. Springer, Berlin, HeidelbergGoogle Scholar
  2. 2.
    Box GEP, Hunter JS, Hunter WG (2005) Statistics for experimenters: Design, innovation, and discovery, 2. ed. Wiley series in probability and statistics. Wiley-Interscience, HobokenMATHGoogle Scholar
  3. 3.
    Otto A, Koch H, Leitz K et al (2011) Numerical simulations—a versatile approach for better understanding dynamics in laser material processing. Phys Procedia 12:11–20. doi: 10.1016/j.phpro.2011.03.003 CrossRefGoogle Scholar
  4. 4.
    Köksal G, Batmaz İ, Testik MC (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38(10):13448–13467. doi: 10.1016/j.eswa.2011.04.063 CrossRefGoogle Scholar
  5. 5.
    Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3. ed. The Morgan Kaufmann series in data management systems. Elsevier/Morgan Kaufmann, AmsterdamCrossRefGoogle Scholar
  6. 6.
    Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3. ed. The Morgan Kaufmann series in data management systems. Kaufmann, San FranciscoGoogle Scholar
  7. 7.
    Reinhard R, Büscher C, Meisen T et al (2012) Virtual Production Intelligence – A Contribution to the Digital Factory. In: Hutchison D, Kanade T, Kittler J et al (eds) Intelligent Robotics and Applications, vol 7506. Springer Berlin Heidelberg, Berlin, pp 706–715CrossRefGoogle Scholar
  8. 8.
    Reinhard R, Khawli TA, Eppelt U et al (2014) The contribution of virtual production intelligence to laser cutting planning processes. In: Zaeh MF (ed) Enabling manufacturing competitiveness and economic sustainability. Springer International Publishing, Cham, pp 117–123CrossRefGoogle Scholar
  9. 9.
    Al Khawli T, Eppelt U, Schulz W (2015) Advanced metamodeling techniques applied to multidimensional applications with piecewise responses. In: Pardalos P, Pavone M, Farinella GM et al (eds) Machine learning, optimization, and big data, vol 9432. Springer International Publishing, Cham, pp 93–104CrossRefGoogle Scholar
  10. 10.
    Tercan H, Khawli TA, Eppelt U et al (2016) Use of classification techniques to design laser cutting processes. Procedia 5CIRP6 52:292–297. doi: 10.1016/j.procir.2016.08.001 CrossRefGoogle Scholar
  11. 11.
    Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview: advances in knowledge discovery and data mining. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P et al (eds). American Association for Artificial Intelligence, Menlo Park, pp 1–34Google Scholar
  12. 12.
    Gebhardt S, Hentschel B, Kuhlen T et al. (2013) Hyperslice visualization of metamodels for manufacturing processes. In: 2013 IEEE Visualization Conference (VIS): Atlanta, GA, USA, 13 Oct–18 Oct 2013. IEEEGoogle Scholar
  13. 13.
    Madić M, Radovanović M (2012) Comparative modeling of CO2 laser cutting using multiple regression analysis and artificial neural network. Int J Phys Sci 7(16):2422–2430Google Scholar
  14. 14.
    Pandremenos J, Chryssolouris G (2011) A neural network approach for the development of modular product architectures. Int J Comput Integra Manuf 24(10):879–887. doi: 10.1080/0951192X.2011.602361 CrossRefGoogle Scholar
  15. 15.
    Cus F, Balic J (2003) Optimization of cutting process by GA approach. Robot Comput Integr Manuf 19(1–2): 113–121. doi: 10.1016/S0736-5845(02)00068-6 CrossRefGoogle Scholar
  16. 16.
    Chong I, Albin SL, Jun C (2007) A data mining approach to process optimization without an explicit quality function. IIE Trans 39(8):795–804. doi: 10.1080/07408170601142668 CrossRefGoogle Scholar
  17. 17.
    Feldkamp N, Bergmann S, Strassburger S (2015) Knowledge discovery in manufacturing simulations. In: Taylor SJ, Mustafee N, Son Y (eds) the 3rd ACM Conference, pp 3–12Google Scholar
  18. 18.
    Dubey AK, Yadava V (2008) Laser beam machining—a review. Int J Mach Tools Manuf 48(6):609–628. doi: 10.1016/j.ijmachtools.2007.10.017 CrossRefGoogle Scholar
  19. 19.
    Radovanovic M, Madic M (2011) Experimental investigations of CO2 laser cut quality: a review. Revista de Tehnologii Neconventionale 15(4):35Google Scholar
  20. 20.
    Schulz W, Kostrykin V, Zefferer H et al (1997) A free boundary problem related to laser beam fusion cutting: ODE approximation. Int J Heat Mass Transfer 40(12):2913–2928. doi: 10.1016/S0017-9310(96)00342-0 CrossRefMATHGoogle Scholar
  21. 21.
    Vossen G, Schüttler J, Nießen M (2010) Optimization of partial differential equations for minimizing the roughness of laser cutting surfaces. In: Diehl M, Glineur F, Jarlebring E et al (eds) Recent advances in optimization and its applications in engineering. Springer Berlin Heidelberg, Berlin, pp 521–530CrossRefGoogle Scholar
  22. 22.
    Vossen G, Hermanns T, Schüttler J (2015) Analysis and optimal control for free melt flow boundaries in laser cutting with distributed radiation. Z Angew Math Mech 95(3):297–316. doi: 10.1002/zamm.201200213 MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17(2/3):107–145. doi: 10.1023/A:1012801612483 CrossRefMATHGoogle Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2017

Authors and Affiliations

  • Hasan Tercan
    • 1
  • Toufik Al Khawli
    • 2
  • Urs Eppelt
    • 2
  • Christian Büscher
    • 1
  • Tobias Meisen
    • 1
  • Sabina Jeschke
    • 1
  1. 1.Institute of Information Management in Mechanical Engineering (IMA)RWTH Aachen UniversityAachenGermany
  2. 2.Department for Nonlinear Dynamics of Laser Processing (NLD)RWTH Aachen UniversityAachenGermany

Personalised recommendations