A fuzzy logic-based model in laser-assisted bending springback control

  • Gennaro Salvatore Ponticelli
  • Stefano Guarino
  • Oliviero Giannini


The present investigation deals with the proposal of a fuzzy model able to describe the inherent uncertainties related to manufacturing processes and is applied to a laser-assisted bending process. The use of such a model is aimed at controlling of the springback phenomena, which occurs during the hybrid forming process, for different set of laser process parameters, i.e., initial deflection, laser power, laser scan speed, and number of passes. In particular, the uncertainties are propagated to the residual springback by the General Transformation Method, providing only an input-output relation. The fuzzy results are then compared with the measured data leading to the evaluation of the membership level of the dataset to the uncertain model. The process maps obtained are used to select operational parameters in order to obtain a desired process output, providing as additional information how much the uncertainty of the model and the process varies by changing those operational parameters. The large variability of the process is highlighted by the fuzzy model through large band of uncertainty that occur in all the process maps generated. The fuzzy model has also been used to assess the optimal parameters in order to satisfy the requirement of the least-cost. In this case, it resulted to be convenient reduce the number of passes and use the highest laser power.


Fuzzy logic Springback Laser-assisted bending 


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  1. 1.
    Petkovic D, Nikolic V, Milovancevic M, Lazov L (2016) Estimation of the most influential factors on the laser cutting process heat affected zone (HAZ) by adaptive neuro-fuzzy technique. Infrared Phys Technol 77:12–15. CrossRefGoogle Scholar
  2. 2.
    Parandoush P, Hossain A (2014) A review of modeling and simulation of laser beam machining. Int J Mach Tools Manuf 85:135–145. CrossRefGoogle Scholar
  3. 3.
    Shen H, Vollertsen F (2009) Modelling of laser forming—an review. Comput Mater Sci 46(4):834–840. CrossRefGoogle Scholar
  4. 4.
    Balaji M, Murthy BSN, Rao NM (2016) Optimization of cutting parameters in drilling of AISI 304 stainless steel using Taguchi and ANOVA. Procedia Technol 25:1106–1113. CrossRefGoogle Scholar
  5. 5.
    Casalino G, Losacco AM, Arnesano A, Facchini F, Pierangeli M, Bonserio C (2017) Statistical analysis and modelling of an Yb: KGW femtosecond laser micro-drilling process. Procedia CIRP 62:275–280. CrossRefGoogle Scholar
  6. 6.
    Leone C, Genna S, Tagliaferri F, Palumbo B, Dix M (2016) Experimental investigation on laser milling of aluminium oxide using a 30W Q-switched Yb:YAG fiber laser. Opt Laser Technol 76:127–137. CrossRefGoogle Scholar
  7. 7.
    Guarino S, Ponticelli GS, Giannini O, Genna S, Trovalusci F (2017) Laser milling of yttria-stabilized zirconia by using a Q-switched Yb:YAG fiber laser: experimental analysis. Int J Adv Manuf Technol.
  8. 8.
    Lambiase F (2012) An analytical model for evaluation of bending angle in laser forming of metal sheets. J Mater Eng Perform 21(10):2044–2052. CrossRefGoogle Scholar
  9. 9.
    Hoseinpour Gollo M, Mahdavian SM, Moslemi Naeini H (2011) Statistical analysis of parameter effects on bending angle in laser forming process by pulsed Nd:YAG laser. Opt Laser Technol 43(3):475–482. CrossRefGoogle Scholar
  10. 10.
    Yan Y, Li L, Sezer K, Whitehead D, Ji L, Bao Y, Jiang Y (2012) Nano-second pulsed DPSS Nd:YAG laser striation-free cutting of alumina sheets. Int J Mach Tools Manuf 53(1):15–26. CrossRefGoogle Scholar
  11. 11.
    Kant R, Joshi SN (2013) Finite element simulation of laser assisted bending with moving mechanical load. Int J Mechatronics Manuf Syst 6(4):351. Google Scholar
  12. 12.
    Zhang P, Guo B, Shan D-B, Ji Z (2007) FE simulation of laser curve bending of sheet metals. J Mater Process Technol 184(1-3):157–162. CrossRefGoogle Scholar
  13. 13.
    Zhang L, Reutzel EW, Michaleris P (2004) Finite element modeling discretization requirements for the laser forming process. Int J Mech Sci 46(4):623–637. CrossRefzbMATHGoogle Scholar
  14. 14.
    Roohi AH, Gollo MH, Naeini HM (2012) External force-assisted laser forming process for gaining high bending angles. J Manuf Process 14(3):269–276. CrossRefGoogle Scholar
  15. 15.
    Guarino S, Ponticelli GS (2017) High power diode laser (HPDL) for fatigue life improvement of steel: numerical modelling. Metals (Basel) 7(10):447. CrossRefGoogle Scholar
  16. 16.
    Nikolic V, Petkovic D, Lazov L, Milovancevic M (2016) Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique. Infrared Phys Technol 77:45–50. CrossRefGoogle Scholar
  17. 17.
    D’Addona DM, Genna S, Leone C, Matarazzo D (2016) Prediction of poly-methyl-methacrylate laser milling process characteristics based on neural networks and fuzzy data. Procedia CIRP 41:981–986. CrossRefGoogle Scholar
  18. 18.
    Akbari M, Saedodin S, Panjehpour A, Hassani M, Afrand M, Torkamany MJ (2016) Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy. Optik (Stuttg) 127(23):11161–11172. CrossRefGoogle Scholar
  19. 19.
    Pandey AK, Dubey AK (2012) Taguchi based fuzzy logic optimization of multiple quality characteristics in laser cutting of duralumin sheet. Opt Lasers Eng 50(3):328–335. CrossRefGoogle Scholar
  20. 20.
    Syn CZ, Mokhtar M, Feng CJ, Manurung YHP (2011) Approach to prediction of laser cutting quality by employing fuzzy expert system. Expert Syst Appl 38(6):7558–7568. CrossRefGoogle Scholar
  21. 21.
    Pandey AK, Dubey AK (2013) Fuzzy expert system for prediction of kerf qualities in pulsed laser cutting of titanium alloy sheet. Mach Sci Technol 17(4):545–574. CrossRefGoogle Scholar
  22. 22.
    Hossain A, Hossain A, Nukman Y, Hassan MA, Harizam MZ, Sifullah AM, Parandoush P (2016) A fuzzy logic-based prediction model for kerf width in laser beam machining. Mater Manuf Process 31(5):679–684. CrossRefGoogle Scholar
  23. 23.
    Cheng JG, Yao YL (2004) Process synthesis of laser forming by genetic algorithm. Int J Mach Tools Manuf 44(15):1619–1628. CrossRefGoogle Scholar
  24. 24.
    Tsai M-J, Li C-H, Chen C-C (2008) Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. J Mater Process Technol 208(1-3):270–283. CrossRefGoogle Scholar
  25. 25.
    Kumar S, Dubey AK, Pandey AK (2013) Computer-aided genetic algorithm based multi-objective optimization of laser trepan drilling. Int J Precis Eng Manuf 14(7):1119–1125. CrossRefGoogle Scholar
  26. 26.
    Rodger JA (2014) Application of a fuzzy feasibility Bayesian probabilistic estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets. Expert Syst Appl 41(16):7005–7022. CrossRefGoogle Scholar
  27. 27.
    Gisario A, Barletta M, Conti C, Guarino S (2011) Springback control in sheet metal bending by laser-assisted bending: experimental analysis, empirical and neural network modelling. Opt Lasers Eng 49(12):1372–1383. CrossRefGoogle Scholar
  28. 28.
    Gisario A, Barletta M, Venettacci S, Veniali F (2015) Laser-assisted bending of sharp angles with small fillet radius on stainless steel sheets: analysis of experimental set-up and processing parameters. Lasers Manuf Mater Process 2(2):57–73. CrossRefGoogle Scholar
  29. 29.
    Hu Y, Luo M, Yao Z (2016) Increasing the capability of laser peen forming to bend titanium alloy sheets with laser-assisted local heating. Mater Des 90:364–372. CrossRefGoogle Scholar
  30. 30.
    Yilbas BS, Akhtar SS (2014) Laser bending of metal sheet and thermal stress analysis. Opt Laser Technol 61:34–44. CrossRefGoogle Scholar
  31. 31.
    Gisario A, Mehrpouya M, Venettacci S, Barletta M (2017) Laser-assisted bending of titanium Grade-2 sheets: experimental analysis and numerical simulation. Opt Lasers Eng 92:110–119. CrossRefGoogle Scholar
  32. 32.
    Chakraborty SS, More H, Racherla V, Nath AK (2015) Modification of bent angle of mechanically formed stainless steel sheets by laser forming. J Mater Process Technol 222:128–141. CrossRefGoogle Scholar
  33. 33.
    Taheri SM (2003) Trends in fuzzy statistics. Austrian J Stat 32:239–257. Accessed 4 Jul 2017
  34. 34.
    Haag T, Herrmann J, Hanss M (2010) Identification procedure for epistemic uncertainties using inverse fuzzy arithmetic. Mech Syst Signal Process 24(7):2021–2034. CrossRefGoogle Scholar
  35. 35.
    Giannini O, Hanss M (2008) The component mode transformation method: a fast implementation of fuzzy arithmetic for uncertainty management in structural dynamics. J Sound Vib 311(3-5):1340–1357. CrossRefGoogle Scholar
  36. 36.
    Hanss M (2002) The transformation method for the simulation and analysis of systems with uncertain parameters. Fuzzy Sets Syst 130(3):277–289. Accessed 5 Jul 2017,
  37. 37.
    Moore MJ, Kearfott RE, Cloud RB (1966) Introduction to interval analysisGoogle Scholar
  38. 38.
    Zadeh LA (1965) Fuzzy Sets. Inf Control 8(3):338–353. CrossRefzbMATHGoogle Scholar
  39. 39.
    Angelov P, Xydeas C (2006) Fuzzy systems design: direct and indirect approaches. Soft Comput 10(9):836–849. CrossRefGoogle Scholar
  40. 40.
    Filev D, Larsson T, Lixing Ma (n.d.) Intelligent control for automotive manufacturing-rule based guided adaptation, in: 2000 26th Annu. Conf. IEEE Ind. Electron. Soc. IECON 2000. 2000 I.E. Int. Conf. Ind. Electron. Control Instrumentation. 21st Century Technol. Ind. Oppor. (Cat. No.00CH37141), IEEE, pp. 283–288.
  41. 41.
    Park H-J, Jang J-Y, Lee J-H (2017) Physically based susceptibility assessment of rainfall-induced shallow landslides using a fuzzy point estimate method. Remote Sens 9(5):487. CrossRefGoogle Scholar
  42. 42.
    Alimardani M, Toyserkani E (2008) Prediction of laser solid freeform fabrication using neuro-fuzzy method. Appl Soft Comput 8(1):316–323. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2017

Authors and Affiliations

  • Gennaro Salvatore Ponticelli
    • 1
  • Stefano Guarino
    • 1
  • Oliviero Giannini
    • 1
  1. 1.University ‘Niccolò Cusano’RomeItaly

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