Skip to main content
SpringerLink
Log in

Selection of laser bending process parameters for maximal deformation angle through neural network and teaching–learning-based optimization algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The present study focused on selecting optimal factors combination that causes maximum bending angle in laser bending of AA6061-T6. For this purpose a L\(_{25}\) Taguchi orthogonal design (four factors-five levels) is used to design experiments. Here, the process main factors are laser power, spot diameter, pulse duration and scanning speed and the main response was bending angle. To correlate relationship between process factors and bending angle, a radial basis function neural network (RBFNN) was utilized. Then the developed RBFNN model was used as an objective function for maximizing deformation through teaching–learning-based optimization algorithm. Results indicated that the laser power of 3.6 kW, spot diameter of 2 mm, pulse duration of 0.9 ms and scanning speed of 2 mm/s lead to maximal bending angle about 28.7\(^\circ \). Hereafter the optimal results have been verified by confirmatory experiments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Babajanzade-Roshan S, Behboodi-Jooibari M, Teimouri R, Asgharzade-Ahmadi G, Falahati-Naghibi M, Sohrabpoor H (2013) Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm. Int J Adv Manuf Technol. doi:10.1007/s00170-013-5131-6

  • Baseri H, Rahmani B, Bakhshi M (2011) Selection of bending parameters for minimal spring-back using an ANFIS model and simulated annealing algorithm. J Manuf Sci Eng ASME Trans. doi:10.1115/1.4004139

  • Cheng PJ, Lin SC (2000) Using neural network to predict bending angle sheet metal formed by laser. Int J Mach Tools Manuf 40:1185–1197

    Article  Google Scholar 

  • Gisario A, Barletta M, Conti C, Guarino S (2011) Spring back control in sheet metal bending: experimental analysis, empirical and neural network modeling. Opt Laser Eng 12:1372–1383

    Article  Google Scholar 

  • Hu Z, Labudovic M, Wang H, Kovacevic R (2001) Computer simulation and experimental investigation of sheet metal bending using laser beam scanning. Int J Mach Tools Manuf 41:589–607

    Article  Google Scholar 

  • Jung H-C (2006) A study on laser forming process with finite element analysis. Fulfilled PhD thesis in University of Canterbury from New Zealand

  • Kuar AS, Doloi B, Bhattacharyya B (2006) Modeling and analysis of pulsed Nd:YAG laser machining characteristics during micro-drilling of zirconia (ZrO\(_{2})\). Int J Mach Tools Manuf 46: 1301–1310

  • Maji K, Pratihar KD, Nath AK (2012) Experimental investigations and statistical analysis of pulsed laser bending of AISI 304 stainless steel sheet. Opt Laser Technol 49:18–27

    Article  Google Scholar 

  • Maji K, Pratihar KD, Nath AK (2014) Laser forming of a dome shaped surface: experimental investigations, statistical analysis and neural network modeling. Opt Laser Eng 53:31–42

    Article  Google Scholar 

  • Maji K, Pratihar KD, Nath AK (2012) Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system. Soft Comput. doi:10.1007/s00500-012-0949-7

  • Pawar PJ, Rao RV (2012) Parameter optimization of machining processes using teaching–learning-based optimization algorithm. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4524-2

  • Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problem information. Science 183:1–15

    MathSciNet  Google Scholar 

  • Rao RV, Patel V (2012) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3:535–560

    Google Scholar 

  • Rao RV, Kalyankar VD (2013) Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng Appl Artif Intell 26:524–531

    Article  Google Scholar 

  • Safdar S, Li L, Sheikh MA, Liu Z (2007) Finite element simulation of laser tube bending: effects of scanning scheme on bending angle, distortions and stress distribution. Opt Laser Technol 39:1101–1110

    Article  Google Scholar 

  • Shen H, Vollertsen F (2009) Modelling of laser forming—an review. Comput Mater Sci 46(4):834–840

    Article  Google Scholar 

  • Sun J, Chan CC, Tan KM, Dong XY, Shum P (2007) Application of an artificial neural network for simultaneous measurement of bending curvature and temperature with long period fiber grating. Sens Actuators A Phys 137:262–267

    Article  Google Scholar 

  • Teimouri R, Baseri H (2012a) Improvement of dry EDM process characteristics using artificial soft computing methodologies. Prod Eng Res Dev. doi:10.1007/s11740-012-0398-2

  • Teimouri R, Baseri H (2012b) Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel. J Mech Sci Technol 27:533–539

    Article  Google Scholar 

  • Teimouri R, Baseri H (2013) Forward and backward predictions of the friction stir welding parameters using fuzzy-Artificial bee colony—imperialist competitive algorithm system. J Intell Manuf. doi:10.1007/s10845-013-0784-4

  • Teimouri R, Baseri H, Rahmani B, Bakhshi-Jooybari M (2012) Modeling and optimization of spring-back in bending process using multiple regression analysis and neural computation. Int J Mater Form. doi:10.1007/s12289-012-1117-4

  • Venkata Rao R, Kalyankar VD (2013) Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. ScientiaIranica. doi:10.1016/j.scient.2013.01.002

  • Venkata Rao R, Patel V (2013) An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. ScientiaIranica. doi:10.1016/j.scient.2012.12.005

  • Wang X, Weixing X, Chen H, Wang J (2008) Parameter prediction in laser bending of aluminum sheet. Front Mech Eng China 3:293–298

    Google Scholar 

  • Yanjin G, Sheng S, Guoqun Z, Yiguo L (2003) Finite element modeling of pre-loaded sheet metals. J Mater Process Technol 142:400–407

    Article  Google Scholar 

  • Zhang L, Michaleris P (2004) Investigation of Lagrangian and Eulerianfininte element methods for modeling laser forming process. Finite Element Anal Des 40:383–405

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Golpayegan Shohada University of Engineering and Technology, Golpayegan, Iran

    Mahyar Omidvar

  2. Department of Mechanical Engineering, Islamic Azad University, Kordestan Branch of Science and Research, Sanandaj, Iran

    Reza Kashiry Fard

  3. Department of Mechanical Engineering, Islamic Azad University, Dezfull Branch, Dezful, Iran

    Hamed Sohrabpoor

  4. Department of Mechanical Engineering, Babol University of Technology, Babol, Iran

    Reza Teimouri

Authors
  1. Mahyar Omidvar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Kashiry Fard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamed Sohrabpoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Reza Teimouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Teimouri.

Additional information

Communicated by M. J. Watts.

Appendix

Appendix

See Table 6.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Omidvar, M., Fard, R.K., Sohrabpoor, H. et al. Selection of laser bending process parameters for maximal deformation angle through neural network and teaching–learning-based optimization algorithm. Soft Comput 19, 609–620 (2015). https://doi.org/10.1007/s00500-014-1282-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1282-0

Keywords

Search

Navigation