Advertisement

An improved artificial neural network for laser welding parameter selection and prediction

  • Zhong Yuguang
  • Xue Kai
  • Shi Dongyan
ORIGINAL ARTICLE

Abstract

In the laser welding production, the selection and prediction of welding parameters is essentially important to guarantee weld quality. Artificial neural networks (ANN), which perform a nonlinear mapping between inputs and outputs, are an alternative approach for developing welding parameter forecasting model. In this paper, in order to speed up the convergence and avoid local minimum of the conditional ANN, genetic algorithm simulated annealing (GASA) based on the random global optimization is inducted into the network training. By means of GASA method, weights and threshold of neural networks can be globally optimized with short training time. Meanwhile, the gray correlation model (GCM) is used as a pre-processing tool to simplify the original networks based on obtaining the main influence elements of network inputs. The GCM–GASA–ANN method combines the complementary features of three computational intelligence techniques and owns very good applicability. Through the simulation and analysis of an orthogonal experiment, the proposed method can be proved to have higher accuracy and to perform better than the traditional ANN to forecast the laser welding parameters.

Keywords

Gray relational analysis Artificial neural networks Laser welding Genetic algorithm simulated annealing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Roca AS, Fals HC, Fernández JB, Macías EJ, De La Parte MP (2009) Artificial neural networks and acoustic emission applied to stability analysis in gas metal arc welding. Sci Technol Weld Join 14(2):117–124CrossRefGoogle Scholar
  2. 2.
    Liukkonen M, Hiltunen T, Havia E, Leinonen H, Hiltunen Y (2009) Modeling of soldering quality by using artificial neural networks. IEEE Transa Electron Packag Manuf 32(2):89–96CrossRefGoogle Scholar
  3. 3.
    Sathiya P, Panneerselvam K, Abdul Jaleel MY (2012) Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm. Mater Des 36:490–498CrossRefGoogle Scholar
  4. 4.
    Campbell SW, Galloway AM, McPherson NA (2012) Artificial neural network prediction of weld geometry performed using GMAW with alternating shielding gases. Weld J 91(6):174–181Google Scholar
  5. 5.
    Chokkalingham S, Vasudevan M, Sudarsan S, Chandrasekhar N (2012) Predicting weld head width and depth of penetration form infrared thermal image of weld pool using artificial neural network. Insight Non Destr Test Cond Monit 54(5):272–277CrossRefGoogle Scholar
  6. 6.
    Ghosal S, Chaki S (2010) Estimation and optimization of depth of penetration in hybrid CO2 LASER-MIG welding using ANN-optimization hybrid model. Int J Adv Manuf Technol 47(9–12):1149–1157CrossRefGoogle Scholar
  7. 7.
    Balasubramanian KR, Sankaranarayanasamy K, Buvanashekaran G (2006) Analysis of laser welding parameters using artificial neural network. Int J Join Mater 18(3–4):99–104Google Scholar
  8. 8.
    Tsai MJ, Li CH, Chen CC (2008) Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. J Mater Process Technol 208(1–3):270–283CrossRefGoogle Scholar
  9. 9.
    Liu K, Guo WY, Shen XL, Tan ZF (2012) Research on the forecast model of electricity power industry loan based on GA-BP neural network. Engery Procedia 14:1918–1924Google Scholar
  10. 10.
    Wang HB, Liu M (2012) Design of robotic visual servo control based on neural network and genetic algorithm. Int J Autom Comput 9(1):24–29CrossRefGoogle Scholar
  11. 11.
    Funahashi KI (1989) On the approximate realization of continuous mapping by neural network. Neural Netw 2(3):183–192CrossRefGoogle Scholar
  12. 12.
    Norouzi A, Hamedi M, Adineh VR (2012) Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods. Int J Adv Manuf Technol 61(1–4):135–147CrossRefGoogle Scholar
  13. 13.
    Zhu YC, Zeng WD, Sun Y, Feng F, Zhou YG (2011) Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy. Comput Mater Sci 50(5):1785–1790CrossRefGoogle Scholar
  14. 14.
    Lin HL, Chou T, Chou CP (2007) Optimization of resistance spot welding process using Taguchi method and a neural network. Exp Tech 31(5):30–36CrossRefGoogle Scholar
  15. 15.
    Lin HL (2012) The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process. J Intell Manuf 23(5):1671–1680CrossRefGoogle Scholar
  16. 16.
    Jia ZY, Ma JW, Wang FJ, Liu W (2009) Characteristics forecasting method of assembled product based on multiple part geometric elements. Jixie Gongcheng Xuebao 45(7):168–173 (in Chinese)CrossRefGoogle Scholar
  17. 17.
    Jia ZY, Ma JW, Wang FJ, Liu W (2010) Characteristics forecasting of hydraulic valve based on grey correlation and ANFIS. Expert Sys Appl 37(2):1250–1255CrossRefGoogle Scholar
  18. 18.
    Zhang ZY, Jiang ZB, Yu CQ (2006) Automated flame rectification process planning system in shipbuilding based on artificial intelligence. Int J Adv Manuf Technol 30(11–12):1119–1125CrossRefGoogle Scholar
  19. 19.
    Antonio CAC, Davim JP, Lapa V (2008) Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials. Int J Adv Manuf Technol 39(11–12):1101–1110CrossRefGoogle Scholar
  20. 20.
    Deng W, Li W, Yang XH (2011) A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction. Expert Sys Appl 38(4):4198–4205CrossRefGoogle Scholar
  21. 21.
    Tansel IN, Demetgul M, Okuyucu H, Yapici A (2010) Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network. Int J Adv Manuf Technol 48(1–4):95–101CrossRefGoogle Scholar
  22. 22.
    Shen CY, Wang LX, Li Q (2007) Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J Mater Process Technol 183(2–3):412–418CrossRefGoogle Scholar
  23. 23.
    Panneerselvam K, Aravindan S, Noorul HA (2009) Hybrid of ANN with genetic algorithm for optimization of frictional vibration joining process of plastics. Int J Adv Manuf Technol 42(7–8):669–677CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.College of Mechanical and Electrical EngineeringHarbin Engineering UniversityHarbinChina

Personalised recommendations