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Optimization of laser brazing onto galvanized steel based on ensemble of metamodels

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Abstract

Laser brazing (LB) provides a promising way to join the galvanized steel in automotive industry for its significant advantages including high speed, small heat-affected zone, and high welding seam quality. The process parameters of LB have significant effects on the bead profile and hence the quality of joint. Since the relationships between the process parameters and bead profile cannot be expressed explicitly, it is impractical to determine the optimal process parameters intuitively. This paper proposes an optimization methodology by combining genetic algorithm (GA) and ensemble of metamodels (EMs) to address the process parameters optimization of the bead profile in LB with crimping butt. Firstly, Taguchi experimental design is adopted to generate the experimental points. Secondly, the relationships between process parameters (i.e., welding speed, wire feed rate, gap) and the bead geometries are fitted using EMs based on the experimental data. The comparative results show that the EMs can take advantage of the prediction ability of each stand-alone metamodel and thus decrease the risk of adopting inappropriate metamodels. Then, the GA is used to facilitate design space exploration and global optimum search. Besides, the main effects and contribution rates of multiple process parameters on bead profile are analyzed. Eventually, the verification experiments are carried out to demonstrate the effectiveness and reliability of the obtained optimal parameters. Overall, the proposed hybrid approach, GA–EMs, exhibits great capability of guiding the actual LB processing and improving welding quality.

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References

  • Acar, E. (2015). Effect of error metrics on optimum weight factor selection for ensemble of metamodels. Expert Systems with Applications, 42(5), 2703–2709.

    Article  Google Scholar 

  • Acar, E., & Rais-Rohani, M. (2009). Ensemble of metamodels with optimized weight factors. Structural and Multidisciplinary Optimization, 37(3), 279–294.

    Article  Google Scholar 

  • Aute, V., Saleh, K., Abdelaziz, O., Azarm, S., & Radermacher, R. (2013). Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations. Structural and Multidisciplinary Optimization, 48(3), 581–605.

    Article  Google Scholar 

  • Cao, R., Yu, G., Chen, J. H., & Wang, P. (2013). Cold metal transfer joining aluminum alloys-to-galvanized mild steel. Journal of Materials Processing Technology, 213(10), 1753–1763.

    Article  Google Scholar 

  • Chaki, S., Bathe, R. N., Ghosal, S., & Padmanabham, G. (2015). Multi-objective optimisation of pulsed Nd: YAG laser cutting process using integrated ANN–NSGAII model. Journal of Intelligent Manufacturing, 1–16. doi:10.1007/s10845-015-1100-2.

  • Chen, W., Ackerson, P., & Molian, P. (2009). \({\rm CO}_{2}\) laser welding of galvanized steel sheets using vent holes. Materials and Design, 30(2), 245–251.

    Google Scholar 

  • Chen, S., Li, L., Chen, Y., Dai, J., & Huang, J. (2011). Improving interfacial reaction nonhomogeneity during laser welding–brazing aluminum to titanium. Materials and Design, 32(8), 4408–4416.

    Article  Google Scholar 

  • Clarke, S. M., Griebsch, J. H., & Simpson, T. W. (2005). Analysis of support vector regression for approximation of complex engineering analyses. Journal of Mechanical Design, 127(6), 1077–1087.

    Article  Google Scholar 

  • Coello Coello, C. A. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113–127.

    Article  Google Scholar 

  • Colombo, D., & Previtali, B. (2014). Laser dimpling and remote welding of zinc-coated steels for automotive applications. The International Journal of Advanced Manufacturing Technology, 72(5–8), 653–663.

    Article  Google Scholar 

  • Gao, X., Zhong, X., You, D., & Katayama, S. (2013). Kalman filtering compensated by radial basis function neural network for seam tracking of laser welding. IEEE Transactions on Control Systems Technology, 21(5), 1916–1923.

    Article  Google Scholar 

  • Islam, M., Buijk, A., Rais-Rohani, M., & Motoyama, K. (2015). Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique. Advances in Engineering Software, 79, 127–136.

    Article  Google Scholar 

  • Katherasan, D., Elias, J. V., Sathiya, P., & Haq, A. N. (2014). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. Journal of Intelligent Manufacturing, 25(1), 67–76.

    Article  Google Scholar 

  • Li, X., Lawson, S., Zhou, Y., & Goodwin, F. (2007). Novel technique for laser lap welding of zinc coated sheet steels. Journal of Laser Applications, 19(4), 259.

    Article  Google Scholar 

  • Lim, N. K. (2014). Optimization of TIG weld geometry using a Kriging surrogate model and Latin Hypercube sampling for data generation. Long Beach: California State University.

    Google Scholar 

  • Lin, J., Ma, N., Lei, Y., & Murakawa, H. (2013). Shear strength of CMT brazed lap joints between aluminum and zinc-coated steel. Journal of Materials Processing Technology, 213(8), 1303–1310.

    Article  Google Scholar 

  • Mei, L., Chen, G., Jin, X., Zhang, Y., & Wu, Q. (2009). Research on laser welding of high-strength galvanized automobile steel sheets. Optics and Lasers in Engineering, 47(11), 1117–1124.

    Article  Google Scholar 

  • Nasiri, A. M., Chartrand, P., Weckman, D. C., & Zhou, N. Y. (2013). Thermochemical analysis of phases formed at the interface of a Mg alloy-Ni-plated steel joint during laser brazing. Metallurgical and Materials Transactions A, 44(4), 1937–1946.

    Article  Google Scholar 

  • Qin, G., Su, Y., Meng, X., & Fu, B. (2015). Numerical simulation on MIG arc brazing-fusion welding of aluminum alloy to galvanized steel plate. The International Journal of Advanced Manufacturing Technology, 78(9), 1917–1925.

  • Rong, Y., Zhang, Z., Zhang, G., Yue, C., Gu, Y., Huang, Y., et al. (2015). Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA. Optics and Lasers in Engineering, 67, 94–104.

    Article  Google Scholar 

  • Roos, C., & Schmidt, M. (2014). Remote laser welding of zinc coated steel sheets in an edge lap configuration with zero gap. Physics Procedia, 56, 535–544.

    Article  Google Scholar 

  • Ruggiero, A., Tricarico, L., Olabi, A. G., & Benyounis, K. Y. (2011). Weld-bead profile and costs optimisation of the \({\rm CO}_{2}\) dissimilar laser welding process of low carbon steel and austenitic steel AISI316. Optics and Laser Technology, 43(1), 82–90.

    Google Scholar 

  • Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4(4), 409–435.

  • Singh, A., Cooper, D. E., Blundell, N. J., Pratihar, D. K., & Gibbons, G. J. (2014). Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms. International Journal of Computer Integrated Manufacturing, 27(7), 656–674.

    Article  Google Scholar 

  • Sinha, A. K., Kim, D. Y., & Ceglarek, D. (2013). Correlation analysis of the variation of weld seam and tensile strength in laser welding of galvanized steel. Optics and Lasers in Engineering, 51(10), 1143–1152.

    Article  Google Scholar 

  • Taguchi, G. (1978). Performance analysis design. International Journal of Production Research, 16, 521–530.

    Article  Google Scholar 

  • Tamrin, K. F., Nukman, Y., Sheikh, N. A., & Harizam, M. Z. (2014). Determination of optimum parameters using grey relational analysis for multi-performance characteristics in \({\rm CO}_{2}\) laser joining of dissimilar materials. Optics and Lasers in Engineering, 57, 40–47.

    Google Scholar 

  • Tan, Z., & Hou, D. (2009). Improve accuracy of laser beam width measurement using a genetic algorithm. Optics and Lasers in Engineering, 47(11), 1091–1096.

    Article  Google Scholar 

  • Tan, C. W., Li, L. Q., Chen, Y. B., Mei, C. X., & Guo, W. (2013). Interfacial microstructure and fracture behavior of laser welded-brazed Mg alloys to Zn-coated steel. The International Journal of Advanced Manufacturing Technology, 68(5–8), 1179–1188.

    Article  Google Scholar 

  • Wang, X., Chen, H., Liu, H., Li, P., Yan, Z., Huang, C., et al. (2013). Simulation and optimization of continuous laser transmission welding between PET and titanium through FEM, RSM, GA and experiments. Optics and Lasers in Engineering, 51(11), 1245–1254.

    Article  Google Scholar 

  • Wang, D., DiazDelaO, F. A., Wang, W., & Mottershead, J. E. (2015). Full-field digital image correlation with Kriging regression. Optics and Lasers in Engineering, 67, 105–115.

    Article  Google Scholar 

  • Wang, G. G., & Shan, S. (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 129(4), 370–380.

    Article  Google Scholar 

  • Zadeh, P. M., Toropov, V. V., & Wood, A. S. (2009). Metamodel-based collaborative optimization framework. Structural and Multidisciplinary Optimization, 38(2), 103–115.

    Article  Google Scholar 

  • Zhao, Y., Zhang, Y., Hu, W., & Lai, X. (2012). Optimization of laser welding thin-gage galvanized steel via response surface methodology. Optics and Lasers in Engineering, 50(9), 1267–1273.

    Article  Google Scholar 

  • Zhou, X. J., Ma, Y. Z., & Li, X. F. (2011). Ensemble of surrogates with recursive arithmetic average. Structural and Multidisciplinary Optimization, 44(5), 651–671.

    Article  Google Scholar 

  • Zhou, Q., Shao, X., Jiang, P., Cao, L., Zhou, H., & Shu, L. (2015a). Differing mapping using ensemble of metamodels for global variable-fidelity metamodeling. CMES: Computer Modeling in Engineering and Sciences, 106(5), 323–355.

    Google Scholar 

  • Zhou, Q., Shao, X., Jiang, P., Zhou, H., & Shu, L. (2015b). An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function. Simulation Modelling Practice and Theory, 59, 18–35.

    Article  Google Scholar 

Download references

Acknowledgments

This research has been supported by the National Basic Research Program (973 Program) of China under Grant No. 2014CB046703, the National Natural Science Foundation of China (NSFC) under Grant Nos. 51505163, 51421062 and 51323009, and the Fundamental Research Funds for the Central Universities, HUST: Grant No. 2014TS040. The authors also would like to thank the anonymous referees for their valuable comments.

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Correspondence to Ping Jiang.

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Zhou, Q., Rong, Y., Shao, X. et al. Optimization of laser brazing onto galvanized steel based on ensemble of metamodels. J Intell Manuf 29, 1417–1431 (2018). https://doi.org/10.1007/s10845-015-1187-5

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  • DOI: https://doi.org/10.1007/s10845-015-1187-5

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