Estimation of laser weld parameters using surrogate modelling technique

[1] E. Pastuchová and M. Zákopčan, “Comparison of Algorithms for Fitting a Gaussian Function used in Testing Smart Sensors”, Journal of Electrical Engineering, vol. 66, no. 3, pp. 178-181, 2015.10.2478/jee-2015-0029Search in Google Scholar

[2] A. Diaz-Manriquez, G. Toscano-Pulido, and W. Gomez-Flores, “On the Selection of Surrogate Models in Evolutionary Optimization Algorithms”, In 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 2155-2162, June 2011.10.1109/CEC.2011.5949881Search in Google Scholar

[3] G. Montemayor-Garca and G. Toscano-Pulido, “A Study of Surrogate Models for their Use inMultiobjective Evolutionary Algorithms”, In 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, pp. 1-6, Oct 2011.10.1109/ICEEE.2011.6106655Search in Google Scholar

[4] M. B. Yelten, T. Zhu, S. Koziel, P. D. Franzon, and M. B. Steer, “Demystifying Surrogate Modelling for Circuits and Systems”, IEEE Circuits and Systems Magazine, vol. 12, no. 1, pp. :45-63, Firstquarter 2012.10.1109/MCAS.2011.2181095Search in Google Scholar

[5] C. K. I. Williams and C. E. Rasmussen, “Gaussian Processes for Regression”, In Advances in neural information processing systems, pp. 514-520, 1996.Search in Google Scholar

[6] C. M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics)”, Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.Search in Google Scholar

[7] C. E. Rasmussen and C. K. I. Williams, “Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)”, The MIT Press, 2005.10.7551/mitpress/3206.001.0001Search in Google Scholar

[8] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay, “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, vol. 12 pp. 2825-2830, 2011.Search in Google Scholar

[9] M. H. S. Mendes, G. L. Soares, J. L. Coulomb, and J. A. Vasconcelos, “Appraisal of Surrogate Modelling Techniques: A Case Study of Electromagnetic Device”, IEEE Transactions on Magnetics, vol. 49, no. 5, pp. 1993-1996, May 2013.10.1109/TMAG.2013.2241401Search in Google Scholar

[10] A. Bollig, D. Abel, C. Kratzsch, and S. Kaierle, “Identification and Predictive Control of Laser Beam Welding using Neural Networks”, In 2003 European Control Conference (ECC), pp. 2457-2462, Sep 2003.10.23919/ECC.2003.7085334Search in Google Scholar

[11] T. Schade, R. M. Ramsayer, and J. P. Bergmann, “Laser Welding of Electrical Steel Stacks Investigation of the Weldability” In 2014 4th International Electric Drives Production Conference (EDPC), pp. 16, Sep 2014.10.1109/EDPC.2014.6984386Search in Google Scholar

[12] S. Williams and W. Suder, “Use of Fundamental Laser Material Interaction Parameters in Laser Welding”, In CLEO: 2011-Laser Science to Photonic Applications, pp. 1-2, May 2011.10.1364/CLEO_AT.2011.AMA1Search in Google Scholar

[13] V. Kotlan, R. Hamar, D. Panek and I. Doležel, “Combined Heat Treatment of Metal Materials”, COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 35 no. 4, pp. 1450-1459, 2016.10.1108/COMPEL-08-2015-0302Search in Google Scholar