Comparative Study of LS-SVM, RVM and ELM for Modelling of Electro-Discharge Coating Process

Document Type : Original Article


1 Department of Mechanical Engineering, Birjand University of Technology, Birjand, Iran

2 Department of Computer Engineering, Birjand University of Technology, Birjand, Iran


The Electro-discharge coating process is an efficient method for improvement of the surface quality of the parts used in molds. In this process, Material Transfer Rate (MTR), an average Layer Thickness (LT) are important factors, and tuning the input process parameters to obtain the desired value of them is a crucial issue. Due to the wide range of the input parameters and nonlinearity of this system, the establishment of a mathematical model is a complicated mathematical problem. Although many efforts have been made to model this process, research is still ongoing to improve the modeling of this process. To this end, in the present study, three powerful machine learning algorithms, namely, Relevance Vector Machine (RVM), Extreme Learning Machine (ELM) and the Least Squares Support Vector Machine (LS-SVM) that have not been used to model this process, have been used. The values R2 above 0.99 for the training data and above 0.97 for the test data show the high accuracy and generalization capability degree related to the LS-SVM models, which can be applied for the input parameters tuning in order to attain a preferred value of the outputs.


[1]      Jameson, E. C., Electrical Discharge Machining: Society of Manufacturing Engineers, 2001.
[2]     Ho, K., Newman, S., State of the Art Electrical Discharge Machining (EDM), International Journal of Machine Tools and Manufacture, Vol. 43, No. 13, 2003, pp. 1287-1300.
[3]     Kumar, S., Singh, R., Singh, T., and Sethi, B., Surface Modification by Electrical Discharge Machining: A Review, Journal of Materials Processing Technology, Vol. 209, No. 8, 2009, pp. 3675-3687.
[4]     Janmanee, P., Muttamara, A., Surface Modification Of Tungsten Carbide by Electrical Discharge Coating (EDC) Using a Titanium Powder Suspension, Applied Surface Science, Vol. 258, No. 19, 2012, pp. 7255-7265.
[5]     Beri, N., Maheshwari, S., Sharma, C., and Kumar, A., Technological Advancement in Electrical Discharge Machining with Powder Metallurgy Processed Electrodes: A Review, Materials and Manufacturing Processes, Vol. 25, No. 10, 2010, pp. 1186-1197.
[6]     Das, A.,Misra, J. P., Experimental investigation On Surface Modification of Aluminum by Electric Discharge Coating Process Using TiC/Cu Green Compact Tool-Electrode, Machining Science and Technology, Vol. 16, No. 4, 2012, pp. 601-623.
[7]     Naik, S. K., Study the Effect of Compact Pressure of P/M Tool Electrode on Electro Discharge Coating (EDC) Process, 2013.
[8]     Hwang, Y. L., Kuo, C. L., and Hwang, S. F., The Coating of Tic Layer On the Surface of Nickel by Electric Discharge Coating (EDC) with a Multi-Layer Electrode, Journal of Materials Processing Technology, Vol. 210, No. 4, 2010, pp. 642-652.
[9]     Patowari, P. K., Saha, P., and Mishra, P., Artificial Neural Network Model in Surface Modification by EDM Using Tungsten–Copper Powder Metallurgy Sintered Electrodes, The International Journal of Advanced Manufacturing Technology, Vol. 51, No. 5, 2010, pp. 627-638.
[10]  Provost, F., Kohavi, R., Glossary of Terms, Journal of Machine Learning, Vol. 30, No. 2-3, 1998, pp. 271-274.
[11]  Hastie, T., Tibshirani, R., and Friedman, J., Overview of supervised Learning, The Elements of Statistical Learning, 2009, pp. 9-41.
[12]  Tyagi, R.,Kumar, S., Kumar, V., Mohanty, S., Das, A.,and Mandal, A., Analysis and Prediction of Electrical Discharge Coating Using Artificial Neural Network (ANN), Advances in Simulation, Product Design and Development, 2020, pp. 177-189.
[13]  Sahu, A. K., Mahapatra, S. S., and Chatterjee, S., Optimization of Electro-Discharge Coating Process Using Harmony Search, Materials Today: Proceedings, Vol. 5, No. 5, 2018, pp. 12673-12680.
[14]  Vapnik, V., The Nature of Statistical Learning Theory: Springer Science & Business Media, 2013.
[15]  Bishop, C. M., Pattern Recognition And Machine Learning: Springer, 2006.
[16]  Meyer, D., Leisch, F., and Hornik, K., The Support Vector Machine Under Test, Neurocomputing, Vol. 55, No. 1, 2003, pp. 169-186.
[17]  Cristianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods: Cambridge University Press, 2000.
[18]  Parrella, F., Online Support Vector Regression, Master's Thesis, Department of Information Science, University of Genoa, Italy, 2007.
[19]  Kecman, V., Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models: MIT Press, 2001.
[20]  Suykens, J. A., Van Gestel, T., and De Brabanter, J., Least Squares Support Vector Machines: World Scientific, 2002.
[21]  Wang, H., Hu, D., Comparison of SVM and LS-SVM for Regression, Vol. 1, 2005, pp. 279-283.
[22]  Ding, S., Zhao, H., Zhang, Y., Xu, X., and Nie, R., Extreme Learning Machine: Algorithm, Theory and Applications, Artificial Intelligence Review, Vol. 44, No. 1, 2015, pp. 103-115.
[23]  Huang, G. B., Zhu, Q. Y., and Siew, C. K., Extreme Learning Machine: Theory and Applications, Neurocomputing, Vol. 70, No. 1-3, pp. 489-501, 2006.
[24]  Sun, Z. L., Choi, T. M., Au, K .F., and Yu, Y., Sales Forecasting Using Extreme Learning Machine with Applications in Fashion Retailing, Decision Support Systems, Vol. 46, No. 1, 2008, pp. 411-419.
[25]  Huang, G. B., Learning Capability and Storage Capacity of Two-Hidden-Layer Feedforward Networks, IEEE Transactions on Neural Networks, Vol. 14, No. 2, 2003, pp. 274-281.
[26]  Liang, N. Y., Huang, G. B., Saratchandran, P., and Sundararajan, N., A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks, IEEE Transactions on Neural Networks, Vol. 17, No. 6, 2006, pp. 1411-1423.
[27]  Wan, C., Xu, Z., Pinson, P., Dong, Z. Y., and Wong, K. P., Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine, IEEE Transactions on Power Systems, Vol. 29, No. 3, 2013, pp. 1033-1044.
[28]  Caesarendra, W., Widodo, A., and Yang, B. S., Application of Relevance Vector Machine and Logistic Regression for Machine Degradation Assessment, Mechanical Systems and Signal Processing, Vol. 24, No. 4, 2010, pp. 1161-1171.
[29]  Widodo, A., Kim, E. Y.,  Son, J. D., Yang, B. S., Tan, A. C., Gu, D. S., Choi, B. K., and Mathew, J., Fault Diagnosis of Low Speed Bearing Based On Relevance Vector Machine And Support Vector Machine, Expert systems with Applications,Vol. 36, No. 3, 2009, pp. 7252-7261.
[30]  Wang, X., Ye, M., and Duanmu, C., Classification of Data From Electronic Nose Using Relevance Vector Machines, Sensors and Actuators B: Chemical, Vol. 140, No. 1, 2009, pp. 143-148.
[31]  Jiang, J., Li, M., Jing, X., and Lv, B., Research on the Performance of Relevance Vector Machine for Regression and Classification, 2015, pp. 758-762.
[32]  MacKay, D. J., Bayesian Interpolation, Neural Computation, Vol. 4, No. 3, 1992, pp. 415-447.
[33]  Thayananthan, A., Relevance Vector Machine Based Mixture of Experts, Cambridge: Department of Engineering, University of Cambridge, 2005.
[34]  Tipping, M. E., Faul, A. C., Fast Marginal Likelihood Maximisation For Sparse Bayesian Models, In C. M. Bishopand B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on ArtificialIntelligence and Statistics, Key West, FL, Jan 3–6, 2003.
[35]  Tipping, M. E., SPARSEBAYES V1. 1: A Baseline Matlab Implementation of Sparse Bayesian Model Estimation, 2009.
[36]  Pelckmans, K., Suykens, J. A., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De, B., Moor, and Vandewalle, J., LS-SVMlab: a Matlab/C Toolbox for Least Squares Support Vector Machines, Tutorial. KULeuven-ESAT. Leuven, Belgium, Vol. 142, 2002, pp. 1-2.