Application of the Relevance Vector Machine for Modeling Surface Roughness in WEDM Process for Ti-6Al-4V Titanium Alloy

Subject Areas : advanced manufacturing technology

Abolfazl  Foorginejad ¹ (Department of Mechanical Engineering, Birjand University of Technology, Iran)
Nader  Mollayi ² (Department of Computer engineering and Information Technology, Birjand University of Technology, Iran)
Morteza  Taheri ³ (Department of Mechanical Engineering, University of Birjand, Iran)

Received: 2018-07-03 Accepted : 2018-11-29 Published : 2018-12-01

Keywords: WEDM, Relevance Vector Machine, Modeling, Ti-6Al-4V Alloy,

Abstract :

Cutting the Titanium alloys is a complicated task which cannot be performed by traditional methods and modern machining processes, such as Wire electro-discharge machining (WEDM) process which are mainly used for this purpose. As a result of the high price of the Ti-6Al-4V alloy, proper tuning of the input parameters so as to attain a desired value of the surface roughness is an important issue in this process. For this purpose, it is necessary to develop a predictive model of surface roughness based on the input process parameters. In this paper, The Taguchi method was used for the design of the experiment. According to their effectiveness, the input parameters are pulse-on time, pulse-off time, wire speed, current intensity, and voltage; and the output parameter is surface roughness. However, a predictive model cannot be defined by a simple mathematical expression as a result of the complicated and coupled multivariable effect of the process parameters on the surface roughness in this process. In this study, application of the relevance vector machine as a powerful machine learning algorithm for modeling and prediction of surface roughness in wire electro-discharge machining for Ti-6Al-4V titanium alloy has been investigated. The predicting result of model based on the root means square error (RMSE) and the coefficient of determination (R2) statistical indices, prove that this approach provides reasonable accuracy in this application.

References:

[1]     Niknam, S. A., Khettabi, R., and Songmene, V., Machinability and Machining of Titanium Alloys: a Review, in: Machining of Titanium Alloys, Eds., Springer, Berlin, Heidelberg, 2014, pp. 1-30, 978-3-662-43902-9.

[2]     Varun, A., Venkaiah, N., Simultaneous Optimization of WEDM Responses Using Grey Relational Analysis Coupled with Genetic Algorithm while Machining EN 353, The International Journal of Advanced Manufacturing Technology, Vol. 76, No. 1-4, 2015, pp. 675-690, 10.1007/s00170-014-6198-4.

[3]     Azadi Moghaddam, M., Kolahan, F., An Empirical Study on Statistical Analysis and Optimization of EDM Process Parameters for Inconel 718 Super Alloy Using D-Optimal Approach and Genetic Algorithm, Journal of Computational Applied Mechanics, Vol. 46, No. 2, 2015, pp. 267-277, 10.22059/JCAMECH.2015.56214.

[4]     Tosun, N., Cogun, C., and Tosun, G., Study on Kerf and Material Removal Rate in Wire Electrical Discharge Machining Based on Taguchi Method, Journal of Materials Processing Technology, Vol. 152, No. 3, 2004, pp. 316-322, 10.1016/j.jmatprotec.2004.04.373.

[5]     Muthu Kumar, V., Suresh Babu, A., Venkatasamy, R., and Raajenthiren, M., Optimization of the WEDM Parameters on Machining Incoloy800 Super Alloy with Multiple Quality Characteristics, Int J Eng Sci Technol, Vol. 2, No. 6, 2010, pp. 1538–1547.

[6]     Arikatla, S. P., Mannan, K. T., and Krishnaiah, A., Investigations on Surface Characterisation of Wire Electric Discharge Machined Surface of Titanium Alloy, International Journal of Engineering Research and Technology, Vol. 6, No. 4, 2013, pp. 663-570.

[7]     Jangra, K., Grover, S., and Aggarwal, A., Optimization of Multi Machining Characteristics in WEDM of WC-5.3%Co Composite Using Integrated Approach of Taguchi, GRA and Entropy Method. Front Mech Eng, Vol. 7, No. 3, 2012, pp. 288–299, 10.1007/s11465-012-0333-4.

[8]     Foorginejad, A., Amirabadi, H., and Khalili, Kh., Electrical Discharge Machining Process Modeling and Optimization of Neural Network Algorithm Using. Journal of Modeling in Engineering, Vol. 12, No. 37, 2014, pp. 1-14, 10.22075/JME.2017.1668.

[9]     Shabgard, M. R., Badamchizadeh, M. A., Ranjbary, G., and Amini, K., Fuzzy Approach to Select Machining Parameters in Electrical Discharge Machining (EDM) and Ultrasonic-Assisted EDM Processes, Journal of Manufacturing Systems, Vol. 32, No. 1, 2013, pp. 32-39, 10.1016/j.jmsy.2012.09.002.

[10]  Ugrasen, G., Ravindra, H. V., Naveen Prakash, G. V., and Keshavamurthy, R., Comparison of Machining Performances Using Multiple Regression Analysis and Group Method Data Handling Technique in Wire EDM of Stavax Material, Procedia Materials Science, Vol. 5, No. 1, 2014, pp. 2215 – 2223, 10.1016/j.mspro.2014.07.430.

[11]  Aldas, K., Ozkul, I., and Akkurt, A., Modelling SURFACE Roughness in WEDM Process Using ANFIS Method, Journal of the Balkan Tribological Association, Vol. 20, No. 4, 2014, pp. 548-558.

[12]  De, D., Nandi, T., and Bandyopadhyay, A., Analysis of Machining Parameters for Wire Cut Electrical Discharge Machining of Pure Titanium Using Response Surface Methodology, Materials Today: Proceedings, Vol. 5, No. 2, Part 1, 2018, pp. 5374-5383, 10.1016/j.matpr.2017.12.123.

[13]  Goswami, A., Kumar, J., Optimization in Wire-Cut EDM of Nimonic-80A Using Taguchi's Approach and Utility Concept, Engineering Science and Technology, International Journal, Vol. 17, No. 4, 2014, pp. 236-246, 10.1016/j.jestch.2014.07.001.

[14]  Hamedi, M., Farzaneh, A., Optimization of Dimensional Deviations in Wax Patterns for Investment Casting, Journal of Computational Applied Mechanics, Vol. 45, No. 1, 2014, pp. 23-28, 10.22059/JCAMECH.2014.52312.

[15]  Goharimanesh, M., Akbari, A., Optimum Parameters of Nonlinear Integrator Using Design of Experiments Based on Taguchi Method, Journal of Computational Applied Mechanics, Vol. 46, No. 2, 2015, pp. 233-241, 10.22059/JCAMECH.2015.55532.

[16]  Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., and Vlahavas, I., Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs, Machine Learning, Vol. 104, No. 1, 2016, pp. 55-98, 10.1007/s10994-016-5546-z.

[17]  Efron, B., Hastie, T., Computer Age Statistical Inference, Cambridge University Press, 2016, pp. 20-30, 978-1107149892.

[18]  Xu, Y., Zhang, M., Zhu, Q., and He, Y., An Improved Multi-Kernel RVM Integrated with CEEMD for High-Quality Intervals Prediction Construction and its Intelligent Modeling Application, Chemometrics and Intelligent Laboratory Systems, Vol. 171, 2017, pp. 151-160, 10.1016/j.chemolab.2017.10.019.

[19]  Feng, Z., Wang, R., Self-Validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine, International Journal of Control and Automation, Vol. 7, No. 11, 2014, pp. 193-212, 10.14257/ijca.2014.7.11.19.