Surface Roughness Prediction Modelling for WEDM of AA6063 Using Support Vector Machine Technique

Article Preview

Abstract:

This research work presents an incorporated approach to modelling of WEDM of AA6063 (armour applications) using support vector machine technique. The experimental investigation has been carried out with four input variables namely pulse-on-time (Pon), pulse-off-time (Poff), servo-voltage (VS) and peak-current (IP). Surface roughness is measured as response parameter. The experimental runs are designed according to 3k full factorial design (k is number of input variables). It is apparent from this study that values anticipated by developed model are found closer to experimental results. Thus, it ensures appropriateness of model for prediction purpose and smart manufacturing. Machined surfaces are also examined by SEM to critically evaluate the process.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

607-612

Citation:

Online since:

August 2019

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2019 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] M.Y. Ali, A. Banu, M. Salehan, E.Y.T. Adesta, M. Hazza, M. Shaffiq, Dimensional accuracy in dry micro wire electrical discharge machining, J. Mech. Engg. Sci. 12 (2018) 3321-3329.

Google Scholar

[2] B. Singh, J.P. Misra, A critical review of wire electric discharge machining, DAAAM International Scientific Book, 2016, pp.249-266.

DOI: 10.2507/daaam.scibook.2016.23

Google Scholar

[3] E.C. Jamson, Electrical Discharge Machining. Michigan: E-Publishing Inc; (2001).

Google Scholar

[4] S. Sarkar, R. Ranjan, A. Das, Optimization of machine process parameters on material removal rate in EDM for AISI P20 tool steel material using RSM. J. Mat. Sci. Mech. Engg, 2 (2015) 117-122.

Google Scholar

[5] V. Singh, R. Bhandari, V.K. Yadav, An experimental investigation on machining parameters of AISI D2 steel using WEDM. Int. J. Adv. Manuf. Tech. 93 (2017) 203-214.

DOI: 10.1007/s00170-016-8681-6

Google Scholar

[6] B. Singh and J. P. Misra, Empirical modeling of average cutting speed during WEDM of gas turbine alloy, ICDME, MATEC Web of Conferences, vol. 221, 01002, (2018).

DOI: 10.1051/matecconf/201822101002

Google Scholar

[7] B. Singh and J. P. Misra, Empirical modeling of average cutting speed during WEDM of hastelloy C22, ICMMM MATEC Web of Conferences, vol. 249, 02003, (2018).

DOI: 10.1051/matecconf/201824902003

Google Scholar

[8] B. Singh and J. P. Misra, Empirical modelling of wear ratio during WEDM of nimonic 263, Mat. Today: Proceed., vol. 5, pp.23612-23618, (2018).

DOI: 10.1016/j.matpr.2018.10.150

Google Scholar

[9] B. Singh and J. P. Misra, Surface finish analysis of wire electric discharge machined specimens by RSM and ANN modeling, Measurement, vol. 137, pp.225-237, (2019).

DOI: 10.1016/j.measurement.2019.01.044

Google Scholar

[10] T. Singh, J.P. Misra, B. Singh, Experimental investigation of influence of process parameters on MRR during WEDM of Al6063 alloy, 5th Int. Conf. Mat. Proc. Charac. Mat. Today: Proceed. Hyderabad, India, 2016, p.2242–2247.

DOI: 10.1016/j.matpr.2017.02.071

Google Scholar

[11] T. Singh, J.P. Misra, V. Upadhyay, P.S. Rao, An adaptive neuro-fuzzy inference system (ANFIS) for Wire-EDM of ballistic grade aluminium alloy, Int. J. Auto. Mech. Engg. 15 (2018) 5295-5307.

DOI: 10.15282/ijame.15.2.2018.11.0408

Google Scholar

[12] V.K. Jain, Advanced Machining Processes, third ed., New Delhi, (2002).

Google Scholar

[13] S.R. Gunn, M. Brown, K.M. Bossly, Network performance assessment for neuro-fuzzy data modeling, Intelligent Data Analysis, 1208 (1997) 313–323.

Google Scholar

[14] S. Cho, S. Asfour, A. Onar, N. Kaundinya, Tool breakage detection using support vector machine learning in a milling process, Int. J. Mach. Tools Manuf. 45 (2005) 241–249.

DOI: 10.1016/j.ijmachtools.2004.08.016

Google Scholar

[15] P. Bhattacharyya, S.K. Sanadhya, Support vector regression based tool wear assessment in face milling, In: Proceed. IEEE int. Conf. Ind. Tech. New York, 2468–2473, (2006).

DOI: 10.1109/icit.2006.372659

Google Scholar