i-manager Publications

Optimization of Process Parameters by ANN and Taguchi Analysis on M2 Tool Steel With Versatile Electrodes during EDM

Dinesh Kumar*, Naveen Beri**

* Dean Academics (Associate Professor), Tawi Engineering College, Pathankot- IKGujral Punjab Technical University, Punjab, India.

** Associate Professor, Department of Mechanical Engineering, Beant College of Engineering Technology, Gurdaspur, Punjab, India.

Periodicity:May - July'2018
DOI : https://doi.org/10.26634/jme.8.3.14738

Abstract

Artificial Neural Network (ANN) analysis is used for the prediction of Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness (SR) in Electrical Discharge Machining(EDM) process of M2 tool steel, followed by a Matlab program.Taguchi's L₃₆ mixed orthogonal array was used to optimize the input parameters, such as polarity, electrode type, and concentration of abrasive powder, discharge current, voltage and duty cycle. Taguchi technique individually is not effective for multiple performance characteristics, so ANN is applied for this experimental study for converting process parameters into single performance characteristics which allows for a potential economical experimentation. The outcome analysis shows that the suggested ANN models can predict the acceptable optimum machining parameters for Electrical Discharge Machining, and has been verified experimentally as well as graphically by adopting Back-Propagation Neural Networks (BPNN) model.

Keywords

Artificial Neural Network (ANN), Material Removal Rate (MRR), Tool Wear Rate (TWR), Surface Roughness (SR), Taguchi, Electrical Discharge Machining (EDM), Matlab.

How to Cite this Article?

Kumar,D., and Beri, N. (2018). Optimization of Process Parameters by ANN and Taguchi Analysis on M2 Tool Steel With Versatile Electrodes during EDM. i-manager’s Journal on Mechanical Engineering, 8(3), 53-58. https://doi.org/10.26634/jme.8.3.14738

References

[1]. Azadeh, A., Mianaei, H. S., Asadzadeh, S. M., Saberi, M., & Sheikhalishahi, M. (2015). A flexible ANN-GAmultivariate algorithm for assessment and optimization of machinery productivity in complex production units. Journal of Manufacturing Systems, 35, 46-75. http://doi.org/10.1016/j.jmsy.2014.11.007

[2]. Chen, Y., Sun, R., Gao, Y., & Leopold, J. (2017). A nested-ANN prediction model for surface roughness considering the effects of cutting forces and tool vibrations. Measurement: Journal of the International Measurement Confederation, 98, 25-34. http://doi.org/10.1016/ j.measurement.2016.11.027

[3]. Das, B., Roy, S., Rai, R. N., & Saha, S. C. (2016). Study on machinability of in-situ Al 4.5%Cu, TiC metal matrix composite-surface finish, cutting force prediction using ANN. CIRP Journal of Manufacturing Science and Technology, 12, 67-78. http://doi.org/10.1016 /j.cirpj.2015.10.002

[4]. Devarasiddappa, D., George, J., Chandrasekaran, M., & Teyi, N. (2016). Application of Artificial Intelligence Approach in Modeling Surface Quality of Aerospace Alloys in WEDM Process. Procedia Technology, (RAEREST), 25, 1199-1208. http://doi.org/10.1016/ j.protcy.2016.08.239

[5]. Nayak, B. B., & Mahapatra, S. S. (2016). Optimization of WEDM process parameters using deep cryo-treated Inconel 718 as work material. Engineering Science and Technology, an International Journal, 19(1), 161-170. http://doi.org/10.1016/j.jestch.2015.06.009

[6]. Niu, X., Yang, C., Wang, H., & Wang, Y. (2017). Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine. Applied Thermal Engineering, 111, 1353-1364. http://doi.org/10.1016/j.applthermaleng. 2016.10.042

[7]. Payal, H., Maheshwari, S., & Bharti, P. S. (2017). Process Modeling of Electric Discharge Machining of Inconel 825 using Artificial Neural Network, Int. J. Mech. Aerosp. Ind. Mechatron. Manufac. Eng.,11(3), 562-566.

[8]. Prabhu, S., & Vinayagam, B. K. (2016). Evaluation of Surface Roughness of Carbon Nanotube TMT Nanosteel Material using Taguchi Analysis and Neural Networks, 39(3), 718-729.

[9]. Sangwan, K. S., Saxena, S., & Kant, G. (2015). Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP, 29, 305-310. http://doi.org/10.1016/ j.procir.2015.02.002

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Optimization of Process Parameters by ANN and Taguchi Analysis on M2 Tool Steel With Versatile Electrodes during EDM

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: