Predicting the process performance of electrical discharged machined D3 steel using artificial neural network

R. Rajeswari; M. Shanmugapriya; R. Sundareswaran; S. Vijayan

doi:10.5269/bspm.77016

R. Rajeswari
M. Shanmugapriya
R. Sundareswaran SSN College of Engineering
S. Vijayan

Abstract

This paper investigates the measures of Electrical Discharge Machining (EDM) process of D3 steel in which the material removal rate (MRR) and surface roughness (Ra) are given the prime importance. An Artificial Neural Network (ANN) model with feed forward network is proposed for predicting the MRR and Ra as outputs depending on the process parameters such as voltage (Vs), current (Cs), Pulse on time (Ton) and Pulse frequency (fp). The multi-layer neural network model has been developed with 125 experimental data sets, 4 different process parameters were defined as input parameters and MRR and Ra output values were obtained. The accuracy of the model was assessed using five known statistical metrics like mean square error (MSE), mean absolute error (MAE), sum of squares error (SSE), the coefficient of determination (R2), and correlation coefficient (R). For the proposed ANN model, was found with the performance of R values of 0.992, 0.944, 0.949 for training, validation and testing data sets in case of Ra. With respect to MRR the correlation coefficient R values of 0.997, 0.995 and 0.981 for training, validation and testing data sets are obtained. The results indicate that the proposed artificial neural network model can accurately predict both MRR and Ra, depending on the process parameters.

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References

A Pugazhenthi, R. Thiyagarajan, P.K. Srividhya, R Udhayasankar, and Suresh R, Artificial Neural Network and Process Optimization of Electrical Discharge Machining of Al 6463, Mar. 2023, doi: https://doi.org/10.1109/icears56392.2023.10085204.

Itagi Vijayakumar Manoj et al., Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing, Paladyn (Online), vol. 14, no. 1, Jan. 2023, doi: https://doi.org/10.1515/pjbr-2022-0118.

X. Mao, S. Almeida, John P.T. Mo, and S. Ding, The state of the art of electrical discharge drilling: a review, The International Journal of Advanced Manufacturing Technology, vol. 121, no. 5-6, pp. 2947-2969, Jul. 2022

M Vivekanandhan, K Rajmohan, and C Senthilkumar, Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network, Surface Topography: Metrology and Properties, vol. 10, no. 1, pp. 015007-015007, Jan. 2022.

J. Martin Sahayaraj, R. Arravind, P. Subramanian, S. Marichamy, and B. Stalin, Artificial neural network based prediction of responses on eglin steel using electrical discharge machining process, Materials Today: Proceedings, vol. 33, pp. 4417-4419, 2020.

V. Lalwani, P. Sharma, Catalin Iulian Pruncu, and Deepak Rajendra Unune, Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy, Journal of manufacturing and materials processing, vol. 4, no. 2, pp. 44-44, May 2020.

Rajeswari, R., & Shunmugam, M. S., Finishing performance of die-sinking EDM with ultrasonic vibration and powder addition through pulse train studies. Machining Science and Technology, vol.24, no. 2, pp. 245-273, 2020.

Rajeswari R, and Shunmugam M.S., Comparative evaluation of powder-mixed and ultrasonic-assisted rough die-sinking electrical discharge machining based on pulse characteristics. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 233, no. 14, pp. 2515-2530, 2019. doi: http://doi.org/10.1177/0954405419840569.

Rajeswari, R., and Shunmugam, M.S., Investigations into process mechanics of rough and finish die sinking EDM using pulse train analysis. International Journal of Advanced Manufacturing Technology, vol. 100, pp. 1945-1964, 2018.

T.L. Banh, H.-P. Nguyen, C. Ngo, and D.-T. Nguyen, Characteristics optimization of powder mixed electric discharge machining using titanium powder for die steel materials, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 232, no. 3, pp. 281-298, Feb. 2017.

V. S. Sreebalaji and K. R. Kumar, Artificial neural networks and multi response optimisation on EDM of aluminium (A380)/fly ash composites, International Journal of Computational Materials Science and Surface Engineering, vol. 6, no. 3/4, p. 244, 2016.

C. P. Mohanty, Siba Sankar Mahapatra, and Manas Ranjan Singh, A particle swarm approach for multi-objective optimization of electrical discharge machining process, Journal of Intelligent Manufacturing, vol. 27, no. 6, pp. 1171-1190, Dec. 2016.

Banu and M. Y. Ali, Electrical Discharge Machining (EDM): A Review, International Journal of Engineering Materials and Manufacture, vol. 1, no. 1, pp. 3-10, Sep. 2016,.

R. V. Barenji, H. H. Pourasl, and V. M. Khojastehnezhad, Electrical discharge machining of the AISI D6 tool steel: Prediction and modeling of the material removal rate and tool wear ratio, Precision Engineering, vol. 45, pp. 435-444, Jul. 2016.

Mohammad Reza Shabgard, Ahad Gholipoor, and Hamid Baseri, A review on recent developments in machining methods based on electrical discharge phenomena, The International Journal of Advanced Manufacturing Technology, vol. 87, no. 5-8, pp. 2081-2097, Mar. 2016.

T. Muthuramalingam and B. Mohan, Application of Taguchi-grey multi responses optimization on process parameters in electro erosion, Measurement, vol. 58, pp. 495-502, Dec. 2014.

S. Hinduja and M. Kunieda, Modelling of ECM and EDM processes, CIRP Annals, vol. 62, no. 2, pp. 775-797, 2013.

C.-J. Tzeng and R.-Y. Chen, Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach, International Journal of Precision Engineering and Manufacturing, vol. 14, no. 5, pp. 709-717, May 2013.

S. S. Agrawal and V. Yadava, Modeling and Prediction of Material Removal Rate and Surface Roughness in SurfaceElectrical Discharge Diamond Grinding Process of Metal Matrix Composites, Materials and Manufacturing Processes, vol. 28, no. 4, pp. 381-389, Apr. 2013.

V. K. Srivastava and P. M. Pandey, Experimental investigation on electrical discharge machining process with ultrasonicassisted cryogenically cooled electrode, Journal of Manufacturing Processes, vol. 227, no. 2, pp. 301-314, Feb. 2013.

E. Weingartner, F. Kuster, and K. Wegener, Modeling and simulation of electrical discharge machining, Procedia CIRP, vol. 2, pp. 74-78, 2012.

Kuntal Maji and Dilip Kumar Pratihar, Modeling of Electrical Discharge Machining Process Using Conventional Regression Analysis and Genetic Algorithms, Journal of materials engineering and performance (Print), vol. 20, no. 7, pp. 1121-1127, Sep. 2010.

K. H. Ho and S. T. Newman, State of the art electrical discharge machining (EDM), International Journal of Machine Tools and Manufacture, vol. 43, no. 13, pp. 1287-1300, Oct. 2003.