EVOLUTIONARY OPTIMIZATION OF WIRE EDM PROCESS FOR THE SURFACE FINISH ON A MAGNESIUM AZ91D ALLOY USING AN ANN AND A GENETIC ALGORITHM
Keywords:
ANN, genetic algorithm, magnesium alloy, wire EDM, optimization
Abstract
In this research the optimizations of the wire EDM process parameters to achieve a minimal surface roughness on a magnesium AZ91D alloy have been carried out. The experiments were conducted with three machining factors, i.e., the pulse-on time, the pulse-off time, and the wire feed, using a Box-Behnken design of experiment. The effects of the Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) models were compared and studied, and it has been found that the ANN approach predicts the perfect output response. The genetic algorithm (GA) was then utilized to determine the best machining parameters to provide a better surface finish using the projected ANN outcomes, which were then used to build a quadratic equation. Furthermore, the optimum machining parameters were identified for a better surface finish through the integration of the ANN and GA approach. Based on the aforementioned findings, this study showed that the suggested methods are capable of predicting the optimum machining parameters, which would be beneficial in the low-cost manufacturing sector.
References
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2. Eva María Rubio, María Villeta, Diego Carou, Adolfo Saa, Comparative analysis of sustainable cooling systems in intermittent turning of magnesium pieces, International Journal of Precision Engineering Manufacturing, Vol 15 (5) (2014), pp 929–940. https://doi.org/10.1007/s12541-014-0419-5
3. Ashvin, J, Makadia J.I, Nanavati, Optimisation of machining parameters for turning operations based on RSM, Measurement, Vol 46 (2013), pp. 1521–1529. https://doi.org/10.1016/j.measurement.2012.11.026
4. Dewangan S, Gangopadhyay S, Biswas C.K, Study of surface integrity and dimensional accuracy in EDM using Fuzzy TOPSIS and sensitivity analysis, Measurement, 63 (2015), 364–376. https://doi.org/10.1016/j.measurement.2014.11.025
5. Vinoth Kumar S, Pradeep Kumar M, Machining process parameter and surface integrity in conventional EDM and cryogenic EDM of Al–SiCp MMC, Journal of Manufacturing Processes, 20 (2015), 70–78. https://doi.org/10.1016/j.jmapro.2015.07.007
6. Kavimani, V, Prakash, K.S., Thankachan, T, Influence of machining parameters on wire electrical discharge machining performance of reduced graphene oxide/magnesium composite and its surface integrity characteristics. Compos. Part B Eng, 167(2019) 621–630. https://doi.org/10.1016/j.compositesb.2019.03.031
7. Gangadharudu Talla, Deepak Kumar Sahoo, S. Gangopadhyay, C.K. Biswas, Modeling and multi-objective optimization of powder mixed electric discharge machining process of aluminum/alumina metal matrix composite, Engineering Science and Technology, an International Journal, Vol 18(2015), 369-373. https://doi.org/10.1016/j.jestch.2015.01.007
8. Murahari Kolli, Adepu Kumar, Effect of dielectric fluid with surfactant and graphite powder on Electrical Discharge Machining of titanium alloy using Taguchi method, Engineering Science and Technology, an International Journal, vol 18 (2015), 524-535. https://doi.org/10.1016/j.jestch.2015.03.009
9. Tripathy S, Tripathy D K, Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis, Engineering Science and Technology, an International Journal, Vol 19(2016),62–70. https://doi.org/10.1016/j.jestch.2015.07.010
10. Rai J. K, Villedieu L, Xirouchakis P, Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations, International Journal of Advanced Manufacturing Technology, vol. 37 (2008), no. 3-4, pp. 256– 264. https://doi.org/10.1007/s00170-007-0973-4
11. Vishal Parashar, Rehman A, Bhagoria J, L, Puri YM, Kerf width analysis for wire cut electrical discharge machining for SS 304L using design of experiment, Indian Journal of Science and Technology, 3(4) (2010), 369-373.
12. Yıldızel S.A, Öztürk A.U, A study on the estimation of prefabricated glass fiber reinforced concrete panel strength values with an Artificial Neural Network Model, CMC-Computers Materials & Continua, vol.52 (2016), no.1, pp.41-52.
13. Harun Akku, Optimising the effect of cutting parameters on the average surface roughness in a turning process with the taguchi method, Materiali in Tehnologije, 52 (2018), 6,781-785. doi:10.17222/mit.2018.110
14. Raymond H, Myers, Douglas C, Montgomery, Christine M, Anderson-Cook. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, 2016.
15. Hamdia KM, Zhuang X, Rabczuk T, An efficient optimization approach for designing machine learning models based on genetic algorithm, Neural Computing and Applications, 33(6) (2021) 1923-335. https://doi.org/10.1007/s00521-020-05035-x
16. Montgomery D C, Design and analysis of experiments, John Wiley and sons, New York, 1991.
17. Shanthi Jayachandran, Mohan Raman, Thangavelu Ramasamy, Experimental investigation for the optimization of the WEDM process parameters to obtain the minimum surface roughness of the AL 7075 aluminium alloy employed with a zinc-coated wire using RSM and GA, Materiali in Tehnologije, 53(3) (2019),349-356. doi:10.17222/mit.2018.166
18. Pradeep Kumar M, Venkatesan R, Kaviarasan V, Evaluation of surface integrity in milling of magnesium alloy using artificial neural network and genetic algorithm, Materiali in Tehnologije, 52 (2018), no. 3, 367-373. doi:10.17222/mit.2017.198
19. Karkalos N E, Galanis N I, and Markopoulos A P, Surface roughness prediction for the milling of Ti-6Al-4V ELI alloy with the use of statistical and soft computing techniques, Measurement, 90 (2016) 25–35. doi:10.1016/j.measurement.2016.04.039
20. Uma Maheshwera Reddy Paturi, Suryapavan Cheruku, Venkat Phani Kumar Pasunuri, Sriteja Salike,Modeling of tool wear in machining of AISI 52100 steel using artificial neural networks,Materials Today: Proceedings,Vol 38 (2021), 5,2358-236. https://doi.org/10.1016/j.matpr.2020.06.581
21. Suresh P, Venkatesan R, Sekar T, Elango N, Optimization of Intervening Variables in MicroEDM of SS 316L using a Genetic Algorithm and Response-Surface Methodology, Strojniški vestnik – Journal of Mechanical Engineering, Vol 60 (2014), Issue 10, pp 656-664. https://doi.org/10.5545/sv-jme.2014.1665
22. Deb, K, Roy, P.C, Hussein, R, Surrogate Modeling Approaches for Multiobjective Optimization: Methods, Taxonomy, and Results, Math. Comput. Appl, 26(2021) 5. https://doi.org/10.3390/mca26010005
Published
2024-10-17
How to Cite
1.
Pradeepkumar M, Jesudas T, Sasikumar C, Narasimharajan M. EVOLUTIONARY OPTIMIZATION OF WIRE EDM PROCESS FOR THE SURFACE FINISH ON A MAGNESIUM AZ91D ALLOY USING AN ANN AND A GENETIC ALGORITHM. MatTech [Internet]. 2024Oct.17 [cited 2026Mar.15];58(5):663–669. Available from: https://mater-tehnol.si/index.php/MatTech/article/view/1107
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