Investigation and optimization of the effect of input parameters on output parameters of electrical discharge machining of A356 nano-composite reinforced by SiC

Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

مقاله 2، دوره 13، شماره 4، بهمن 2021، صفحه 5-18   

نوع مقاله: Research article

نویسندگان

Amir Rahmani؛ Ali Mokhtarian^* ؛ Mojtaba Rahimi

Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran

چکیده

In this study, the impact of input parameters of Electrical Discharge Machining (EDM) on A356 nano-composite reinforced by 6% SiC was investigated and optimized using Taguchi's method based on the L9 orthogonal array and duplicated levels method. We considered voltage, current intensity, pulse on-time, and pulse off-time as the input parameters. Furthermore, material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) were taken into account as the output parameters. The analysis of results and examination of the signal-to-noise graphs (S/N) and analysis of variance (ANOVA) were performed using Minitab@16 software. Moreover, with the determination of the loss function of total normalized values of the output parameters based on assumed weight functions, the optimal level of each input parameter was established. Besides, the magnitude of contribution percentage of each of the input parameters in the total variance was computed through the variance analysis. According to the achieved results, the second level of the voltage (250 V), the first level of the current intensity (10 A), the third level of the pulse on-time (100 µs), and the first level of the pulse off-time (30 µs) were determined as the optimal input parameters. The contribution percentage of the input parameters for voltage, current intensity, pulse on-time, and pulse off-time was determined respectively to be 20.7, 62.06, 9.19, and 8.05.

کلیدواژه‌ها

Electrical Discharge Machining (EDM)؛ Nano-composite؛ Taguchi method؛ Variance analysis؛ Optimization

مراجع

[1] Biqal,  F. (2010). Modern machining methods  Tarah publication. Sixth edition, (in Persian).

[2] Naderi, D., & Ghasemi, E. (2009). Fundamentals of machining using spark and wire cut. Tarrah publication.

[3] Erden, A., & Bilgin, S. (1980). Role of impurities in electric discharge machining. In Proceedings of 21st international machine tool design and research conference (pp. 345-350).

[4] Ming, Q., & He, L. (1995). Powder-suspension dielectric fluid for EDM. Journal of Materials Processing Technology, 52, 44-54.

[5] Mohan, B., Rajadurai, A., & Satyanarayana, K.G. (2004). Electric discharge machining of Al-SiC metal matrix composites using rotary tube electrode. Journal of Materials Processing Technology, 153-154(1), 978-985.

[6] George, P. M., Raghunath, B. K., Manocha, L. M., & Warrier, A. M. (2004). EDM machining of carbon–carbon composite—a Taguchi approach. Journal of materials processing technology, 145(1), 66-71.

[7] Kansal, H. K., Singh, S., & Kumar, P. (2005). Parametric optimization of powder mixed electrical discharge machining by response surface methodology. Journal of Materials Processing Technology, 169(3), 427–436.

[8] Abedpour, S., Jafari, E., & Hadidi Mood, S. (2008). Influence of EDM characteristic parameters on the surface microstructure in CK45 alloy steel. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 1(4), 31-40, (in Persian).

[9] Kuppan, P., Rajadurai, A., & Narayanan, S. (2008). Influence of EDM process parameters in deep hole drilling of Inconel 718. The International Journal of Advanced Manufacturing Technology, 38, 74–84.

[10] Rajmohan, T., & Prubho, R. (2012). Optimization of machining parameter in EDM of 304 stainless steel. Procedia Engineering, 38, 1030-1036.

[11] Gopalakannan, S., Senthilvelan, T., & Ranganathan, S. (2012). Modeling and optimization of EDM process parameters on machining of Al 7075-B4C MMC using RSM. Procedia Engineering, 38, 685-690.

[12] Sidhu, S. S., Batish, A., & Kumar, S. (2014). Study of Surface Properties in Particulate-Reinforced Metal Matrix Composites (MMCs) Using Powder-Mixed Electrical Discharge Machining (EDM). Materials and Manufacturing Processes, 29(1), 46-52.

[13] Sadr, P., Kolahdooz, A., & Eftekhari, S. A. (2015). The effect of electrical discharge machining parameters on alloy DIN 1.2080 using the Taguchi method and optimal determinant. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 8(2), 71-89, (in Persian).

[14] Sadr, P., Kolahdooz, A., & Eftekhari, S. A. (2016). Optimization of material removal rate in electrical discharge machining alloy on DIN1.2080 with the neural network and genetic algorithm. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 9(1), 77-92, (in Persian).

[15] Fattahi, H., & Park, A. (2018). Investigation of ultrasonic waves-assisted electrical discharge machining using the mixture of nano-powders of TiO2, ZnO, and Al2O3 in a dielectric. Amirkabir Journal of Mechanical Engineering, 50(3), 541-550, (in Persian).

[16] Lotfi, A., & Daneshmand S. (2019). Investigation of electrical discharge machining of composite aluminum reinforced by titanium oxide nano-particles. Modern Processes in Material Engineering, 13(2), 27-43, (in Persian).

[17] Shahbazi Dastjerdi, M., Mokhtarian, A., & Saraeian, P. (2020). The effect of alumina powder in dielectric on electrical discharge machining parameters of aluminum composite A413-Al2O3 by the Taguchi method, the signal-to-noise analysis and the total normalized quality loss. International Journal of Mechanical and Materials Engineering, 15(1), 1-11.

[18] Sajjadi, S. A., Parizi, M. T., Ezatpour, H. R., & Sedghi, A. (2012). Fabrication of A356 composite reinforced with micro and nano Al2O3 particles by a developed compocasting method and study of its properties. Journal of alloys and compounds, 511(1), 226-231.

[19] Masoudi, B., & Daneshmand, S. (2017). Investigation of the effect of electrical discharge machining parameters on Al 2024 matrix composite using TNQL and S/N. Modern Processes in Material Engineering, 11(1), 91-110, (in Persian).

[20] Antony, J. (2001). Simultaneous optimisation of multiple quality characteristics in manufacturing processes using Taguchi's quality loss function. The International Journal of Advanced Manufacturing Technology, 17(2), 134-138.

[21] Taguchi, G. (2005), Taguchi’s quality engineering handbook.

[22] Karimi, M., Toroghinejad, M. R., & Farmanesh, K. (2015). Multi-response optimization on the annealing of accumulative roll bonded monolithic Ti and Ti–SiCp composites. Materials & Design (1980-2015), 65, 34-41.

[23] Yousefieh, M., Shamanian, M., & Saatchi, A. (2011). Optimization of the pulsed current gas tungsten arc welding (PCGTAW) parameters for corrosion resistance of super duplex stainless steel (UNS S32760) welds using the Taguchi method. Journal of Alloys and Compounds, 509(3), 782-788.

[24] Ma, Y., Hu, H., Northwood, D., & Nie, X. (2007). Optimization of the electrolytic plasma oxidation processes for corrosion protection of magnesium alloy AM50 using the Taguchi method. Journal of materials processing technology, 182(1-3), 58-64.