Análisis de la integridad de la superficie y evaluación de la sostenibilidad en el mecanizado por electroerosión de un material compuesto de matriz metálica ingenieril Al-22% SiC

Subhashree Naik; Sudhansu Ranjan Das; Debabrata Dhupal; Ajit Kumar Khatua

doi:10.3989/revmetalm.210

Autores/as

Subhashree Naik Department of Production Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha https://orcid.org/0000-0002-8456-4043
Sudhansu Ranjan Das Department of Production Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha https://orcid.org/0000-0003-2723-4459
Debabrata Dhupal Department of Production Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha https://orcid.org/0000-0001-9428-294X
Ajit Kumar Khatua Department of Mechanical Engineering, TempleCity Institute of Technology & Engineering, Khurda 752057, Odisha https://orcid.org/0000-0001-7278-3743

DOI:

https://doi.org/10.3989/revmetalm.210

Palabras clave:

Acabado superficial, Electroerosión, Estimación de costes, Material compuesto de matriz metálica Al-22%SiC, Metodología de superficie de respuesta, Sostenibilidad

Resumen

En vista de las amplias aplicaciones que tienen los materiales compuestos ingenieriles de matriz metálica, particularmente en las industrias automotriz, eléctrica y aeroespacial, dar forma a estos materiales es un desafío realmente difícil. Esta investigación aborda el mecanizado por electroerosión de un material compuesto de matriz metálica Al-22% SiC para analizar la rugosidad de la superficie de las piezas mecanizadas. Se realizan una serie de pruebas de mecanizado en diversas condiciones de procesado (presión de chorro, voltaje, tiempo de activación del pulso, corriente de descarga, tiempo de desactivación del pulso) obtenido por un diseño Box- Behnken. Adicionalmente, este trabajo aborda la metodología de optimización deseada y modelado predictivo para la minimización de la calidad de la superficie mecanizada empleando la metodología de superficie de respuesta. Basado en el punto de vista motivacional de “Sea verde-piense en verde-actúe en verde”, se ha sugerido un enfoque único para el análisis económico y la evaluación de sostenibilidad para determinar el costo total de mecanizado por pieza y para justificar la utilidad del aceite vegetal como medio dieléctrico en el proceso de electroerosión. De acuerdo con este análisis estadístico, la contribución de la corriente de descarga de chispa se identificó como el factor principal en la degradación de la calidad de la superficie. El valor de rugosidad superficial óptima estimado (Ra) de 0,181 µm y el coste total de mecanizado calculado por pieza de Rs. 245,9 (2,95 €) se prefirieron con un tiempo de activación del pulso de 100 µs, voltaje de 1 V, tiempo de desactivación de pulso de 30 µs, corriente de descarga de 4 A y presión de lavado de 0,056 MPa, lo que indica que es tecno-económicamente viable. El aceite vegetal considerado como fluido dieléctrico es biodegradable, ambientalmente seguro y, por tanto, contribuye a tener una producción sostenible. Los datos de mecanizado de este material compuesto Al-SiC serían beneficiosos para la industria.

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Citas

Belloufi, A., Mezoudj, M., Abdelkrim, M., Rezgui, I., Chiba, E. (2020). Experimental and predictive study by multi-output fuzzy model of electrical discharge machining performances. Int. J. Adv. Manuf. Technol. 109, 2065-2093. https://doi.org/10.1007/s00170-020-05718-8

Bharathi Raja, S., Srinivas Pramod, C.V., Vamshee Krishna, K., Ragunathan, A., Vinesh, S. (2013). Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm. Eng. Comput. 31(1), 1-9. https://doi.org/10.1007/s00366-013-0320-3

Bharti, P.S., Maheshwari, S., Sharma, C. (2012). Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II. J. Mech. Sci. Technol. 26 (6), 1875-1883. https://doi.org/10.1007/s12206-012-0411-x

Cakir, M.V., Eyercioglu, O., Gov, K., Sahin, M., Cakir, S.H. (2013). Comparison of Soft Computing Techniques for Modelling of the EDM Performance Parameters. Adv. Mech. Eng. 5, 392531. https://doi.org/10.1155/2013/392531

Chattopadhyay, K.D., Verma, S., Satsangi, P.S., Sharma, P.C. (2009). Development of empirical model for different process parameters during rotary electrical discharge machining of copper-steel (EN-8) system. J. Mater. Process. Technol. 209 (3), 1454-1465. https://doi.org/10.1016/j.jmatprotec.2008.03.068

Chaudhury, P., Samantaray, S. (2021). Modelling and Optimization of Machining of SiC-CNT Conductive Ceramic Composite used for Micro and Nano Sensor by Electrical Discharge Machining. J. Inst. Eng. India Ser. D. https://doi.org/10.1007/s40033-021-00256-3

Dash, L., Padhan, S., Das, S.R. (2020). Experimental investigations on surface integrity and chip morphology in hard tuning of AISI D3 steel under sustainable nanofluid-based minimum quantity lubrication. J. Braz. Soc. Mech. Sci. Eng. 42 (10), 500 https://doi.org/10.1007/s40430-020-02594-x

D'Urso, G., Giardini, C., Ravasio, C. (2018). Effects of Electrode and Workpiece Materials on the Sustainability of Micro-EDM Drilling Process. Int. J. Precis. Eng. Manuf. 19 (11), 1727-1734. https://doi.org/10.1007/s12541-018-0200-2

Ekmekci, B. (2007). Residual stresses and white layer in electric discharge machining (EDM). Appl. Surf. Sci. 253 (23), 9234-9240. https://doi.org/10.1016/j.apsusc.2007.05.078

Gao, Q., Zhang, Qh., Su, S., Zhang, J. (2008). Parameter optimization model in electrical discharge machining process. J. Zhejiang Univ. Sci. A 9 (1), 104-108. https://doi.org/10.1631/jzus.A071242

Garcia Rojas, E.E.G., Coimbra, J.S.R., Telis-Romero, J. (2013). Thermophysical Properties of Cotton, Canola, Sunflower and Soybean Oils as a Function of Temperature. Int. J. Food Prop. 16 (7), 1620-1629. https://doi.org/10.1080/10942912.2011.604889

Gohil, V., Puri, Y. M. (2016). Statistical analysis of material removal rate and surface roughness in electrical discharge turning of titanium alloy (Ti-6Al-4V). Proc. Inst. Mech. Eng. B J. Eng. Manuf. 232 (9), 1603-1614. https://doi.org/10.1177/0954405416673104

Gopalakannan, S., Senthilvelan, T. (2013). A parametric study of electrical discharge machining process parameters on machining of cast Al/B4C metal matrix nanocomposites. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 227 (7), 993-1004. https://doi.org/10.1177/0954405413479505

Hanif, M., Wasim, A., Shah, A.H., Noor, S., Sajid, M., Mujtaba, N. (2019). Optimization of process parameters using graphene-based dielectric in electric discharge machining of AISI D2 steel. Int. J. Adv. Manuf. Technol. 103, 3735-3749. https://doi.org/10.1007/s00170-019-03688-0

Jagadish, Kumar, S. Soni, D.L. (2021). Performance Analysis and Optimization of Different Electrode Materials and Dielectric Fluids on Machining of High Carbon High Chromium Steel. In Electrical Discharge Machining. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. https://doi.org/10.1007/s40010-020-00727-4

Keskin, Y., Halkacı, H.S., Kizil, M. (2005). An experimental study for determination of the effects of machining parameters on surface roughness in electrical discharge machining (EDM). Int. J. Adv. Manuf. Technol. 28 (11-12), 1118-1121. https://doi.org/10.1007/s00170-004-2478-8

Kumar, S., Batish, A., Singh, R., Singh, T.P. (2014). A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys. J. Mech. Sci.Technol. 28 (7), 2831-2844. https://doi.org/10.1007/s12206-014-0637-x

Kumar, S., Dhingra, A.K., Kumar, S. (2017). Parametric optimization of powder mixed electrical discharge machining for nickel-based superalloy inconel-800 using response surface methodology. Mech. Adv. Mater. Mod. Process. 3 (1), 7. https://doi.org/10.1186/s40759-017-0022-4

Kumar, A., Grover, N., Manna, A., Chohan J.S., Kumar, R., Singh, S., Prakash, C., Pruncu, C.I. (2020). Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites. Adv. Compos. Lett. 29, 1-14. https://doi.org/10.1177/2633366X20963137

Majumder, A. (2014). Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 229 (9), 1504-1516. https://doi.org/10.1177/0954405414538960

Marichamy, S., Saravanan, M., Ravichandran, M., Veerappan, G. (2016). Parametric optimization of electrical discharge machining process on α-β brass using grey relational analysis. J. Mater. Res. 31 (16), 2531-2537. https://doi.org/10.1557/jmr.2016.213

Markopoulos, A.P., Manolakos, D.E., Vaxevanidis, N.M. (2008). Artificial neural network models for the prediction of surface roughness in electrical discharge machining. J. Intell. Manuf. 19 (3), 283-292. https://doi.org/10.1007/s10845-008-0081-9

Mohan, B., Rajadurai, A., Satyanarayana, K.G. (2004). Electric discharge machining of Al-SiC metal matrix composites using rotary tube electrode. J. Mater. Process. Technol. 153-154, 978-985. https://doi.org/10.1016/j.jmatprotec.2004.04.347

Mohanty, C.P., Mahapatra, S.S., Singh, M.R. (2014). A particle swarm approach for multi-objective optimization of electrical discharge machining process. J. Intell. Manuf. 27 (6), 1171-1190. https://doi.org/10.1007/s10845-014-0942-3

Mohanty, C.P., Mahapatra, S.S., Singh, M.R. (2017). An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm. Eng. Sci. Technol. Int. J. 20 (2), 552-562. https://doi.org/10.1016/j.jestch.2016.07.003

Muthuramalingam, T., Mohan, B. (2013). Influence of Discharge Current Pulse on Machinability in Electrical Discharge Machining. Mater. Manuf. Process. 28 (4), 375-380. https://doi.org/10.1080/10426914.2012.746700

Naik, S., Das, S.R., Dhupal, D. (2020). Experimental Investigation, Predictive Modeling, Parametric Optimization and Cost Analysis in Electrical Discharge Machining of Al-SiC Metal Matrix Composite. Silicon 13, 1017-1040. https://doi.org/10.1007/s12633-020-00482-6

Pellegrini, G; Ravasio, C. (2019). Evaluation of the sustainability of the micro-electrical discharge milling process. APEM 14 (3), 343-354. https://doi.org/10.14743/apem2019.3.332

Prabhu, S., Uma, M., Vinayagam, B.K. (2013). Electrical discharge machining parameters optimization using response surface methodology and fuzzy logic modeling. J. Braz. Soc. Mech. Sci. Eng. 36 (3), 637-652. https://doi.org/10.1007/s40430-013-0112-0

Pradhan, M.K., Das, R., Biswas, C.K. (2009). Comparisons of neural network models onsurface roughness in electrical discharge machining. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 223 (7), 801-808. https://doi.org/10.1243/09544054JEM1367

Prakash, C., Kansal, H.K., Pabla, B.S., Puri, S. (2016). Multi-objective optimization of powder mixed electric discharge machining parameters for fabrication of biocompatible layer on β-Ti alloy using NSGA-II coupled with Taguchi based response surface methodology. J. Mech. Sci. Technol. 30 (9), 4195-4204. https://doi.org/10.1007/s12206-016-0831-0

Puertas, I., Perez, C.J.L. (2003). Modelling the manufacturing parameters in electrical discharge machining of siliconized silicon carbide. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 217 (6), 791-803. https://doi.org/10.1243/09544050360673170

Rahul, Datta, S., Biswal, B.B., Mahapatra, S.S. (2017). A Novel Satisfaction Function and Distance-Based Approach for Machining Performance Optimization During Electro-Discharge Machining on Super Alloy Inconel 718. Arab. J. Sci. Eng. 42 (5), 1999-2020. https://doi.org/10.1007/s13369-017-2422-5

Rahul, Datta, S., Biswal, B.B., Mahapatra, S.S. (2019). Machinability Analysis of Inconel 601, 625, 718 and 825 during Electro-Discharge Machining: On Evaluation of Optimal Parameters Setting. Measurement 137, 382-400. https://doi.org/10.1016/j.measurement.2019.01.065

Ramesh, S., Jenarthanan, M.P. (2021). Optimizing the powder mixed EDM process of nickel based super alloy. Proc. Inst. Mech. Eng. E J. Process. Mech. Eng. 235 (4), 1092-1103. https://doi.org/10.1177/09544089211002782

Raza, M.H., Wasim, A., Ali, M.A., Hussain, S., Jahanzaib, M. (2018). Investigating the effects of different electrodes on Al6061-SiC-7.5 wt% during electric discharge machining. Int. J. Adv. Manuf. Technol. 99 (9-12), 3017-3034. https://doi.org/10.1007/s00170-018-2694-2

Reddy, V.V., Valli, P.M., Kumar, A., Reddy, C.S. (2014). Multi-objective optimization of electrical discharge machining of PH17-4 stainless steel with surfactant-mixed and graphite powder-mixed dielectric using Taguchi-data envelopment analysis-based ranking method. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 229 (3), 487-494. https://doi.org/10.1177/0954405414530904

Sahu, S.K., Jadam, T., Datta, S., Nandi, G. (2018). Effect of using SiC powder-added dielectric media during electro-discharge machining of Inconel 718 superalloys. J. Braz. Soc. Mech. Sci. Eng. 40 (7), 330. https://doi.org/10.1007/s40430-018-1257-7

Sahu, S.K., Datta, S. (2019). Experimental studies on graphite powder-mixed electro-discharge machining of Inconel 718 super alloys: Comparison with conventional electro-discharge machining. Proc. Inst. Mech. Eng. E J. Process. Mech. Eng. 233 (2), 384-402. https://doi.org/10.1177/0954408918787104

Sahu, A.K., Thomas, J., Mahapatra, S.S. (2020). An intelligent approach to optimize the electrical discharge machining of titanium alloy by simple optimization algorithm. Proc. Inst. Mech. Eng. E J. Process. Mech. Eng. 235 (2), 371-383. https://doi.org/10.1177/0954408920964685

Selvarajan, L., Manohar, M., Udhaya kumar, A., Dhinakaran, P. (2017). Modelling and experimental investigation of process parameters in EDM of Si3N4-TiN composites using GRA-RSM. J. Mech. Sci. Technol. 31 (1), 111-122. https://doi.org/10.1007/s12206-016-1009-5

Senthil Kumar, R., Suresh, P. (2019). Experimental study on electrical discharge machining of Inconel using RSM and NSGA optimization technique. J. Braz. Soc. Mech. Sci. Eng. 41, 35. https://doi.org/10.1007/s40430-018-1526-5

Shabgard, M.R., Farahmand, M R., Ivanov, A. (2009). Mathematical modelling and comparative study of the machining characteristics in ultrasonic-assisted electrical discharge machining of cemented tungsten carbide (WC-10%Co). Proc. Inst. Mech. Eng. B J. Eng. Manuf. 223 (9), 1115-1126. https://doi.org/10.1243/09544054JEM1461

Singh, S. (2012). Optimization of machining characteristics in electric discharge machining of 6061Al/Al2O3p/20P composites by grey relational analysis. Int J. Adv. Manuf. Technol. 63 (9-12), 1191-1202. https://doi.org/10.1007/s00170-012-3984-8

Singh, B., Kumar, J., Kumar, S. (2015). Optimization and surface modification in electrical discharge machining of AA 6061/SiCp composite using Cu-W electrode. Proc. Inst. Mech. Eng. Pt. L J. 231 (3), 332-348. https://doi.org/10.1177/1464420715596544

Singh, J., Sharma, R.K. (2017). Multi-objective optimization of green powder-mixed electrical discharge machining of tungsten carbide alloy. Proc. Inst. Mech. Eng. Part C 232 (16), 2774-2786. https://doi.org/10.1177/0954406217727306

Singh, N.K., Kumar, S., Singh, Y., Sharma, V. (2019). Predictive analysis of surface finish in gas assisted electrical discharge machining using statistical and soft computing techniques. Surf. Rev. Lett. 27 (4), 1950126. https://doi.org/10.1142/S0218625X19501269

Sivam, S.P., Michaelraj, A.L., Kumar, S.S., Prabhakaran, G., Dinakaran, D., Ilankumaran, V. (2013). Statistical multi-objective optimization of electrical discharge machining parameters in machining titanium grade 5 alloy using graphite electrode. Proc. Inst. Mech. Eng. Part B 228 (7), 736-743. https://doi.org/10.1177/0954405413511073

Talla, G., Sahoo, D.K., Gangopadhyay, S., Biswas, C.K. (2015). Modeling and multi-objective optimization of powder mixed electric discharge machining process of aluminum/alumina metal matrix composite. Int. J. Eng. Sci. Technol. 18 (3), 369-373. https://doi.org/10.1016/j.jestch.2015.01.007

Tang, L., Guo, Y.F. (2013). Electrical discharge precision machining parameters optimization investigation on S-03 special stainless steel. Int. J. Adv. Manuf. Technol. 70 (5-8), 1369-1376. https://doi.org/10.1007/s00170-013-5380-4

Tang, L., Du, Y.T. (2014). Experimental study on green electrical discharge machining in tap water of Ti-6Al-4V and parameters optimization. Int. J. Adv. Manuf. Technol. 70 (1-4), 469-475. https://doi.org/10.1007/s00170-013-5274-5

Tzeng, C.-J., Chen, R.-Y. (2013). Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach. Int. J. Precis. Eng. Manuf. 14 (5), 709-717. https://doi.org/10.1007/s12541-013-0095-x

Uyyala, S.B., Kumar, A., Krishna, A.G. (2014). Performance analysis of electrical discharge machining parameters on RENE 80 nickel super alloy using statistical tools. I.J.M.M.M. 15 (3/4), 212-234. https://doi.org/10.1504/IJMMM.2014.060551

Yadav, U.S., Yadava, V. (2014). Experimental modeling and multiobjective optimization of electrical discharge drilling of aerospace superalloy material. Proc. Inst. Mech. Eng. Part B 229 (10), 1764-1780. https://doi.org/10.1177/0954405414539299

Yildiz, Y., Sundaram, M.M., Rajurkar, K.P. (2012). Statistical analysis and optimization study on the machinability of beryllium-copper alloy in electro discharge machining. Proc. Inst. Mech. Eng. Part B 226 (11), 1847-1861. https://doi.org/10.1177/0954405412457610