Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

• Autorzy: Kanniyappan Vijayakumar , Tamilperuvalathan Sekar

Identyfikatory

DOI

10.2478/pjct-2022-0024

• Języki publikacji: EN

Abstrakty

EN

In our work, the process efficiency of the ECMM should be improved by using different combinations of nano-particles and added electrolytes. The superior aim of this work is to improve and predict the ECMM machining characteristics of die hardened steel, namely material removal rate (MRR), Tool wear rate (TWR) and Surface Roughness (Ra). The machining conditions are optimized using Response Surface Methodology (RSM) based on Box Behnken Design. The better Nano electrolyte is optimized using Deer Hunting Optimization (DHO) based on the machined outcomes, and the performances are predicted using a hybrid Deep Neural Network (DNN) based DHO. The hybrid DNN-DHO based predicted outcome of MRR is 0.361 mg/min, TWR is 0.272 mg/min and Ra is 2.511 μm. The validation results show that our proposed DNN-DHO model performed well and obtained above 0.99 regression for both training and validation of DNN-DHO, where the root mean square error ranges between 0.018 and 0.024.

Słowa kluczowe

EN

ECMM; Die hardened steel; machining parameters; RSM; hybrid; neural network; prediction

• Wydawca: West Pomeranian University of Technology. Publishing House
• Czasopismo: Polish Journal of Chemical Technology
• Rocznik: 2022
• Tom: Vol. 24, nr 4
• Strony: 7--22
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wz.

Twórcy

autor

Kanniyappan Vijayakumar

• Department of Mechanical Engineering, TPEVR Government Polytechnic College, Vellore-632002, India

autor

Tamilperuvalathan Sekar

• Department of Mechanical Engineering, Government College of Technology, Coimbatore, 641013, India

Bibliografia

• 1. Prakash, C., Kansal, H.K., Pabla, B.S. & Puri, S. (2017). Experimental investigations in powder mixed electric discharge machining of Ti–35Nb–7Ta–5Zrβ-titanium alloy. Materials and Manufacturing Processes, 32(3), 274–285. DOI: 10.1080/10426914.2016.1198018.
• 2. Sathish, T. (2019). Experimental investigation of machined hole and optimization of machining parameters using electro-chemical machining. J. Mater. Res. Technol., 8(5), 4354–4363. DOI: 10.1016/j.jmrt.2019.07.046.
• 3. He, H.D., Qu, N.S., Zeng, Y.B. & Yao, Y.Y. (2017). Enhancement of mass transport in wire electrochemical micro-machining by using a micro-wire with surface microstructures. The International J. Adv. Manufact. Technol., 89(9), 3177–3186. DOI: 10.1007/s00170-016-9262-4.
• 4. Sekar, T. & Marappan, R. (2008). Experimental investigations into the influencing parameters of electrochemical machining of AISI 202. J. Adv. Manufact. Systems, 7(02), 337–343. DOI: 10.1142/S0219686708001486.
• 5. Meng, L., Zeng, Y. & Zhu, D. (2017). Investigation on wire electrochemical micro machining of Ni-based metallic glass. Electrochimica Acta, 233, 274–283. DOI: 10.1016/j. electacta.2017.03.045.
• 6. Dong, S., Wang, Z. & Wang, Y. (2017). High-speed electrochemical discharge drilling (HSECDD) for micro-holes on C17200 beryllium copper alloy in deionized water. The International J. Adv. Manufact. Technol. 88(1), 827–835. DOI: 10.1007/s00170-016-8645-x.
• 7. Soundarrajan, M. & Thanigaivelan, R. (2019). Investigation of electrochemical micromachining process using ultrasonic heated electrolyte. Adv. Micro and Nano Manufact. Surf. Engin., Springer, Singapore, 423–434. DOI: 10.1007/978-981-32-9425-7_38.
• 8. Rathod, V., Doloi, B. & Bhattacharyya, B. (2017). Fabrication of microgrooves with varied cross-sections by electro-chemical micromachining. Internat. J. Adv. Manufact. Technol., 92(1), 505–518. DOI: 10.1007/s00170-017-0167-7.
• 9. Anasane, S.S. & Bhattacharyya, B. (2016). Experimental investigation on suitability of electrolytes for electrochemical micromachining of titanium. Internat. J. Adv. Manufact. Technol., 86(5), 2147–2160. DOI: 10.1007/s00170-015-8309-2.
• 10. Thanigaivelan, R., Arunachalam, R.M., Kumar, M. & Dheeraj, B.P. (2018). Performance of electrochemical micromachining of copper through infrared heated electrolyte. Mater. Manufact. Proces., 33(4), 383–389. DOI: 10.1080/10426914.2017.1279304.
• 11. Liu, W., Zhang, H., Luo, Z., Zhao, C., Ao, S., Gao, F. & Sun, Y. (2018). Electrochemical micromachining on titanium using the NaCl-containing ethylene glycol electrolyte. J. Mater. Proces. Technol., 255, 784–794. DOI: 10.1016/j. jmatprotec.2018.01.009.
• 12. Geethapriyan, T., Samson, R.M., Thavamani, J., Arun Raj, A.C. & Pulagam, B.R. (2019). Experimental investigation of electrochemical micro-machining process parameters on stainless steel 316 using sodium chloride electrolyte. Adv. Manufact. Proces. Springer, Singapore, 471-480. DOI: 10.1007/978-981-13-1724-8_45.
• 13. Bhuyan, B.K. & Yadava, V. (2013). Experimental modeling and multi-objective optimization of traveling wire electro-chemical spark machining (TW-ECSM) process. J. Mech. Sci. Technol., 27(8), 2467–2476. DOI: 10.1007/s12206-013-0632-7.
• 14. Sethi, A., Acharya, B.R. & Saha, P. (2022). Electrochemical dissolution of WC-Co micro-tool in micro-WECM using an Eco-friendly citric acid mixed NaNO3 electrolyte. J. The Electrochem. Soc., 169(3), 033503. DOI: 10.1149/1945-7111/ac54d9.
• 15. Yu, N., Fang, X., Meng, L., Zeng, Y. & Zhu, D. (2018). Electrochemical micromachining of titanium microstructures in an NaCl–ethylene glycol electrolyte. J. Appl. Electrochem., 48(3), 263–273. DOI: 10.1007/s10800-018-1145-y.
• 16. Tak, M., Reddy S.V., Mishra, A. & Mote, R.G. (2018). Investigation of pulsed electrochemical micro-drilling on titanium alloy in the presence of complexing agent in electrolyte. J. Micromanufac., 1(2), 142–153. DOI: 10.1177/2516598418784682.
• 17. Ma, N., Phattharasupakun, N., Wutthiprom, J., Tanggarnjanavalukul, C., Wuanprakhon, P., Kidkhunthod, P. & Sawangphruk, M. (2018). High-performance hybrid supercapacitor of mixed-valence manganese oxide/n-doped graphene aerogel nanoflower using an ionic liquid with a redox additive as the electrolyte: In situ electrochemical x-ray absorption spectroscopy. Electrochimica Acta, 271, 110–119. DOI: org/10.1016/j. electacta.2018.03.116.
• 18. Singh, P.K., Das, A.K., Hatui, G. & Nayak, G.C. (2017). Shape controlled green synthesis of CuO nanoparticles through ultrasonic assisted electrochemical discharge process and its application for supercapacitor. Mater. Chem. Phys., 198, 16–34. DOI: 10.1016/j.matchemphys.2017.04.070.
• 19. Sekar, T., Arularasu, M. & Sathiyamoorthy, V. (2016). Investigations on the effects of Nano-fluid in ECM of die steel. Measurement, 83, 38–43. DOI: 10.1016/j.measurement.2016.01.035.
• 20. Jiang, K., Wu, X., Lei, J., Wu, Z., Wu, W., Li, W. & Diao, D. (2018). Vibration-assisted wire electrochemical micromachining with a suspension of B4C particles in the electrolyte. Internat. J. Adv. Manufac. Technol., 97(9), 3565–3574. DOI: 10.1007/s00170-018-2190-8.
• 21. Geethapriyan, T., Muthuramalingam, T., Vasanth, S., Thavamani, J. & Srinivasan, V.H. (2019). Influence of nanoparticles-suspended electrolyte on machinability of stainless steel 430 using electrochemical micro-machining process. Adv. Manufac. Proces. Sprin., Singap. 433–440. DOI: 10.1007/978-981-13-1724-8_42.
• 22. Kumaar, J.R.V., Thanigaivelan, R. & Soundarrajan, M. (2022). A performance study of electrochemical micro-machining on SS 316L using suspended copper metal powder along with stirring effect. Mater. Manufac. Proces., 1–14. DOI: 10.1080/10426914.2022.2030874.
• 23. Yang, Y., Natsu, W. & Zhao, W. (2011). Realization of eco-friendly electrochemical micromachining using mineral water as an electrolyte. Precision Engin., 35(2), 204–213. DOI: 10.1016/j.precisioneng.2010.09.009.
• 24. Geethapriyan, T., Kalaichelvan, K. & Muthuramalingam, T. (2016). Multi performance optimization of electrochemical micro-machining process surface related parameters on machining Inconel 718 using Taguchi-grey relational analysis. La Metallurgia Italiana, 2016(4), 13–19.
• 25. Fard, A.F. & Hajiaghaei-Keshteli, M. (2016). Red Deer Algorithm (RDA); a new optimization algorithm inspired by Red Deers’ mating. Internat. Confer. Ind. Engin., IEEE 12, 331–342.
• 26. Pradeep, N., Sundaram, K.S. & Kumar, M.P. (2020). Performance investigation of variant polymer graphite electrodes used in electrochemical micromachining of ASTM A240 grade 304. Mater. Manufact. Proces., 35(1), 72–85. DOI: 10.1080/10426914.2019.1697445.
• 27. Krishnan, N., Deepak, J. & Hariharan, P. (2020). Multi-response optimization of electrochemical micromachining on masked SS304. Engin. Res. Express, 2(1), 015041. DOI: 10.1088/2631-8695/ab5eb9.
• 28. Panigrahi, D., Rout, S., Patel, S.K. and Dhupal, D. (2021). Stray current and its consequences on microstructure of Hastelloy C-276 during parametric investigation on geometrical features: fabricated by electrochemical micromachining. Internat. J. Adv. Manufact. Technol., 112(1), 133–156. DOI:10.1007/s00170-020-06365-9.
• 29. Prakash, J. & Gopalakannan, S. (2021). Teaching— learning-based optimization coupled with response surface methodology for micro electrochemical machining of aluminium nanocomposite. Silicon, 13(2), 409–432. DOI: 10.1007/s12633-020-00434-0.
• 30. Ranganayakulu, J., Srihari, P.V. & Rao, K.V. (2021). An optimization strategy to improve performance in electrochemical discharge machining of borosilicate glass using graph theory algorithm and desirability index. Silicon, 1–14. DOI: 10.1007/s12633-021-01317-8.
• 31. Gautam, N., Goyal, A., Sharma, S.S., Oza, A.D. & Kumar, R., 2022. Study of various optimization techniques for electric discharge machining and electrochemical machining processes. Materials Today: Proceedings, 57, 615–621. DOI: 10.1016/j.matpr.2022.02.005.
• 32. Aslan, N.E.V.Z.A.T. & Cebeci, Y.A.K.U.P. (2007). Application of Box–Behnken design and response surface methodology for modeling of some Turkish coals. Fuel, 86(1–2), 90–97. DOI: 10.1016/j.fuel.2006.06.010.
• 33. Barabadi, H., Honary, S., Ebrahimi, P., Alizadeh, A., Naghibi, F. & Saravanan, M. (2019). Optimization of myco-synthesized silver nanoparticles by response surface methodology employing Box-Behnken design. Inorganic and Nano-Metal Chemistry, 49(2), 33–43. DOI: 10.1080/24701556.2019.1583251.
• 34. Kim, S.G., Harwani, M., Grama, A. & Chaterji, S. (2016). EP-DNN: a deep neural network-based global enhancer prediction algorithm. Scientific reports, 6(1), 1–13. DOI: 10.1038/srep38433.
• 35. Brammya, G., Praveena, S., Ninu Preetha, N.S., Ramya, R., Rajakumar, B.R. & Binu, D. (2019). Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comp. J. DOI: 10.1093/comjnl/bxy133.
• 36. Elhami, S. & Razfar, M.R. (2020). Application of nano electrolyte in the electrochemical discharge machining process. Precision Engin., 64, 34–44. DOI: 10.1016/j.precisioneng.2020.03.010.
• 37. Teimouri, R. & Sohrabpoor, H. (2013). Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process. Front. Mech. Engin., 8(4), 429–442. DOI: 10.1007/s11465-013-0277-3.
• 38. Charak, A. & Jawalkar, C.S. (2020). Experimental studies in micro channelling on borosilicate glass using RSM optimization technique. Silicon, 12(7), 1707–1721. DOI: 10.1007/s12633-019-00269-4.
• 39. Rajput, V., Goud, M. & Suri, N.M. (2021). Performance analysis of closed-loop electrochemical discharge machining (CLECDM) during micro-drilling and response surface methodology based multi-response parametric optimization. Adv. Mater. Process. Technol.1–31. DOI: 10.1080/2374068X.2020.1860494.
• 40. Gopinath, C., Lakshmanan, P. & Amith, S.C. (2021). Production of Micro-holes on Duplex Stainless Steel 2205 by Electrochemical Micromachining: A Grey-RSM Approach. Arabian J. Sci. Engin., 46(3), 2769–2782. DOI: 10.1007/s13369-020-05277-w.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).