Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network
Author:
Affiliation:
1. Department of Mechanical Engineering , TPEVR Government Polytechnic College , Vellore - , India
2. Department of Mechanical Engineering , Government College of Technology , Coimbatore , , India
Abstract
Publisher
Walter de Gruyter GmbH
Subject
General Chemical Engineering,General Chemistry,Biotechnology
Link
https://www.sciendo.com/pdf/10.2478/pjct-2022-0024
Reference40 articles.
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2. 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.2019.07.046.10.1016/j.jmrt.2019.07.046
3. 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/s00170-016-9262-4.10.1007/s00170-016-9262-4
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/S0219686708001486.10.1142/S0219686708001486
5. 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.2017.03.045.10.1016/j.electacta.2017.03.045
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