• Mar 24, 2016 News!Vol.3, No.3 has been indexed by EI(Inspec)!   [Click]
  • Sep 27, 2017 News!IJMMM Vol.6, No.3 has been published with online version. 16 peer reviewed articles are published in this issue.   [Click]
  • Sep 25, 2017 News!IJMMM Vol.6, No.2 has been published with online version. 16 peer reviewed articles are published in this issue.   [Click]
General Information
    • ISSN: 1793-8198
    • Frequency: Quarterly
    • DOI: 10.18178/IJMMM
    • Editor-in-Chief: Prof. K. M. Gupta, Prof. Ian McAndrew
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/Indexing: EI (INSPEC, IET), Chemical Abstracts Services (CAS), Engineering & Technology Digital Library,  ProQuest, Crossref, Ulrich's Periodicals Directory, and Electronic Journals Library .
    • E-mail ijmmm@ejournal.net
Editor-in-chief
Prof. Ian McAndrew
Embry Riddle Aeronautical University, UK.
It is my honor to be the editor-in-chief of IJMMM. I will do my best to help develop this journal better.

IJMMM 2018 Vol.6(2): 82-87 ISSN: 1793-8198
DOI: 10.18178/ijmmm.2018.6.2.352

Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach

Pan Zou, Manik Rajora, Mingyou Ma, Hungyi Chen, Wenchieh Wu, and Steven Y. Liang
Abstract—In spite of much work done in mapping between the process parameters and performance indicators of electrochemical micro-machining (EMM), very sparse research is available on the optimization of its process parameters. In this article, first, an ANN trained using a hybrid Simulated Annealing (SA) – Levenberg-Marquardt (LM) is developed to map between the process parameters (voltage, feed-rate, and pulse-on time) and performance indicators (inlet and outlet diameters) of EMM. Once the prediction capabilities of the ANN are verified by the use of several testing data sets, the trained ANN is then used as a fitness function to optimize the process parameters of EMM that would lead to the minimization of taper and overcut. The optimization of the process parameters was accomplished using a Genetic Algorithm (GA) based approach. The prediction model was further validates by comparing the tendencies seen in the prediction model to those obtained using partial correlation coefficient.

Index Terms—Electrochemical micro-machining (EMM), genetic algorithm (GA), levenberg-marquardt (LM) algorithm, simulated annealing (SA).

P. Zou is with Donghua University, Songjiang, District, Shanghai 201620, China (e-mail: zoupzp123@gmail.com).
M. Rajora and S.Y. Liang are with Georgia Institute of Technology, Atlanta, Ga, 30332, USA (e-mail: manikrajora@gmail.com, steven.liang@me.gatech.edu).
M. Ma, H. Chen, and W. Wu are with Metal Industries Research & Development Center, Taichung 207 Taiwan, ROC (e-mail: mmy@mail.mirdc.org.tw, hyc@mail.mirdc.org.tw, hyc@mail.mirdc.org.tw).

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Cite: Pan Zou, Manik Rajora, Mingyou Ma, Hungyi Chen, Wenchieh Wu, and Steven Y. Liang, "Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach," International Journal of Materials, Mechanics and Manufacturing vol. 6, no. 2, pp. 82-87, 2018.

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