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Optimization of Electric Discharge Machining Based Processes

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Optimization of Manufacturing Processes

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

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Abstract

The results of Electrical Discharge Machining (EDM) are characterized through many parameters. These include, material removal rate , surface finish, geometrical accuracy, tool wear, and kerf width. The three main types of EDM, wire, sinker, and micro EDM all have similar characteristics in relation to input parameters and their effects on the results. The typical EDM system is too complex to accurately model the effect of all the parameters together. Therefore, it is necessary to create an optimization algorithm to predict the results of specific input parameters. Various techniques such as Taguchi robust design, grey relational analysis , desirability , genetic algorithm , and neural network etc. have been used for optimization of EDM based processes. This chapter first briefly introduces all the aforementioned optimization processes and comprehensively discusses their implementation and effect for optimization of EDM based processes.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH, 45056, USA

    Roan Kirwin, Aakash Niraula, Chong Liu, Landon Kovach & Muhammad Jahan

Authors
  1. Roan Kirwin
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  2. Aakash Niraula
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  3. Chong Liu
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  4. Landon Kovach
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  5. Muhammad Jahan
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Correspondence to Muhammad Jahan .

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Editors and Affiliations

  1. Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Doornfontein, Johannesburg, South Africa

    Kapil Gupta

  2. Assistant Professor, University Center for Research and Development, Chandigarh University, Gharuan, Mohali, India

    Munish Kumar Gupta

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Kirwin, R., Niraula, A., Liu, C., Kovach, L., Jahan, M. (2020). Optimization of Electric Discharge Machining Based Processes. In: Gupta, K., Gupta, M. (eds) Optimization of Manufacturing Processes. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-19638-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-19638-7_2

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