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Advances in Distributed Computing and Machine Learning pp 257–270Cite as

A Fuzzy Graph Recurrent Neural Network Approach for the Prediction of Radial Overcut in Electro Discharge Machining

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

Manufacturing of goods rely on its design methodology and the process parameters. The parameters used in manufacturing process play an important role to build a quality product. Initially heuristic techniques are used for parameter selection. Many researchers conducted research to predict the radial overcut using neural networks. Besides, fuzzy neural network gains more popularity due to presence of fuzzy system and neural network. In this paper fuzzy graph recurrent neural network architecture is used for modelling and predicting the radial over cut for an electro discharge machining information system.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT, Vellore, India

    Amrut Ranjan Jena & D. P. Acharjya

  2. School of Advanced Science, VIT, Vellore, India

    Raja Das

Authors
  1. Amrut Ranjan Jena
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  2. D. P. Acharjya
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  3. Raja Das
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Corresponding author

Correspondence to Amrut Ranjan Jena .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jena, A.R., Acharjya, D.P., Das, R. (2021). A Fuzzy Graph Recurrent Neural Network Approach for the Prediction of Radial Overcut in Electro Discharge Machining. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_26

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