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End wear compensation during planetary EDM of Ti–6Al–4V by adaptive neuro fuzzy inference system

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Abstract

Wear on the tool electrode is one of the most critical issues in electro discharge machining (EDM) process, as it affects the dimensional accuracy of the final feature as well as increase in total production cost due to the requirement of post processing. In present study, an attempt has been made to develop a compensation model for end wear of the tool electrode during planetary EDM of Ti–6Al–4V using adaptive neuro fuzzy inference system (ANFIS). Prior to model development, detailed analysis has been carried out to understand the effect of various electrical as well as tool actuation parameters on end wear of the tool electrode. Further, an algorithm is coded in MATLAB interface using the ANFIS model developed for end wear as the prediction element. The proposed model is capable of providing the compensated machining depth for a specified cavity dimension when a set of electrical and tool actuation parameters are provided. Validation of the model has been carried out by comparing the predicted and actual results for machining depth under different experimental conditions. The values of compensated depth obtained using the proposed model are found to be in reasonable agreement with actual results.

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Acknowledgements

This work is supported by Gujarat Council of Science and Technology (GUJCOST), Government of Gujarat, India [Grant permission number: GUJCOST/MRP/2014-15/402]. Further, the assistance provided by Mr. Parth U. Rana, Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, Gujarat, for MATLAB coding is also deeply acknowledged.

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  1. Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, Gujarat, 395007, India

    Vishal John Mathai, Harshit K. Dave & Keyur P. Desai

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  1. Vishal John Mathai
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  2. Harshit K. Dave
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  3. Keyur P. Desai
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Correspondence to Vishal John Mathai.

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Mathai, V.J., Dave, H.K. & Desai, K.P. End wear compensation during planetary EDM of Ti–6Al–4V by adaptive neuro fuzzy inference system. Prod. Eng. Res. Devel. 12, 1–10 (2018). https://doi.org/10.1007/s11740-017-0778-8

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  • DOI: https://doi.org/10.1007/s11740-017-0778-8

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