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Ceramic Tool Condition Monitoring in Machining of Inconel 718

D.Kondala Rao1 , Kolla Srinivas2 , Ch.Deva Raj3

Section:Research Paper, Product Type: Journal
Vol.7 , Issue.1 , pp.1-9, Mar-2019

Online published on Mar 10, 2019


Copyright © D.Kondala Rao, Kolla Srinivas , Ch.Deva Raj . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Citation :
IEEE Style Citation: D.Kondala Rao, Kolla Srinivas , Ch.Deva Raj, “Ceramic Tool Condition Monitoring in Machining of Inconel 718”, International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.1, pp.1-9, 2019.

MLA Style Citation: D.Kondala Rao, Kolla Srinivas , Ch.Deva Raj "Ceramic Tool Condition Monitoring in Machining of Inconel 718." International Journal of Scientific Research in Network Security and Communication 7.1 (2019): 1-9.

APA Style Citation: D.Kondala Rao, Kolla Srinivas , Ch.Deva Raj, (2019). Ceramic Tool Condition Monitoring in Machining of Inconel 718. International Journal of Scientific Research in Network Security and Communication, 7(1), 1-9.

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Abstract :
There is an in-depth discussion in this paper about the improvement of a system regarding tool wear monitoring in hard turning operation. Acoustic emission (AE) signals from metal cutting processes have been investigated for various purposes, including in-process tool wear monitoring. Hard turning is a machining process Nickel based alloys are difficult-to-machine materials which are widely used in various applications. Tool wear is a major problem in these materials because of their high hardness. The present study is focusing on Inconel 718 with varying HRC (51, 53, and 55) and the tool employed here is ceramic. By using L9 orthogonal array extracted from taguchi method, taking input parameters such as speed, feed, depth of cut and hardness. Taking vibration signal data as an input to ANOVA and Grey relation analysis (GRA) which identifies the optimal and most dominant feature (Root Mean Square(RMS), Crest Factor(CF), Skewness(Sk), Kurtosis(Ku), Absolute Deviation(AD), Mean, Standard Deviation(SD), Variance, peak, Frequency and Time in the tool wear operation.

Key-Words / Index Term :
Tool condition Monitoring, Dominant features, Acoustic Emission, Grey relation analysis, Anova

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