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Surface Roughness Prediction in Grinding Ti using ANFIS Hybrid Algorithm

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Field Value
 
Title Surface Roughness Prediction in Grinding Ti using ANFIS Hybrid Algorithm
 
Creator Stephen, Deborah Serenade
Prabhu, Sethuramalingam
 
Subject ANFIS
CNT Grinding wheel
Fuzzy logic
Regression analysis
Sensitivity analysis
Taguchi analysis
 
Description 668-677
Intelligent manufacturing is needed, and many techniques and tools have been developed with this in mind. Over time,
many of these techniques have been combined, and hybrid approaches have provided better results in shorter times, leading
to a more precise prediction of outcomes when compared to the use of individual tools. This research focused on grinding
Ti-6Al-4V workpiece material with a Carbon nanotube (CNT) incorporated grinding wheel. The Adaptive Neuro-Fuzzy
Inference System (ANFIS) was used to predict surface roughness which was taken as the output of choice for this study. A
new hybrid of ANFIS with Genetic Algorithm (ANFIS-GA) was then proposed to see if this prediction method could obtain
greater precision. The regression analysis predicted the experimental model’s linear relationship to surface roughness, and
the effect of grinding process parameters on surface roughness was analysed using the sensitivity analysis method.
 
Date 2022-11-01T04:56:02Z
2022-11-01T04:56:02Z
2022-10
 
Type Article
 
Identifier 0971-4588 (Print); 0975-1017 (Online)
http://nopr.niscpr.res.in/handle/123456789/60752
https://doi.org/10.56042/ijems.v29i5.47533
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJEMS Vol.29(5) [October 2022]