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dc.contributor.authorBandapalli, Chakradhar-
dc.contributor.authorSutaria, Bharatkumar Mohanbhai-
dc.contributor.authorBhatt, Dhananjay Vishnuprasad-
dc.identifier.issn0975-1017 (Online); 0971-4588 (Print)-
dc.description.abstractTitanium and its alloys are a few of the most suitable materials in medical applications due to their biocompatibility, anticorrosion and desirable mechanical properties compared to other materials like commercially pure Nb & Ta, Cr-Co alloys and stainless steels. High speed micro end milling is one of the favorable methods for accomplishing micro features on hard metals/alloys with better quality products delivering efficiently in shorter lead and production times. In this paper, experimental investigation of machining parameters influence on surface roughness in high speed micro end milling of Ti-6Al-4V using uncoated tungsten carbide tools under dry cutting conditions and prediction of surface roughness using adaptive neuro- fuzzy inference system (ANFIS) methodology has been presented. Using MATLAB tool box - ANFIS approach four membership functions - triangular, trapezoidal, gbell, gauss has been chosen during the training process in order to evaluate the prediction accuracy of surface roughness. The model’s predictions have been compared with experimental data for verifying the approach. From the comparison of four membership functions, the prediction accuracy of ANFIS has been reached 99.96% using general bell membership function. The most influential factor which influences the surface roughness has the feed rate followed by depth of cut.en_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJEMS Vol.26(5&6) [October & December 2019]en_US
dc.subjectMicro end millingen_US
dc.subjectSurface roughnessen_US
dc.titleEstimation of surface roughness on Ti-6Al-4V in high speed micro end milling by ANFIS modelen_US
Appears in Collections:IJEMS Vol.26(5&6) [October & December 2019]

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