International Journal of Scientific Research and Engineering Development

International Journal of Scientific Research and Engineering Development


( International Peer Reviewed Open Access Journal ) ISSN [ Online ] : 2581 - 7175

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Experimental Investigation and Development of Mathematical Correlations of Cutting Parameters for Machining Titanium with CNC WEDM



    International Journal of Scientific Research and Engineering Development (IJSRED)

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Published Issue : Volume-4 Issue-1
Year of Publication : 2021
Unique Identification Number : IJSRED-V4I1P35
Authors : Sabhavath Mutyala Kumar Naik, DR.P. Shreenivasa Rao
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Abstract :

process material Titanium different parameters of machining current, cutting speed, spark gap and Material removal rate will be investigated and best suited values for stable and controlled machining with least wire breakage.In the present work aimed at Experimental Investigation to determining optimal values of machining parameters in the machining of Titanium material of different Thickness using wire cut electric discharge machine. It also aimed at development of mathematical correlations to determine the effect of machining parameters on Current, Cutting speed, Spark gap, and Material Removal Rate investigated and best suited values for stable and controlled machining with least wire breakage. The experiments are conducted on the Titanium material by cutting L and U shapes by varying machining current from a lower value to a higher value in 5 steps. The cutting speed is noted down from machine display and surface finish is measured on using Tally surf.The spark gap is calculated from cutting width. The optimum values of machining current, cutting speed, spark gap, surface roughness and MRR are used for plotting the curves and best fit curve is selected using the Origin 8.0,software.The mathematical relation is for best fit curve and statistically analysis is performed to find fitness of the curve. The maximum error obtained from calculated values and experimental values are found to be less than 4 %.From these we, conclude that Regression Statistical analysis gives better prediction values with less error%.