Optimization of Drilling Parameters using Genetic Algorithms |
Author(s): |
Vipin , PEC University of technology chandigarh; Suman Kant, PEC University of technology chandigarh; CS Jawalkar, PEC University of technology chandigarh |
Keywords: |
Drilling, surface roughness, genetic algorithms, Anova, Regression, Taguchi |
Abstract |
Surface roughness in drilling process is important aspect so there is needs to minimize surface roughness while drilling. This paper investigates the influence of drilling parameters such as spindle speed, feed rate and cutting tools on surface roughness while drilling titanium alloy. The experiments have been conducted by employing L27 orthogonal array by considering HSS, Coated HSS and carbide drills. After experimentation second order empirical modelling was done using regression method. Finally, optimization of drilling parameters for surface roughness was done through genetic algorithms. The genetic algorithms give minimum value of surface roughness was 0.0689 corresponding optimize drilling parameters were 576.069, 0.03, and carbide as spindle speed, feed rate and cutting tool respectively. |
Other Details |
Paper ID: IJSRDV4I50117 Published in: Volume : 4, Issue : 5 Publication Date: 01/08/2016 Page(s): 69-71 |
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