Authors :
Asabe Maruti P; Dr. Sonawane S.A.
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/4y9mdp35
Scribd :
https://tinyurl.com/hxb72vnk
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR028
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This research aims to determine the optimal
Surface Roughness for machining D3 die steel alloy with
uncoated carbide inserts. It will do this by studying the
most efficient turning parameters, such as cutting speed,
feed, and depth of cut. Models have been generated
using a variety of statistical modeling approaches,
including Genetic Algorithm with Response Surface
Methodology. This research aimed to use the regression
technique to develop a model that could predict surface
roughness. It has also been investigated if the Taguchi
Technique may be used to optimize process parameters.
To decide the primary boundaries affecting Surface
Unpleasantness, we used Signal-to-Noise (S/N) ratio and
Analysis of Variance (ANOVA) tests. This paper aims to
contribute valuable insights into achieving the best
Surface Roughness outcomes in the machining process
for D3 die steel alloy with Uncoated Carbide Inserts. The
utilization of Genetic Algorithm and Response Surface
Methodology showcases a robust approach for modelling
intricate parameter interactions. If you know the values
of the parameters, you may use the Regression
Technique to forecast the surface roughness. Process
parameter optimization may be made more systematic
with the use of the Taguchi Technique.
Keywords :
Turning Operation, Surface Roughness, Mathematical Model, ANOVA, Taguchi Technique.
This research aims to determine the optimal
Surface Roughness for machining D3 die steel alloy with
uncoated carbide inserts. It will do this by studying the
most efficient turning parameters, such as cutting speed,
feed, and depth of cut. Models have been generated
using a variety of statistical modeling approaches,
including Genetic Algorithm with Response Surface
Methodology. This research aimed to use the regression
technique to develop a model that could predict surface
roughness. It has also been investigated if the Taguchi
Technique may be used to optimize process parameters.
To decide the primary boundaries affecting Surface
Unpleasantness, we used Signal-to-Noise (S/N) ratio and
Analysis of Variance (ANOVA) tests. This paper aims to
contribute valuable insights into achieving the best
Surface Roughness outcomes in the machining process
for D3 die steel alloy with Uncoated Carbide Inserts. The
utilization of Genetic Algorithm and Response Surface
Methodology showcases a robust approach for modelling
intricate parameter interactions. If you know the values
of the parameters, you may use the Regression
Technique to forecast the surface roughness. Process
parameter optimization may be made more systematic
with the use of the Taguchi Technique.
Keywords :
Turning Operation, Surface Roughness, Mathematical Model, ANOVA, Taguchi Technique.
