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A Hybrid Ensemble Learning Model For Evaluating The Surface Roughness Of AZ91 Alloy During The End Milling Operation
Panchanand Jha
, G. Shaikshavali
, M. Gowri Shankar
, M. Dinesh Sai Ram
, Din Bandhu
, Kuldeep K. Saxena
*
, Dharam Buddhi
, Manoj Kumar Agrawal
*
Corresponding author for this work
Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Bengaluru
Research output
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Contribution to journal
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Article
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peer-review
17
Citations (Scopus)
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INIS
surfaces
100%
milling
100%
learning
100%
hybrids
100%
alloys
100%
operation
100%
roughness
100%
errors
37%
performance
25%
speed
25%
cutting
25%
machining
25%
cost
12%
comparative evaluations
12%
depth
12%
metals
12%
randomness
12%
forests
12%
neural networks
12%
Engineering
End Milling
100%
Az91 Alloy
100%
Comparative Analysis
50%
Hybrid Model
50%
Product Performance
50%
Machining Parameter
50%
Spindle Speed
50%
Feed Rate
50%
Cutting Speed
50%
Metal Cutting
50%
Random Forest
50%
Mean Square Error
50%
Local Stress
50%
Endurance Limit
50%
Material Science
Milling
100%
Surface Roughness
100%
Machining
33%
Rough Surface
16%
Surface (Surface Science)
16%