scholarly journals Optimization of CNC end milling process parameters using PCA-based Taguchi method

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
S Moshat ◽  
S Datta ◽  
A Bandyopadhyay ◽  
P Pal
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Cnc End Milling ◽  
End Milling Process

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

TOPSIS based selection of optimal end milling process parameters

2020 ◽  
Author(s):  
A. Singh ◽  
I. Shivakoti ◽  
Z. Mustafa ◽  
R. Phipon ◽  
A. Sharma
Keyword(s):  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
End Milling Process ◽  
Selection Of

Role of end- milling process parameters on surface integrity of SS-304: Integrated taguchi-grey approach

2021 ◽  
Author(s):  
Gaurav Kumar ◽  
Pankul Goel ◽  
Mukesh Kumar ◽  
Ankit Tomer ◽  
Mohd Atif Wahid
Keyword(s):  
Process Parameters ◽  
Surface Integrity ◽  
End Milling ◽  
Milling Process ◽  
Ss 304 ◽  
End Milling Process

Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

2014 ◽  
Vol 592-594 ◽  
pp. 2733-2737 ◽  
Author(s):  
G. Harinath Gowd ◽  
K. Divya Theja ◽  
Peyyala Rayudu ◽  
M. Venugopal Goud ◽  
M .Subba Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Artificial Neural ◽  
End Milling Process

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

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