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

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.

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.

Selection of Minimum Quantity Lubrication (MQL) Parameters in Milling of Ti-6Al-4V

2012 ◽  
Vol 426 ◽  
pp. 139-142 ◽  
Author(s):  
Zhi Qiang Liu ◽  
X.J. Cai ◽  
Ming Chen ◽  
Qing Long An
Keyword(s):  
Cutting Force ◽  
End Milling ◽  
Cutting Temperature ◽  
Minimum Quantity Lubrication ◽  
Milling Process ◽  
Minimum Quantity ◽  
Nozzle Position ◽  
Mql System ◽  
End Milling Process ◽  
Selection Of

Different parameters of Minimum Quantity Lubrication (MQL) system, including air pressure, oil quantity, nozzle position, might have different influences on the cutting force and the cutting temperature. This paper presents an experiment of end-milling titanium alloy with MQL system. The objective of the experiment is to investigate the influences of MQL parameters in milling of Ti-6Al-4V. The results of experiment show that there are different effects on the cutting force and the cutting temperature with different MQL parameters, which will help to select different parameters in the end-milling process of Ti-6Al-4V.

scholarly journals Optimization of Cutting Parameters for Milling Process of (4032) Al-Alloy using Taguchi-Based Grey Relational Analysis

2021 ◽  
Vol 17 (3) ◽  
pp. 1-12
Author(s):  
Sami Abbas Hammood
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
End Milling ◽  
Cutting Parameters ◽  
Milling Process ◽  
Spindle Speed ◽  
Al Alloy ◽  
Relational Analysis ◽  
Grey Relational ◽  
End Milling Process

The objective of this work is to study the influence of end milling cutting process parameters, tool material and geometry on multi-response outputs for 4032 Al-alloy. This can be done by proposing an approach that combines Taguchi method with grey relational analysis. Three cutting parameters have been selected (spindle speed, feed rate and cut depth) with three levels for each parameter. Three tools with different materials and geometry have been also used to design the experimental tests and runs based on matrix L9. The end milling process with several output characteristics is solved using a grey relational analysis. The results of analysis of variance (ANOVA) showed that the major influencing parameters on multi-objective response were spindle speed and cutting tool with contribution percentage (52.75%, 24%), respectively. In addition, the optimum combination of end milling process parameters was then validated by performing confirmation tests to determine the improvement in multi-response outputs. The confirmation tests obtained a minimum (surface roughness and micro-hardness) and maximum metal removal rate with grey relational grade of 0.784 and improvement percentage of 2.3%.

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