Multiresponse optimization of end milling process parameters on ZE41A Mg alloy using Taguchi and TOPSIS approach

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.592-594.2733 ◽

2014 ◽

Vol 592-594 ◽

pp. 2733-2737 ◽

Cited By ~ 1

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

Al-Khwarizmi Engineering Journal ◽

10.22153/kej.2021.06.001 ◽

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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