Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling

2018 ◽  
Vol 51 ◽  
pp. 582-596 ◽  
Author(s):  
Szymon Wojciechowski ◽  
Radosław W. Maruda ◽  
Grzegorz M. Krolczyk ◽  
Piotr Niesłony
Keyword(s):  
Grey Relational Analysis ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Ball End Milling ◽  
Relational Analysis ◽  
Noise Ratio ◽  
Grey Relational

scholarly journals High Speed Machining for Enhancing the AZ91 Magnesium Alloy Surface Characteristics Influence and Optimisation of Machining Parameters

2022 ◽  
Vol 72 (1) ◽  
pp. 105-113
Author(s):  
Vikas Marakini ◽  
Srinivasa P Pai ◽  
Uday K Bhat ◽  
Dinesh Thakur Singh ◽  
Bhaskar P Achar
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Signal To Noise Ratio ◽  
Surface Characteristics ◽  
Taguchi Design ◽  
Machining Parameters ◽  
Signal To Noise ◽  
Relational Analysis ◽  
Noise Ratio ◽  
Grey Relational

In this study, optimum machining parameters are evaluated for enhancing the surface roughness and hardness of AZ91 alloy using Taguchi design of experiments with Grey Relational Analysis. Dry face milling is performed using cutting conditions determined using Taguchi L9 design and Grey Relational Analysis has been used for the optimization of multiple objectives. Taguchi’s signal-to-noise ratio analysis is also performed individually for both characteristics and grey relational grade to identify the most influential machining parameter affecting them. Further, Analysis of Variance is carried to see the contribution of factors on both surface roughness and hardness. Finally, the predicted trends obtained from the signal-to-noise ratio are validated using confirmation experiments. The study showed the effectiveness of Taguchi design combined with Grey Relational Analysis for the multi-objective problems such as surface characteristics studies.

Optimization of Cutting Parameters of Multiple Performance Characteristics in End Milling of AlSi/AIN MMC – Taguchi Method and Grey Relational Analysis

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
S. H. Tomadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim ◽  
A. R. Daud
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Volume Fraction ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

The main objective of this paper is to investigate and optimize the cutting parameters on multiple performance characteristics in end milling of Aluminium Silicon alloy reinforced with Aluminium Nitride (AlSi/AlN MMC) using Taguchi method and Grey relational analysis (GRA). The fabrication of AlSi/AlN MMC was made via stir casting with various volume fraction of particles reinforcement (10%, 15% and 20%). End milling machining was done under dry cutting condition by using two types of cutting tool (uncoated & PVD TiAlN coated carbide). Eighteen experiments (L18) orthogonal array with five factors (type of tool, cutting speed, feed rate, depth of cut, and volume fraction of particles reinforcement) were implemented. The analysis of optimization using GRA concludes that the better results for the combination of lower surface roughness, longer tool life, lower cutting force and higher material removal could be achieved when using uncoated carbide with cutting speed 240m/min, feed 0.4mm/tooth, depth of cut 0.3mm and 15% volume fraction of AlN particles reinforcement. The study confirmed that with a minimum number of experiments, Taguchi method is capable to design the experiments and optimized the cutting parameters for these performance characteristics using GRA for this newly develop material under investigation.

scholarly journals Optimum combination of process parameters to optimize Surface Roughness and Chip Thickness during End Milling of Aluminium 6351-T6 Alloy Using Taguchi Grey Relational Analysis

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Reddy Sreenivasulu
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Feed Rate ◽  
End Milling ◽  
Chip Thickness ◽  
Optimum Combination ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

In any machining operations, quality is the important conflicting objective. In order to give assurance for high productivity, some extent of quality has to be compromised. Similarly productivity will be decreased while the efforts are channelized to enhance quality. In this study,  the experiments were carried out on a CNC vertical machining center (KENT and INDIA Co. Ltd, Taiwan make) to perform 10mm slots on Al 6351-T6 alloy work piece by K10 carbide, four flute end milling cutter as per taguchi design of experiments plan by L9 orthogonal array was choosen to determine experimental trials. Furthermore the spindle speed (rpm), the feed rate (mm/min) and depth of cut (mm) are regulated in these experiments. Surface roughness and chip thickness was measured by a surface analyser of Surf Test-211 series (Mitutoyo) and Digital Micrometer (Mitutoyo) with least count 0.001 mm respectively. Grey relational analysis was employed to minimize surface roughness and chip thickness by setting of optimum combination of machining parameters. Minimum surface roughness and chip thickness obtained with 1000 rpm of spindle speed, 50 mm/min feed rate and 0.7 mm depth of cut respectively. Confirmation experiments showed that Gray relational analysis precisely optimized the drilling parameters in drilling of Al 6351-T6 alloy. 

Study on the Surface Roughness of Titanium Alloy Machined by WEDM

2013 ◽  
Vol 683 ◽  
pp. 581-584 ◽  
Author(s):  
Shu Guo Zhao ◽  
Rui Li ◽  
Xiao Min Yao
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Electrical Discharge ◽  
Medical Equipment ◽  
Electrical Discharge Machining ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Relational Analysis ◽  
Electric Parameters ◽  
Grey Relational

Due to the excellent characteristics of titanium alloy, was applied to aviation, marine, automotive, metallurgy, medical equipment and other fields. However, some of the characteristics make it machine difficultly, so the range of applications would be limited. In this paper, titanium alloy is cut by wire electrical discharge machining (WEDM) and study the surface finish. The model of surface roughness is established which is based on theoretical analysis. Experimental results were analyzed and optimized by the theory of signal-to-noise ratio and grey relational analysis (GRA) method. The minimal surface roughness is achieved by the optimal results .According to GRA, get the order that is the influence of electric parameters on the surface roughness.

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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