This document investigates the machinability of aluminum alloy 6061, focusing on the effects of spindle speed, feed rate, and depth of cut on surface roughness and hardness. Various studies are reviewed to determine optimal machining parameters that achieve desired surface qualities. Experimental results demonstrate that specific control factors significantly influence both surface roughness and hardness, suggesting that optimized parameters improve machining efficiency.

1. JIS COLLEGE OF ENGINEERING
Mechanical Engineering
Department
Presented By-
1. Pradip Ghosh
2. Tanmoy Halder
3. Amit Banerjee
4. Aniket Pal
INVESTIGATING MECHINABILITY OF ALUMINIUM
WITH VARIOUS PROCESS PARAMETER ON CNC

2. Surface roughness and Hardness, an indicator of
surface quality is one of the most specified
customer requirements in a machining process. For
efficient use of machine tools, optimum cutting
parameters (speed, feed and depth of cut) are
required.
Aluminum is one of the metals, besides iron and
steel, which are widely used in many industrial
fields such as aviation, navigation and automotive.
INTRODUCTION

3. OBJECTIVE
Study on the Effects of Spindle speed ,
Feed rate and Depth of cut on surface
roughness of Aluminium-6061 alloy.
Study on the Effects of Spindle speed ,
Feed rate and Depth of cut on Hardness of
Aluminium-6061 alloy.

4. RavindraThamma [1] has found different models to obtain optimal machining
parameters for required surface roughness for an aluminum 6061 work pieces.
He concluded that Spindle speed, feed rate, and nose radius have significant
control factors for surface roughness. Smoother surfaces will be produced when
machined with a larger spindle speed, smaller feed rate, and nose radius Depth
of cut has a significant influence on surface roughness.
 H. M. Somashekaraet. al. [2] used control factors e.g. cutting speed, feed rate
and depth of cut to optimize Surface Roughness while machining Al 6351-T6
alloy with Uncoated Carbide tool. They used Taguchi Technique to optimize the
process parameters and confirmation test were also performed for finding main
factors influencing Surface Roughness. They concluded that Speed has a greater
influence on the Surface Roughness.
Gaurav Vohra et. al. [3] have optimized the machining parameters for boring
of aluminum material on CNC turning centre e.g. cutting speed, feed rate and
depth of cut, to obtain optimal material removal rate and minimum surface
roughness by using the Taguchi method. The conclusion found that the spindle
speed and depth of cut are the most affecting parameters for metal removal
rate and for roughness spindle speed and feed rate are the most affecting
parameters.
LITERATURE REVIEW
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5. Ranganath M S et. al. [4] have studied the parameters, which affects surface
roughness produced during the machining process of aluminum 6061 on CNC
turning machine. They used Taguchi Methodology and confirmation test
ANOVA to analyze the experimental results. The conclusion found that the
feed rate and spindle speed are the most significant process parameters on
surface roughness.
Biswajit Das et. al. [5] has studied surface roughness affecting parameters on
turning operation using CNC turning machine. The mainly affected control
factors in experimentation were spindle speed, feed and depth of cut. They
found that, the feed rate is the affecting parameter for surface roughness.
Md. Tayab Ali et. al. [6] have optimized the machining parameters such as
spindle speed, feed rate, and depth of cut for optimization of material
Removal Rate (MRR) and Surface Roughness for machining of alloy of
Aluminum (AA6063-T6) using carbide tool in dry condition on CNC Lathe .They
concluded that the most affecting parameters for surface roughness are feed,
cutting speed and least affecting factor is depth of cut. For metal removal rate,
the depth of cut and the cutting speed is the most affecting parameters and
least affecting factor is feed.
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6.  Ali Abdallah et. al. [7] had optimized machining parameters for the surface
roughness with aluminum alloy 6061 material in CNC Lathe. They uses „response
surface methodology‟ on control factors such as feed rate, spindle speed, and depth
of cut, and minimize surface roughness and maximize the material removal rate for
CNC turning operation. Based on the results of surface roughness it was found that
feed rate affects both surface roughness and metal removal rate. The spindle speed is
the most significant control factor for surface roughness than metal removal rate.
Larger spindle speed results minimum surface roughness and this result can be
explained along with other affecting parameters.
 S.V. Alagarsamyet. al. [8] has conducted experiment by turn aluminum Alloy 6061
using TNMG 115 100 tungsten carbide tool at three levels (Spindle speed, feed rate &
DOC) of cutting parameters and analyzed by employing Taguchi methodology and
respond surface methodology. Taguchi method and Response surface methodology
were applied for analyzing to get optimum surface roughness and material removal
rate for turning process of Aluminum Alloy 7075 using CNC machine via considering
three influencing input parameters- spindle speed, Feed rate and Depth of Cut. They
found that the input parameter feed rate has most influencing to the quality
characteristics of surface roughness and depth of cut being most affecting parameter
for MRR.
David et al. (2006)[9] described an approach to predict Surface roughness in a high
speed end-milling process and used artificial neural networks (ANN) and statistical
tools to develop different surface roughness predictors. Cntd.

7.  Srikanth and Kamala (2008) [10] proposed a real coded genetic algorithm
(RCGA) to find optimum cutting parameters and explained various issues of
RCGA and its advantages over the existing approach of binary coded genetic
algorithm (BCGA).
 Franic and Joze (2003) [11] used binary coded genetic algorithm (BCGA) for
the optimization of cutting parameters. This genetic algorithm optimizes the
cutting conditions having an influence on production cost, time and quality of
the final product.
 Suresh et al. (2002) [12] developed optimum surface roughness predictive
model using binary coded genetic algorithm (BCGA). This GA program gives
minimum and maximum values of surface roughness and their respective
optimal machining conditions.
Yang and Tarng (1998] [13] used Taguchi method for design optimization on
surface quality. An orthogonal array, the signal-to-noise (S/N) ratio and the
analysis of variance (ANOVA) were employed to investigate the cutting
characteristics.
Uros and Franci (2003) [14] proposed a neural network-based approach to
complex optimization of cutting parameters and described the multi-objective
technique of optimization of cutting conditions by means of the neural
networks taking into consideration the technological, economic and
organizational limitations.
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8. Oktem et al. (2005) [15] utilized response surface methodology to create an
efficient analytical model for surface roughness in terms of cutting parameters:
Feed, cutting speed, axial depth of cut, radial depth of cut and machining tolerance.
Al-Ahmari (2007) [16] developed empirical models for tool life, surface
roughness and cutting force for turning operations. Two important data mining
techniques used were response surface methodology and neural networks.
 Huang and Joseph (2001) [17] predicted in-process surface roughness through
multiple regression model in turning operation via accelerometer.
 Hossain et al. (2008) [18] developed an artificial neural network algorithm for
predicting the surface roughness in end milling of Inconel 718 alloy.
Avisekh et al. (2009) [19] conducted a study of feasibility of on-line monitoring of
surface roughness in turning operations using a developed opto-electrical
transducer. Regression and neural network (NN) models were exploited to predict
surface roughness and compared to actual and on-line measurements.
 Groover and Mikell (1996) [20] depicted the impact of three factors, namely, the
feed, nose radius, and cutting-edge angles, on surface roughness.
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9. Azouzi and Guillot (1997) [21] proposed an on-line prediction of surface finish and
dimensional deviation in turning using neural network based sensor fusion.
 Feng and Hu (2001) [22] addressed a comparative study of the ideal and actual
surface roughness in finish turning and also applied the fractional factorial
experimentation approach for studying the impact of turning parameters on the
roughness of turned surfaces and used analysis of variances to examine the impact of
turning factors and factor interactions on surface roughness.
 Muammer et al. (2007) [23] addressed regression analysis and neural network-
based models used for the prediction of surface roughness and compared for various
cutting conditions in turning.
Bajic et al. (2008) [24] focused on modeling of machined surface roughness and
optimization of cutting parameters in face milling and examined the influence of
cutting parameters on surface roughness in face milling.
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10. Sakir et al. (2008)[25] worked on the prediction of surface roughness using
artificial neural network in lathe and investigated the effect of tool geometry on
surface roughness in universal lathe and carried out machining process on AISI
1040 steel in dry cutting condition using various insert geometry at depth of cut
of 0.5 mm. Optimization of machining parameters not only increases the utility
for machining economics, but also the product quality to a great extent. The
dynamic nature and widespread usage of turning operations in practice have
raised a need for seeking a systematic approach that can help to set-up turning
operations in a timely manner and also to achieve the desired surface roughness
quality. After a detailed literature survey, it is inferred that there are no
appropriate surface recognition models for machining Brass C26000 metal in
CNC turning. The experimental works were conducted in a leading pump
manufacturing company. The seamless pipe which is being manufactured in the
pump industry made up of B surface area that is considered in this work brass
C26000 requires more surface finish in the inner.

11. Aluminum
alloy
Cast Alloy
Wrought
Alloy
Excellent
machinability
Machining Difficulties
due to mixing of Copper;
Magnesium; Zinc

12. SPECIFICATION OF ALUMINIUM
COMPOSITION OF “Al-6061” OR “Alloy 61S”
SILICON (0.4% to 0.8%)
IRON(0.7%)
COPPER(0.15 to 0.4%)
MANGANESE (0.15%)
MAGNESIUM(0.8 to 1.2%)
CHROMIUM(0.04 to 0.35%)
ZINC(0.25%)
TITANIUM(0.15%)
OTHER IMPURITY(Less than 0.05%)
REMAINDER ALUMINIUM(95.85 to 98.56%)

13. 1. CNC MILLING MACHINE (M-TAB FLEXMILL)
Milling is the machining process of using rotary cutters to remove
material from a work piece by advancing (or feeding) in a direction at an angle
with the axis of the tool. It covers a wide variety of different operations and
machines, on scales from small individual parts to large, heavy-duty gang milling
operations. It is one of the most commonly used processes in industry and
machine shops today for machining parts to precise sizes and shapes.
Most CNC milling machines (also called machining centers) are computer
controlled vertical mills with the ability to move the spindle vertically along the
Z-axis. This extra degree of freedom permits their use in die sinking, engraving
applications, and 2.5D surfaces such as relief sculptures. When combined with
the use of conical tools or a ball nose cutter, it also significantly improves milling
precision without impacting speed, providing a cost-efficient alternative to most
flat-surface hand-engraving work.
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EXPERIMENTAL SET-UP

14. SPECIFICATIONS OF CNC FLEXMILL:

15. 2. SURFACE ROUGHNESS TESTER

16. 3. HARDNESS TESTING MACHINE
The machine is designed for
determining the Brinell
Hardness (BHN) of metals and
alloys. The load is applied by
manually and the indentation
is read with the help of a
Brinell Microscope.

17. 4. JOB SET UP
We have taken Aluminium alloy 6061 grade for the experiment and
performing the operation in the laboratory
DIMENSION OF JOB – 90*90*10(All in mm)

18. RESULTS AND ANALYSIS
DETERMINATION OF ROUGHNESS VALUE AFTER MACHINING-I :
SECTION NO. VARIABLES ROUGHNESS VALUE (RMS VALUE)
1ST VALUE 2ND VALUE 3RD VALUE MEAN
SECTION NO .
I
f= 45
s= 750
z= 0.5
0.343 0.301 0.393 0.345
SECTION NO.
II
f= 45
s= 750
z= 0.75
0.641 0.672 0.547 0.620
SECTION NO
III
f= 45
s= 750
z= 1.0
0.686 0.639 0.701 0.675

19. 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 0.2 0.4 0.6 0.8 1 1.2
Depthth of Cut (Z) Vs RMS
Z VS RMS

20. SECTION NO. VARIABLES ROUGHNESS VALUE (RMS VALUE)
1ST VALUE 2ND VALUE 3RD VALUE MEAN
SECTION NO
. I
f= 60
s=750
z=0.75
0.609 0.655 0.690 0.651
SECTION NO.
II
f=67.5
s=750
z=0.75
0.599 0.637 0.591 0.609
SECTION NO
III
f=75
s=750
z=0.75
0.747 0.697 0.674 0.706
DETERMINATION OF ROUGHNESS VALUE AFTER MACHINING-II :

21. 0.6
0.62
0.64
0.66
0.68
0.7
0.72
0 10 20 30 40 50 60 70 80
Feed Vs RMS
Feed Vs RMS

22. SECTION
NO.
VARIABLES ROUGHNESS VALUE (RMS VALUE)
1ST VALUE 2ND VALUE 3RD VALUE MEAN
SECTION
NO . I
f=67.5
s=800
z=0.75
0.377 0.404 0.454 0.411
SECTION
NO. II
f=67.5
s=850
z=0.75
0.552 0.567 0.535 0.551
SECTION
NO III
f=67.5
s=900
z=0.75
0.562 0.577 0.552 0.563
DETERMINATION OF ROUGHNESS VALUE AFTER MACHINING-III :

23. 0
0.1
0.2
0.3
0.4
0.5
0.6
780 800 820 840 860 880 900 920
Speed Vs RMS
Speed Vs RMS

24. SURFACE ROUGHNESS GRAPH ANALYSIS
 we can state that, the optimum cutting will be Spindle
Speed 800 RPM, Feed rate 67.5 mm/min and Depth of cut
0.50 mm. The performance of Machining will be
maximum on these parameters and we can get the better
quality of product.

25. DETERMINATION OF HARDNESS AFTER MACHINING-I :
HARDNESS:
SECTION NO. VARIABLES HARDNESS VALUE( BHN NO.)
1ST
VALUE
2ND
VALUE
3RD
VALUE
MEAN
VALUE
SECTION NO. I f= 45
s= 750
z= 0.5
37 38.5 35.5 37
SECTION NO.
II
f= 45
s= 750
z= 0.75
39 38.5 40 39.1
SECTION NO.
III
f= 45
s= 750
z= 1.0
38 38 37.5 37.8

26. HARDNESS:
36.5
37
37.5
38
38.5
39
39.5
0 0.2 0.4 0.6 0.8 1 1.2
Depth of cut Vs Hardness
Depth of cut Vs Hardness

27. SECTION NO. VARIABLES HARDNESS VALUE( BHN NO.)
1ST
VALUE
2ND
VALUE
3RD
VALUE
MEAN
VALUE
SECTION NO.
I
f= 60
s=750
z=0.75
30.5 29 35 31.5
SECTION NO.
II
f=67.5
s=750
z=0.75
36 35 37 36
SECTION NO.
III
f=75
s=750
z=0.75
35 33 37 35
DETERMINATION OF HARDNESS AFTER MACHINING-II :

28. 31
31.5
32
32.5
33
33.5
34
34.5
35
35.5
36
36.5
0 10 20 30 40 50 60 70 80
Feed Vs Hardness
Feed Vs Hardness

29. SECTION NO. VARIABLES HARDNESS VALUE( BHN NO.)
1ST VALUE 2ND VALUE 3RD VALUE MEAN
VALUE
SECTION NO.
I
f=67.5
s=800
z=0.75
32 27.5 30 29.8
SECTION NO.
II
f=67.5
s=850
z=0.75
33 35.5 30 32.8
SECTION NO.
III
f=67.5
s=900
z=0.75
29 34 31.5 31.5
DETERMINATION OF HARDNESS AFTER MACHINING-III :

30. 29.5
30
30.5
31
31.5
32
32.5
33
780 800 820 840 860 880 900 920
Speed Vs Hardness
Speed Vs Hardness

31. HARDNESS GRAPH ANALYSIS
 we can say that for the greater Hardness value after
machining Aluminium-6061 grade the machining parameters
should be like z=0.75mm, f=67.5mm/min and s=850 RPM and
then we could get better quality product.

32. The experimentation uses to obtain optimum machining
condition for surface roughness of aluminum in CNC milling
operation. The initial stage of experimentation consists of
evaluating the effect of control factors, which mainly affect the
output parameter.
The experimentation was carried out by varying control factors,
which results the control factors such as spindle speed, feed rate
and depth of cut mainly affect the output parameter surface
roughness. From this study the range of parameter also selected.
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CONCLUSIONS

33. Total 9 experimental steps are performed for this experiment and
by using CNC Milling machine, the following Conclusion were
found:
For surface roughness (Ra) from the all selected parameters,
Feed Rate was most significantly affecting the Turning of
Aluminum. The result showed that the feed rate contributed
54.65%, cutting speed contributed only 34.67% and depth of cut
contributed was least with 10.47%.
 For surface roughness (Ra) optimum machining parameters are
spindle speed 800 rpm, feed rate 67.5 mm/min and depth of cut
0.50 mm.
For hardness optimum machining parameters are spindle speed
850 rpm, feed rate 67.5 mm/min and depth of cut 0.75 mm.

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