Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Dr. S. Mahendran, Dr. S. Sukumar
DOI Link: https://doi.org/10.22214/ijraset.2024.62990
Certificate: View Certificate
To aim for the highest quality product quality and a high metal removal rate, metal cutting is crucial. The primary obstacle to attaining optimal quality and high production is the short tool life. The hybrid aluminum metal matrix AA 6082, with reinforcement consisting of titanium dioxide (7.5%) and cenosphere (2.5%), was optimized using the Modified Taguchi optimization method for simultaneous minimization and maximization of surface roughness (Ra), machining time, and material removal rate. The surface finish was affected by different types of geometrical precision values of the CNC drilling process parameters in order to both minimize and maximize the responses. The experiments, including call for the machining of hybrid AMMC and the use of an HSS drill bit, will be performed using a CNC with different process parameters. The L16 orthogonal array will be used for the examinations, and each experiment will be run under a distinct set of circumstances, including feed, speed, and pecking. Using the TAGUCHI design, the optimal geometrical and machining parameters were determined from the experimental output responses, and significant influences were discovered for the corresponding specific output responses.
I. INTRODUCTION OF MACHINING TECHNIQUES
One of the fundamental machining techniques for creating holes is drilling, which is mostly used in the manufacturing sectors such as the aerospace, medical, and automotive industries. Drilling is particularly important in sectors where mechanical fastener assembly is involved. According to reports, around 55,000 holes are bored in an AIR BUS A350 aircraft throughout its single unit manufacture (3). Metal drilling is becoming more and more necessary in order to produce smaller, more highly functioning goods.
II. INTRODUCTION OF MATRIX AA 6082
AL 6082 has a smooth exterior that is very resistant to corrosion, is well suited for welding, and is simple to anodize. An alloy with medium strength and exceptional resistance to corrosion is aluminum 6082. Of the alloys in the 6000 class, it possesses the highest strength. One term for alloy 6082 is structural alloy. The most popular alloy for machining in plate form is 6082. Despite being a relatively new alloy, 6082 has replaced 6061 in many applications due to its better strength. A significant amount of manganese is added to the alloy to control the grain structure, making the alloy stronger.
A. Hybrid Metal Matrix
Materials created by fusing two or more distinct types of fibers together inside a single matrix are known as hybrid composites. Titanium dioxide and cenosphere have been employed as reinforcing particles in this study project.
B. Titanium DI Oxide (TiO2)
Titanium dioxide, also known as titanium oxide or titania, it is the naturally occurring oxide of titanium, chemical formula TiO2. When used as a pigment, it is called titanium white, Pigment White, or CI 77891. The melting point and density of titanium dioxide is 1843°C and 4.23 g/cm3. Titanium dioxide has an elastic modulus of 228 GPa and tensile strength of 367 Mpa. The grain size of titanium dioxide is 25 μm.
C. Cenosphere
Boilers that burn coal are widely used to generate steam, which is used to generate power. A typical boiler involves injecting pulverized coal into a furnace. The process of burning coal in a boiler produces fly ash. A portion of the enhanced fly ash can be used commercially for a variety of uses, including reinforced plastics, lightweight aggregate, pavement foundation materials, cement manufacturing, stabilization of sewage sludge, and concrete and related goods. The residual fly ash must usually be disposed of by land filling since it has hardly any commercial value. Depending on the composition of coal the composition of fly ash varies. Consequently, material requirements have been devised for this waste material to be employed in particular applications. The specific demonstrates that the majority of these waste particles are composed of silicon dioxide, iron oxide, and aluminum oxide. There are various kinds of architectures seen in the fly ash particle. There are some powerful particles in fly ash. Fly ash contains various empty particles known as cenospheres..
III. WORK PIECE PREPARATION THROUGH CASTING PROCESS
Crucible casting is used for the preparation of the hybrid aluminum metal matrix composite work material Titanium dii oxide7.5%, cenosphere 2.5% and Mg 1% of average particle size 120 mesh and 140 mesh respectively are used as the reinforcements for casting. The cast specimen of 30 mm diameter and 300 mm length after casting it had been cutting using power hacksaw in to circular plates of 15 mm thickness of 16 numbers.
A. Drilling Process
Drilling is a cutting process that uses a drill bit to cut or enlarge a hole of circular cross-section in solid materials. The drill bit is a rotary cutting tool, often multipoint. The bit is pressed against the work piece and rotated at rates from hundreds to thousands of revolutions per minute. This forces the cutting edge against the work piece, cutting off chips (swarf) from the hole as it is drilled.
B. High Speed Steel End Mill Bit (HSS)
Te reason HSS tools got their moniker is that they were designed to cut faster. The highest alloyed tool steels are HSS, which were developed in 1900. First developed, the tungsten (T series) usually has 12–18% tungsten, together with 4% chromium and 1%–5% vanadium.
C. CNC Turning Machine Set Up
IV. DESIGN OF EXPERIMENT (DOE)
When it comes to cutting down on the cycle time needed to develop new products or processes, experiment design is a potent tool for enhancing process performance or product design. A design experiment is a test, or set of tests, in which a process's input variable (parameter) is changed in order to observe and detect changes in the output response that correlate to those changes. The process outcome is examined to determine the ideal value or parameters that have the biggest impact on the process. The objectives of the experiment could be as follows
A. Analysis Of Variance (ANNOVA)
In social science research, the Analysis of Variance (ANOVA) is a potent and often used statistical technique. It is an application to determine the impact of certain elements. ANOVA is a group of statistical models and related techniques in statistics that divide the observed variance into components because of various explanatory variables.
B. Taguchi Technique
Essentially, the first fishermen created experimental design techniques. However, the procedures for designing experiments are too complicated and difficult to use. Additionally, as the number of process parameters rises, more tests must be conducted. To address this issue, the Taguchi technique makes use of a unique orthogonal array design to examine the whole parameter space using a limited number of experiments. After that, the experimental data are converted into a signal-to-noise (S/N) ratio in order to quantify the quality attributes that deviate from the intended values. When analyzing the S/N ratio, three kinds of quality attributes are often considered: the lower the better, the higher the better, and the nominally better.
V. DESIGN OF EXPERIMENT
Table 1 Process parameters and their levels of drilling process
Sl.no |
SPEED |
FEED |
PECKING |
EPA |
1 |
500 |
0.02 |
2 |
55 |
2 |
600 |
0.04 |
3 |
75 |
3 |
700 |
0.06 |
4 |
95 |
4 |
800 |
0.08 |
5 |
118 |
A. An Orthogonal Array L16 Formation (INTERACTION)
Table 2 An orthogonal array L16 formation (interaction) .
NO |
SPINDLE SPEED (N) (RPM) |
FEED ( F ) (mm/rev) |
PECKING mm |
EPA |
1 |
500 |
0.02 |
2 |
55 |
2 |
500 |
0.04 |
3 |
75 |
3 |
500 |
0.06 |
4 |
95 |
4 |
500 |
0.08 |
5 |
118 |
5 |
600 |
0.02 |
3 |
95 |
6 |
600 |
0.04 |
2 |
118 |
7 |
600 |
0.06 |
5 |
55 |
8 |
600 |
0.08 |
4 |
75 |
9 |
700 |
0.02 |
4 |
118 |
10 |
700 |
0.04 |
5 |
95 |
11 |
700 |
0.06 |
2 |
75 |
12 |
700 |
0.08 |
3 |
55 |
13 |
800 |
0.02 |
5 |
75 |
14 |
800 |
0.04 |
4 |
55 |
15 |
800 |
0.06 |
3 |
118 |
16 |
800 |
0.08 |
2 |
95 |
B. Experimental Data Analysis And Optimization
Table 3 Experimental data and output response analysis
EXP |
RA |
DIA ERR |
RE ERR |
CYL ERR |
MT |
1 |
0.883 |
8.242 |
0.009 |
0.018 |
137 |
2 |
1.497 |
8.226 |
0.008 |
0.019 |
74 |
3 |
2.009 |
8.160 |
0.008 |
0.016 |
53 |
4 |
2.056 |
8.114 |
0.008 |
0.018 |
42 |
5 |
1.516 |
8.185 |
0.006 |
0.015 |
116 |
6 |
4.246 |
8.126 |
0.009 |
0.017 |
63 |
7 |
2.263 |
8.171 |
0.008 |
0.019 |
45 |
8 |
2.714 |
8.165 |
0.009 |
0.019 |
37 |
9 |
1.969 |
8.104 |
0.011 |
0.020 |
101 |
10 |
2.498 |
8.156 |
0.008 |
0.017 |
55 |
11 |
2.294 |
8.183 |
0.010 |
0.019 |
41 |
12 |
1.578 |
8.156 |
0.011 |
0.020 |
33 |
13 |
2.445 |
8.253 |
0.009 |
0.017 |
89 |
14 |
1.731 |
8.256 |
0.008 |
0.019 |
50 |
15 |
3.771 |
8.096 |
0.010 |
0.020 |
37 |
16 |
1.690 |
8.151 |
0.008 |
0.019 |
30 |
C. Conclusion Of Drilling Responses
By observing the output responses value the roughness average is minimum obtained 0.883 µmm on first parameter specimen and exact geometrical precision is obtained on fifth specimen value of Roundness Error is 0.006mm & Cylindrical error-0.015mm during the drilling operation.
VI. RA (ANALYSIS OF RESULT)
Table: 4 RA and S/N RATIO Values for the Experiments
NO |
SPINDLE SPEED (N) (RPM) |
FEED ( F ) (mm/rev) |
PECKING mm |
EPA° |
RA µm |
SN-RATIO |
1 |
500 |
0.02 |
2 |
55 |
0.883 |
1.0808 |
2 |
500 |
0.04 |
3 |
75 |
1.497 |
-3.5044 |
3 |
500 |
0.06 |
4 |
95 |
2.009 |
-6.0596 |
4 |
500 |
0.08 |
5 |
118 |
2.056 |
-6.2605 |
5 |
600 |
0.02 |
3 |
95 |
1.516 |
-3.6140 |
6 |
600 |
0.04 |
2 |
118 |
4.246 |
-12.5596 |
7 |
600 |
0.06 |
5 |
55 |
2.263 |
-7.0937 |
8 |
600 |
0.08 |
4 |
75 |
2.714 |
-8.6722 |
9 |
700 |
0.02 |
4 |
118 |
1.969 |
-5.8849 |
10 |
700 |
0.04 |
5 |
95 |
2.498 |
-7.9518 |
11 |
700 |
0.06 |
2 |
75 |
2.294 |
-7.2119 |
12 |
700 |
0.08 |
3 |
55 |
1.578 |
-3.9621 |
13 |
800 |
0.02 |
5 |
75 |
2.445 |
-7.7656 |
14 |
800 |
0.04 |
4 |
55 |
1.731 |
-4.7659 |
15 |
800 |
0.06 |
3 |
118 |
3.771 |
-11.5291 |
16 |
800 |
0.08 |
2 |
95 |
1.690 |
-4.5577 |
A. Ra-Responsible & Anova For Each Level Of The Process Parameter
Table: 5 Response Table for Signal to Noise (RA-Smaller is better)
Level |
SPEED |
FEED |
PECKING |
EPA |
1 |
-3.686 |
-4.046 |
-5.812 |
-3.685 |
2 |
-7.985 |
-7.195 |
-5.652 |
-6.789 |
3 |
-6.253 |
-7.974 |
-6.346 |
-5.546 |
4 |
-7.155 |
-5.863 |
-7.268 |
-9.059 |
Delta |
4.299 |
3.928 |
1.615 |
5.373 |
Rank |
2 |
3 |
4 |
1 |
Table: 6 Analysis of Variance for RA
SOURCE |
DF |
SEQ SS |
ADJ MS |
F |
P |
% of contribution |
SPEED |
3 |
2.5546 |
0.85154 |
1.53 |
0.367 |
24 |
FEED |
3 |
2.0661 |
0.68870 |
1.24 |
0.432 |
19 |
PECKING |
3 |
0.1612 |
0.05375 |
0.10 |
0.957 |
1 |
EPA |
3 |
4.3033 |
1.43444 |
2.58 |
0.229 |
40 |
Error |
3 |
1.6687 |
0.55623 |
|
|
16 |
Total |
15 |
10.7540 |
|
|
|
100 |
VII. DIAMETER ERRORS (ANALYSIS OF RESULT)
Table: 7 Diameter Error (Analysis of Result)
NO |
SPINDLE SPEED (N) (RPM) |
FEED ( F ) (mm/rev) |
PECKING mm |
EPA° |
DIA ERR |
SN-RAIO |
1 |
500 |
0.02 |
2 |
55 |
8.242 |
-18.3207 |
2 |
500 |
0.04 |
3 |
75 |
8.226 |
-18.3038 |
3 |
500 |
0.06 |
4 |
95 |
8.160 |
-18.2338 |
4 |
500 |
0.08 |
5 |
118 |
8.114 |
-18.1847 |
5 |
600 |
0.02 |
3 |
95 |
8.185 |
-18.2604 |
6 |
600 |
0.04 |
2 |
118 |
8.126 |
-18.1975 |
7 |
600 |
0.06 |
5 |
55 |
8.171 |
-18.2455 |
8 |
600 |
0.08 |
4 |
75 |
8.165 |
-18.2391 |
9 |
700 |
0.02 |
4 |
118 |
8.104 |
-18.1740 |
10 |
700 |
0.04 |
5 |
95 |
8.156 |
-18.2295 |
11 |
700 |
0.06 |
2 |
75 |
8.183 |
-18.2583 |
12 |
700 |
0.08 |
3 |
55 |
8.156 |
-18.2295 |
13 |
800 |
0.02 |
5 |
75 |
8.253 |
-18.3322 |
14 |
800 |
0.04 |
4 |
55 |
8.256 |
-18.3354 |
15 |
800 |
0.06 |
3 |
118 |
8.096 |
-18.1654 |
16 |
800 |
0.08 |
2 |
95 |
8.151 |
-18.2242 |
A. able: 8 Response Table for Signal to Noise (Diameter Error-Smaller is better)
Level |
SPEED |
FEED |
PECKING |
EPA |
1 |
-18.26 |
-18.27 |
-18.25 |
-18.28 |
2 |
-18.24 |
-18.27 |
-18.24 |
-18.28 |
3 |
-18.22 |
-18.23 |
-18.25 |
-18.24 |
4 |
-18.26 |
-18.22 |
-18.25 |
-18.18 |
Delta |
0.04 |
0.05 |
0.01 |
0.10 |
Rank |
3 |
2 |
4 |
1 |
Table: 9 Analysis of Variance of Circle
SOURCE |
DF |
SEQ SS |
ADJ MS |
F |
P |
%of Contribution |
SPEED |
3 |
0.004282 |
0.001427 |
3.67 |
0.157 |
11 |
FEED |
3 |
0.007866 |
0.002622 |
6.74 |
0.076 |
20 |
PECKING |
3 |
0.000212 |
0.000071 |
0.18 |
0.902 |
1 |
EPA |
3 |
0.025219 |
0.008406 |
21.60 |
0.016 |
65 |
Error |
3 |
0.001168 |
0.000389 |
|
|
3 |
Total |
15 |
0.038746 |
|
|
|
100 |
VIII. ROUNDNESS ERRORS (ANALYSIS OF RESULT)
Table: 10 S/N RATIO Values for the Roundness Error
NO |
SPINDLE SPEED (N) (RPM) |
FEED ( F ) (mm/rev) |
PECKING mm |
EPA° |
ROUNDNESS ERROR |
S-N RATIO |
1 |
500 |
0.02 |
2 |
55 |
0.009 |
40.9151 |
2 |
500 |
0.04 |
3 |
75 |
0.008 |
41.9382 |
3 |
500 |
0.06 |
4 |
95 |
0.008 |
41.9382 |
4 |
500 |
0.08 |
5 |
118 |
0.008 |
41.9382 |
5 |
600 |
0.02 |
3 |
95 |
0.006 |
44.4370 |
6 |
600 |
0.04 |
2 |
118 |
0.009 |
40.9151 |
7 |
600 |
0.06 |
5 |
55 |
0.008 |
41.9382 |
8 |
600 |
0.08 |
4 |
75 |
0.009 |
40.9151 |
9 |
700 |
0.02 |
4 |
118 |
0.011 |
39.1721 |
10 |
700 |
0.04 |
5 |
95 |
0.008 |
41.9382 |
11 |
700 |
0.06 |
2 |
75 |
0.010 |
40.0000 |
12 |
700 |
0.08 |
3 |
55 |
0.011 |
39.1721 |
13 |
800 |
0.02 |
5 |
75 |
0.009 |
40.9151 |
14 |
800 |
0.04 |
4 |
55 |
0.008 |
41.9382 |
15 |
800 |
0.06 |
3 |
118 |
0.010 |
40.0000 |
16 |
800 |
0.08 |
2 |
95 |
0.008 |
41.9382 |
A. Roundness Error Responsible & Anova For Each Level Of The Process Parameter
Table: 11 Response Table for Signal to Noise (-Smaller is better)
Level |
SPEED |
FEED |
PECKING |
EPA |
1 |
41.68 |
41.36 |
40.94 |
40.99 |
2 |
42.05 |
41.68 |
41.39 |
40.94 |
3 |
40.07 |
40.97 |
40.99 |
42.56 |
4 |
41.20 |
40.99 |
41.68 |
40.51 |
Delta |
1.98 |
0.71 |
0.74 |
2.06 |
Rank |
2 |
4 |
3 |
1 |
Table: 12 Analysis of Variance- Roundness
Source |
DF |
Seq SS |
Adj MS |
F |
P |
% of contribution |
SPEED |
3 |
0.000009 |
0.000003 |
2.71 |
0.217 |
37 |
FEED |
3 |
0.000002 |
0.000001 |
0.43 |
0.748 |
8 |
PECKING |
3 |
0.000002 |
0.000000 |
0.43 |
0.748 |
8 |
EPA |
3 |
0.000009 |
0.000003 |
2.57 |
0.229 |
35 |
Error |
3 |
0.000003 |
0.000001 |
|
|
12 |
Total |
15 |
0.000025 |
|
|
|
100 |
IX. CYLINDRICITY ERROR (ANALYSIS OF RESULT)
Table: 13 S/N RATIO Values for the Cylindricity Error
NO |
SPINDLE SPEED (N) (RPM) |
FEED ( F ) (mm/rev) |
PECKING mm |
EPA° |
CYL ERR |
SN-RAIO |
1 |
500 |
0.02 |
2 |
55 |
0.018 |
34.8945 |
2 |
500 |
0.04 |
3 |
75 |
0.019 |
34.4249 |
3 |
500 |
0.06 |
4 |
95 |
0.016 |
35.9176 |
4 |
500 |
0.08 |
5 |
118 |
0.018 |
34.8945 |
5 |
600 |
0.02 |
3 |
95 |
0.015 |
36.4782 |
6 |
600 |
0.04 |
2 |
118 |
0.017 |
35.3910 |
7 |
600 |
0.06 |
5 |
55 |
0.019 |
34.4249 |
8 |
600 |
0.08 |
4 |
75 |
0.019 |
34.4249 |
9 |
700 |
0.02 |
4 |
118 |
0.020 |
33.9794 |
10 |
700 |
0.04 |
5 |
95 |
0.017 |
35.3910 |
11 |
700 |
0.06 |
2 |
75 |
0.019 |
34.4249 |
12 |
700 |
0.08 |
3 |
55 |
0.020 |
33.9794 |
13 |
800 |
0.02 |
5 |
75 |
0.017 |
35.3910 |
14 |
800 |
0.04 |
4 |
55 |
0.019 |
34.4249 |
15 |
800 |
0.06 |
3 |
118 |
0.020 |
33.9794 |
16 |
800 |
0.08 |
2 |
95 |
0.019 |
34.4249 |
A. Cylindricity – Responsible & Anova For Each Level Of The Process Parameter
Table: 14 Response Table for Signal to Noise Cylindricity Error – (Smaller is better)
LEVEL |
SPEED |
FEED |
PECKING |
TOOL |
1 |
35.03 |
35.19 |
34.78 |
34.43 |
2 |
35.18 |
34.91 |
34.72 |
34.67 |
3 |
34.44 |
34.69 |
34.69 |
35.55 |
4 |
34.56 |
34.43 |
35.03 |
34.56 |
Delta |
0.74 |
0.75 |
0.34 |
1.12 |
Rank |
3 |
2 |
4 |
1 |
Table: 15 Analysis of Variance for Cylindricity Error
SOURCE |
DF |
SEQ SS |
ADJ MS |
F |
P |
% of contribution |
SPEED |
3 |
0.000006 |
0.000002 |
0.87 |
0.545 |
18 |
FEED |
3 |
0.000005 |
0.000002 |
0.67 |
0.626 |
15 |
PECKING |
3 |
0.000001 |
0.000000 |
0.20 |
0.890 |
3 |
EPA |
3 |
0.000013 |
0.000004 |
1.67 |
0.343 |
43 |
Error |
3 |
0.000007 |
0.000002 |
|
|
21 |
Total |
15 |
0.000033 |
|
|
|
100 |
X. MACHINING TIME (ANALYSIS OF RESULT)
Table: 16 S/N RATIO Values for the Machining Timing
NO |
SPINDLE SPEED (N) (RPM) |
FEED ( F ) (mm/rev) |
PECKING mm |
TOOL COATED |
MT SEC |
SN-RAIO |
1 |
500 |
0.02 |
2 |
55 |
137 |
-42.7344 |
2 |
500 |
0.04 |
3 |
75 |
74 |
-37.3846 |
3 |
500 |
0.06 |
4 |
95 |
53 |
-34.4855 |
4 |
500 |
0.08 |
5 |
118 |
42 |
-32.4650 |
5 |
600 |
0.02 |
3 |
95 |
116 |
-41.2892 |
6 |
600 |
0.04 |
2 |
118 |
63 |
-35.9868 |
7 |
600 |
0.06 |
5 |
55 |
45 |
-33.0643 |
8 |
600 |
0.08 |
4 |
75 |
37 |
-31.3640 |
9 |
700 |
0.02 |
4 |
118 |
101 |
-40.0864 |
10 |
700 |
0.04 |
5 |
95 |
55 |
-34.8073 |
11 |
700 |
0.06 |
2 |
75 |
41 |
-32.2557 |
12 |
700 |
0.08 |
3 |
55 |
33 |
-30.3703 |
13 |
800 |
0.02 |
5 |
75 |
89 |
-38.9878 |
14 |
800 |
0.04 |
4 |
55 |
50 |
-33.9794 |
15 |
800 |
0.06 |
3 |
118 |
37 |
-31.3640 |
16 |
800 |
0.08 |
2 |
95 |
30 |
-29.5424 |
A. Machining Time Responsible & Anova For Each Level Of The Process Parameter
Table:17 Response Table for Signal to Noise Machining Timing-(Smaller is better)
LEVEL |
SPEED |
FEED |
PECKING |
EPA |
1 |
-36.77 |
-40.77 |
-35.13 |
-35.04 |
2 |
-35.43 |
-35.54 |
-35.10 |
-35.00 |
3 |
-34.38 |
-32.79 |
-34.98 |
-35.03 |
4 |
-33.47 |
-30.94 |
-34.83 |
-34.98 |
Delta |
3.30 |
9.84 |
0.30 |
0.06 |
Rank |
2 |
1 |
3 |
4 |
Table: 18 Analysis of Variance for Machining Timing
SOURCE |
DF |
SEQ SS |
ADJ MS |
F |
P |
% of contribution |
SPEED |
3 |
1397.7 |
465.90 |
14.02 |
0.029 |
9 |
FEED |
3 |
13612.7 |
4537.56 |
136.55 |
0.001 |
88 |
PECKING |
3 |
245.2 |
81.73 |
2.46 |
0.240 |
2 |
EPA |
3 |
92.2 |
30.73 |
0.92 |
0.525 |
1 |
Error |
3 |
99.7 |
33.23 |
|
|
0 |
Total |
15 |
15447.4 |
|
|
|
100 |
A. Taguchi Result The below mentioned taguchi result along with % of contribution found through ANOVA were illustrated. Optimal control factor for drilling process 1) Surface roughness- A2 (RPM -600) B3 (Feed -0.06 mm/Rev) C4 (Pecking-5mm) D1 (EPA55°) 2) Diameter Error- A3 (RPM -700) B2 (Feed -0.04 mm/Rev) C4(PECKING -5mm) D1 (EPA55°) 3) Roundness Error- A2 (RPM -600) B4 (Feed -0.08 mm/Rev) C3 (Pecking-4mm) D1 (EPA55°) 4) Cylindricity Error- A3 (RPM -700) B2 (Feed -0.04 mm/Rev) C4 (Pecking -5mm) D1 (EPA55°) 5) Machining Timing- A2 (RPM -600) B1 (Feed -0.02 mm/Rev) C3 (Pecking-4mm) D4 (EPA118°) B. Percentage Contribution Of Process Parameter 1) Surface roughness –EPA-40% 2) Diameter Error – EPA- 65% 3) Roundness error – Speed -37% 4) Cylindricity Error - EPA -43% 5) Machining Timing - Feed-88% C. Conclusion The objective of this research is to use an HSS drill bit to drill a hybrid AMMC 6082 and determine how drilling settings affect the geometric precision and surface roughness. Pecking, spindle speed, and feed rate were the variables, and geometrical and machining data were the responses. The model for this experiment was developed using the Taguchi Design, and an ANOVA was used to analyze the results. By looking at the output answers value, the first parameter specimen\'s roughness average is found to be as low as 0.883 µmm, and the fifth specimen yields the exact geometrical precision of 0.006 mm for roundness error and 0.015 mm for cylindrical error during the drilling process.
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Copyright © 2024 Dr. S. Mahendran, Dr. S. Sukumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET62990
Publish Date : 2024-05-30
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here