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Effect of process parameters on surface roughness of alloy steels while working on Milling Machine using Taguchi Technique- A Review
1Bombale Ravindra Ramesh1, 2V. L. Kadlag, 3D. R. Mahajan
PG Student, SVIT, Nashik1, Assistant Professor, SVIT, Nashik2, Assistant Professor, LGNSCOE, Nashik3 Email: 1[email protected], 2[email protected], 3[email protected] Abstract— For alloy steel material we need economical and
efficient machining parameters and taguchi robust design method is good method as it reduces the number of experiments. To remain competitive in the today’s competitive market the manufacturers rely on their engineers and production personnel too quickly and effectively set up manufacturing processes for new products to achieve good quality. Due to this surface finish &
dimensional accuracy becomes very important for designer-manufacturer of machine tools, as well as to user.
Furthermore, their prediction helps in the analysis of optimization.
This paper discusses about study of process parameter such as spindle speed, depth of cut, feed rate, tool material, coolant flow on surface roughness of different alloy steel.
Feed rate was found to be dominantly affecting the surface finish.
Keywords: Alloy steel, Machining, Milling machine, Taguchi Method
I. INTRODUCTION
Determination of the optimal cutting parameters (cutting conditions) such as the number of passes, depth of cut, speed, and feed is considered as a crucial stage of milling machining processes and especially in process planning.
This is mainly due to the complex nature of optimization of machining operations that require the following:
(i) Knowledge of machining like drilling, turning or milling.
(ii) Empirical equations relating the tool life, forces, power, surface finish, material removal rate, and arbor deflection to develop realistic constraints.
(iii) Specification of machine tool capabilities like maximum power or maximum feed available from a machine tool.
(iv) Development of an effective optimization criterion like maximum production rate, minimum production cost,
(v) Knowledge of mathematical and numerical optimization techniques, such as the Simplex method, Search method, Geometric programming and dynamic programming.
(vi) Knowledge of stochastic optimization techniques, such as the Genetic Algorithms, Simulated Annealing, Scatter Search, Particle Swarm Optimization and Tribes.
II. MILLING PROCESS
Milling is the process of removing extra material from the work piece with a rotating multi-point cutting tool, called milling cutter. The machine tool employed for milling is called milling machine. Milling machines are basically classified as vertical or horizontal. These machines are also classified as knee-type, ram-type, manufacturing or bed type, and planer-type. Most milling machines have self-contained electric drive motors, coolant systems, variable spindle speeds, and power-operated and table feeds. The three primary factors in any basic milling operation are speed, feed and depth of cut. Other factors such as kind of material and type of tool materials have a large influence, of course, but these three are the ones the operator can change by adjusting the controls, right at the machine vary from one make of controller to the next. [5]
III. SURFACE ROUGHNESS
Surface roughness is an important measure of product quality since it greatly influences the performance of mechanical parts as well as production cost. Surface roughness has received serious attention for many years and it is a key process to assess the quality of a particular product. Surface roughness has an impact on the mechanical properties like fatigue behavior, corrosion resistance, creep life, etc. It also affects other functional attributes of parts like friction, wear, light reflection, heat transmission, lubrication, electrical conductivity, etc.
Surface roughness of turned components has greater influence on the quality of the product.
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Whenever two machined surfaces come in contact with one another the quality of the mating parts plays an important role in the performance and wear of the mating parts. The height, shape, arrangement and direction of these surface irregularities on the work piece depend upon a number of factors such as:
A) The machining variables which include a) Cutting speed,
b) Feed and c) Depth of cut.
B) The tool geometry
Some geometric factors which affect achieved surface roughness include:
a) Nose radius, b) Rake angle,
c) Side cutting edge angle and d) Cutting edge.
C) Work piece and tool material combination and their mechanical properties
D) Quality and type of the machine tool used, E) Auxiliary tooling and lubricant used and
F) Vibrations between the work piece, machine tool and cutting tool. [5]
IV. SURFACE ROUGHNESS TESTER
Surface roughness measurement is measured using a portable stylus type Profilometer. The profilometer is portable self-contained instrument for the measurement of surface texture (Ra). The parameter evaluations are microprocessor based. The measurement results are displayed on an LCD screen and can be output to an optional printer or another computer for further evaluation. The instrument is powered by non-rechargeable alkaline battery (9V). It is equipped with a diamond stylus having a tip radius five micrometers.
Figure 1 Stylus Probe Type Profilometer (Profilometer Measuring Surface Roughness) [5]
V. TAGUCHI METHOD
Taguchi proposed a standard procedure for applying his method for optimizing any process. The steps suggested by Taguchi are,
1. Determine the quality characteristic to be optimized
The first step in the Taguchi method is to determine the quality characteristic to be optimized. The quality characteristic is a parameter whose variation has a critical effect on product quality.
2. Identify the noise factors and test conditions The next step is to identify the noise factors that can have a negative impact on system performance and quality.
3. Identify the control parameters and their alternative levels
The third step is to identify the control parameters thought to have significant effects on the quality characteristic.
Control (test) parameters are those design factors that can be set and maintained. The levels (test values) for each test parameter must be chosen at this point.
4. Design the matrix experiment and define the data analysis procedure
The next step is to design the matrix experiment and define the data analysis procedure. First, the appropriate orthogonal arrays for the noise and control parameters to fit a specific study are selected.
5. Conduct the matrix experiment
The next step is to conduct the matrix experiment and record the results.
6. Analyze the data and determine the optimum levels for control factors
After the experiments have been conducted, the optimal test parameter configuration within the experiment design must be determined. To analyze the results, the Taguchi method uses a statistical measure of performance called signal to noise (S/N) ratio.
7. Predict the performance at these levels
The final step is an experimental confirmation run using the predicted optimum levels for the control parameters being studied. [8]
VI. STUDIES ON IMPROVEMENTS IN SURFACE QUALITY
Pratyusha J., Ashok kumar U. and Laxminarayana P.
(2013) studied the effects of various milling parameters such as spindle speed, feed rate, and depth of cut on the surface roughness of finished components. The experiments were conducted on AISI 304 S.S plate material on vertical milling machine using carbide inserts.
It was observed from Figure 2 that Depth of cut has get major contribution towards variation in surface roughness, next best significant parameters is spindle speed and next best significant parameters is feed rate.
Hence, spindle speed and Depth of cut are significant parameter which must be maintained at the levels specified i.e. Depth of cut at level-2(0.50mm) and Spindle speed at level-3(1500rpm) other parameter can be maintained at any one of the level values specified based on cost consideration. By the experiment results it was found that the surface roughness quality characteristic is smaller the better but the experimental value is 3.00mm.After applying Taguchi techniques the predicted values are 2.82mm.
Figure 2 Percentage Contribution of each factor on Surface Roughness [1]
R. Ashok Raj et. al. (2013) in their work found cutting speed play a dominating role in surface roughness in milling process parameters. Significant values were selected by 5% (α = 0.05) from this main effect interaction plot values of milling parameters predict the low value of surface roughness as indicate at spindle speed of 285 m/min, feed rate 0.27 mm/rev and depth of cut 0.4 mm were the best combinations of this experimental work.
Milling process is best suitable machining of EN8 steel other than conventional machining process such as turning, planning and shaping process. Side and face milling cutter is suitable for machining EN8 steel which produce good surface finish with required accuracy.
A.A.Thakare (2013) conducted experiments on 1040 MS material on CNC vertical milling machine using carbide inserts. The analysis of mean and variance technique is
employed to study the significance of each machining parameter on the surface roughness. The results indicated that coolant flow with the contribution of 60.69% is the most important parameter in controlling the surface roughness, followed by spindle speed. The optimal parameters for surface roughness is obtained as spindle speed of 2500 rpm, feed rate of 800 mm/min, 0.8 mm depth of cut, 30 lit/min coolant flow. For this combination the experimentally found surface roughness value 0.357 μm is lower than other combinations.
Figure3 Contributions of Factors Affecting Roughness [3]
S Nizam Sadiq et al., (2014) found feed rate a dominating and influencing parameter and optimum milling process parameters for achieving lower surface roughness are 1000 rpm of spindle speed, 0.08 mm of feed rate and 0.8 mm depth of cut. However the OHNS steel plates are probable for manufacturing tools and having good machinability property by using TiAlN coated milling cutter with optimum cutting parameters.
G.G.naidu,A.V, A.V, A.V. Vishnu and G.J.raju (2014) in their experimentation found the optimum speed using taguchi technique as 1094rpm. Similarly the results obtained for feed and depth of cut are 100m/min and 1mm respectively. Hence it can be concluded that the parameters obtained are valid and within the range of EN31 machining standards. The corresponding Optimum coolant flow is 90 lts/min.
B.singh et.al.(2014) in their work from all selected parameters, Feed Rate was significantly affecting the milling of EN24. The result showed that the feed rate contributed 87.79%, cutting speed contributed only 1.58% and depth of cut contributed was least with 0.003%
for surface roughness (Ra).
T. Kıvak (2014) found the optimal conditions for surface roughness and flank wear were observed at A2B3C1 (i.e., cutting tool = CVD, cutting speed = 150 m/min and feed rate = 0.09 mm/rev) and A2B1C1 (i.e., cutting tool = CVD, cutting speed = 90 m/min and feed rate = 0.09 mm/rev), respectively. CVD TiCN/Al2O3-coated carbide inserts exhibited better performance than PVD TiAlN-coated carbide inserts and could be recommended for use in the milling of Hadfield steel. According to the
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rate was the most significant parameter for surface roughness with a percentage contribution of 82.38%.
VII. SUMMARY
Paper Steel Alloy
Milling Machine Type
Tool Material
Optimized Parameter values Pratyusha
J, Ashok kumar U.
and Laxminara yana P.
(2013) [1]
AISI 304 S.S.
VMC Milling Machine
Carbide inserts
Depth of cut=
0.50mm, Feed = 100 mm/min and Spindle speed
=1500rpm.
R. Ashok Raj et. al.
(2013) [2]
EN8 Steel
Universal Milling Machine
Side and face milling cutter
Spindle speed=
285m/min, Feed rate= 0.27 mm/rev and Depth of cut=
0.4 mm A.A.Thaka
re (2013) [3]
1040 M.S.
CNC Vertical Milling Machine
Carbide inserts
Spindle speed=
2500 rpm, Feed
rate= 800
mm/min, Depth of cut=0.8 mm, Coolant flow= 30 lit/min.
S Nizam Sadiq et al., (2014) [4]
OHNS - TiAlN
coated milling cutter
Spindle
speed=1000 rpm, feed rate=0.08 mm and Depth of cut =0.8 mm G.G.naidu,
A.V, A.V, A.V.
Vishnu and G.J.raju (2014) [5]
EN31 Steel
End Milling Machine
CVD Brass coated Cutting Tool
Spindle Speed=
1094rpm, Feed rate=100m/min, Depth of cut = 1mm and Coolant flow= 90lts/min.
B.singh et.al.(2014) [6]
EN24 Steel
CNC Vertical Milling
- Spindle
speed=2800 RPM, Feed rate =1200 mm/min) and Depth of cut=
0.6mm T. Kıvak
(2014) [7]
Hadfiel d Steel
CNC Vertical
PVD &
CVD Coated Inserts
Cutting tool = CVD, Cutting speed = 90 m/min and Feed rate = 0.09 mm/rev
Table 1 Summary of Literature Review.
VIII. CONCLUSION
From above study we can conclude that,
This work is rich in terms of literature review.
Milling process is best suitable machining of alloy steels other than conventional machining process such as turning, planning and shaping process.
The spindle speed, feed rate and depth of cut are considered prominent process parameters for machining on milling machine.
Surface roughness is the response variable considered
in the work for machining of many alloy steels.
Researchers claim that feed rate is the most important factor influencing surface roughness in machining of alloy steels.
Apart from feed rate, the other parameters such as coolant flow and tool materials were also found to be significantly affecting surface roughness in respective case studies.
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