NIRAJAN PUDASAINI 2VX12MPD15 A Project on: PROF. R R MALAGI PROJECT GUIDE
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- Slide 1
- NIRAJAN PUDASAINI 2VX12MPD15 A Project on: PROF. R R MALAGI
PROJECT GUIDE
- Slide 2
- Contents Abstract Vertical milling About SG iron Input
parameters Response variables Objective Literature review Problem
statement About DOE RSM Box- Behnken design Design matrix Sequence
of operation Manufacture of workpiece Machining Measurement of SR
Observation table Results Conclusion Future works
- Slide 3
- Abstract This report presents an approach to predicting the
surface roughness and material removal rate in milling of
spheroidal graphite iron using tungsten carbide insert tool and its
optimization by coupling the prediction model with response surface
methodology. In this work, experiments are carried out as per the
Box- Behnken design and an L13 orthogonal array is used to study
the influence of various combinations of process parameters on SR
and MRR. ANOVA test is conducted to determine the significance of
each process parameter. Two sets of L13 OA are used each for tool
orientation of 45 and 90 degrees. This work may be useful in
selecting optimum values of various process parameters that would
maximize the MRR and minimize the SR in machining.
- Slide 4
- Vertical Milling Machining Centers classified as: Vertical
Machining centers Horizontal Machining centers Universal Machining
centers VMC has spindle on vertical axis relative to work table
Used for flat works that require tool access from top For e.g.:
Mould and die cavities, large aircraft components Figure: A
Vertical Milling Machine
- Slide 5
- Spheroidal Graphite Iron Also called ductile iron Characterized
by graphite occurred in microscopic spheroids Various grades,
differed due to matrix (microstructure of metal around the
graphite) IndiaIS 1865 SG37 0/17 SG40 0/12 SG5 00/7 SG6 00/3 SG7
00/2 SG8 00/2 ISOISO 1083 400-15450-10500-7600-3700-2800-2900-
2
- Slide 6
- Cutting parameters
- Slide 7
- Response variables 1. Surface roughness It is a measure of the
level of unevenness of the part's surface. Measurement procedure
Surface inspection by comparison method Direct instrument method
Parameters Ra = arithmetic mean of departures of profile from mean
line Rq, Ry, Rz, Sm are other parameters
- Slide 8
- 2. Material removal rate It is the volume of material removed
divided by the machining time. MRR can be expressed as the ratio of
the difference between the weight of the work piece before and
after machining to the machining time. MRR= (Wb-Wa)/t Where Wb =
Weight of work piece before machining. Wa = Weight of work piece
after machining. t = Machining time
- Slide 9
- Objective The objective of this study is to find out the
optimum levels for the process parameters so that the surface
roughness value will be minimum and rate of material removal will
be maximum in a vertical machining center and to check the
optimality by developing empirical models.
- Slide 10
- Literature review Pratyusha J et al. made a study for finding
out optimum parameters for milling process using Taguchi methods.
L9 array was used, parameters studied were speed, feed and depth of
cut. They found that Taguchi method provides a systematic and
efficient methodology for searching optimal milling parameters. R.
Suresh et al. made an attempt to analyse the influence of cutting
speed, feed rate, DOC and machining time on machinability
characteristics like SR and tool wear using RSM. The found that
combination of low feed rate, low depth of cut and low machining
time with high cutting speed is beneficial for minimizing the
machining force and surface roughness
- Slide 11
- Balinder singh et al. carried out experiments for optimization
of input parameters in the CNC milling on EN 24 steel. Taguchi
technique used SR and MRR were response variables, speed, feed and
DOC were control parameters L27 array was used generated from
MINITAB V15 Confirmation runs was used to verify the experiment
Other research on VMC using Taguchi technique were done by Piyush
Pandey et al., Avinash A. Thakre, Reddy Sreenivalsu and so on.
Milon D. Selvam et al., R. Jalili Saffar et al. used GA
method.
- Slide 12
- Ahmad Hamdan et al. carried out experiments for high speed
machining of stainless steel using L9 array and Taguchi method.
Results showed a reduction of 25.5% in cutting forces and 41.3% in
SR improvement. Norfadzlan Yusup et al. made a comparison of five
year researches from 2007 to 2007 that used evolutionary techniques
to optimize machining process parameters. They found that SR is
mostly studied with GA. A Kacal and M Gulesin studied optimal
cutting condition in finish turning of of ductile iron using
Taguchi method. ANOVA was used to identify significant factors
affecting SR. They found that feed rate is most significant.
- Slide 13
- Problem statement In machining operation, the quality of
surface finish and the rate of material removal are important
requirements. The choice of optimized cutting parameters is very
important for controlling the required surface quality and
obtaining the maximum MRR. In this study, the optimum machining
parameters, for vertical milling of SG iron, are to be determined
to increase MRR and reduce the SR.
- Slide 14
- DOE technique Statistical design of experiments refers to the
process of planning the experiments so that appropriate data that
can be analysed by statistical methods will be collected, resulting
in valid and objective conclusions.
- Slide 15
- Guidelines for designing an experiment 1. Recognition of and
statement of the problem 2. Choice of factors, levels and ranges*
3. Selection of response variable* 4. Choice of experimental design
5. Performing the experiment 6. Statistical Analysis of the data 7.
Conclusions and recommendations *In practice, step 2 and 3 are
often done simultaneously or in reverse order
- Slide 16
- Response surface methodology It is a collection of mathematical
and statistical techniques useful for the modeling and analysis of
problems in which a response of interest is influenced by several
variables and the objective is to optimize this response. In
Statistics, RSM explores relationship between several explanatory
variables and one or more response variables. Idea is to use a
sequence of designed experiments to obtain an optimal solution.
Estimate first-degree polynomial by factorial experiments Explains
which explanatory variables have an impact on response variable of
interest. By Box-behnken method, 2 nd degree polynomial model is
estimated. This second degree polynomial can be used to
optimize.
- Slide 17
- Box-Behnken design A useful method for developing second-order
response surface models Based on the construction of balanced
incomplete block designs and requires at least three levels for
each factor. Requires only three levels to run an experiment. It is
a special 3-level design because it does not contain any points at
the vertices of the experiment region.
- Slide 18
- Geometric representation Number of trails and corresponding
level
- Slide 19
- Design matrix Input parameters with levels Factors/LevelsLevel
-1 (low)Level 0 (medium)Level 1 (high) Speed (rpm)8009001000 Feed
(mm/rev)300400500 Depth of Cut (mm) 0.51.01.5
- Slide 20
- Design matrix for 45 degree tool orientation Std Order RunOrd
erPtTypeBlocks Feed (mm/rev) Speed (rpm) Depth of cut (mm)
81215009001.5 12213008001 432150010001 24215008001 115214008001.5
136014009001 372130010001 1282140010001.5 99214008000.5
510213009000.5 10112140010000.5 612215009000.5 713213009001.5
- Slide 21
- Design matrix for 90 degree tool orientation Std Order RunOrd
erPtTypeBlocks Feed (mm/rev) Speed (rpm) Depth of cut (mm)
81215009001.5 12213008001 432150010001 24215008001 115214008001.5
136014009001 372130010001 1282140010001.5 99214008000.5
510213009000.5 10112140010000.5 612215009000.5 713213009001.5
- Slide 22
- Sequence of operation 1. Manufacture of workpieces (26 pieces)
from casting 2. Measurement of initial weight 3. Machining in the
Vertical Machining Center (MCV-1000) 4. Measurement of Surface
Roughness 5. Measurement of final weight 6. Calculation of MRR and
Surface Roughness for each trials 7. Analysis using MINITAB V16 8.
Optimization of the responses
- Slide 23
- Manufacture of workpieces Using a sand casting method. 26 sets
of workpieces were manufactured Made up of SG Iron
- Slide 24
- Machining Performed at a vertical machining center, Shradha
enterprises, Udyambag, Belgaum The machine used is TAKUMI MCV-1000
model Takumi MCV- 1000
- Slide 25
- Machining procedure Clamping of workpiece Milling cutter
Machining with coolant
- Slide 26
- Machining outputs Run Order (45 degree) Initial weight (kg)
Final weight (kg) Time (min) Run Order (90 degree) Initial weight
(kg) Final weight (kg) Time (min) 10.9180.8480.3510.90020.8320.35
20.87050.8480.4520.92490.9080.4 30.9550.8920.3530.94810.8820.35
40.95810.8740.4540.92160.8580.4 50.89510.8620.450.86050.8220.35
60.8690.8380.3560.92260.8880.4 70.90000.860.470.86690.8260.45
80.92730.8580.3580.93950.8660.35 90.93770.8580.4590.92850.8520.45
100.85220.8320.45100.8560.8380.45 110.8580.8420.4110.87660.8600.45
120.9340.870.4120.9140.8520.4 130.9120.8920.4130.90280.8440.35
- Slide 27
- Measurement of Surface roughness Surface roughness measurement
is done using the Surtronic 3+ device available at metrology
laboratory of Gogte Institute of Technolgy, Belgaum.
- Slide 28
- Observation table Observation table for 45 degree tool
orientation Std. Order Run Order Pt. TypeBlocks Feed (mm/rev) Speed
(rpm) DOC (mm) MRR (kg/min) SR (microns) 81215009001.50.24.46
122130080010.056.85 4321500100010.184.87 242150080010.1875.28
115214008001.50.08286.56 1360140090010.095.7 3721300100010.14.89
1282140010001.50.1984.57 99214008000.50.177147.2
510213009000.50.0455.29 10112140010000.50.045.22
612215009000.50.165.2 713213009001.50.055.67
- Slide 29
- Observation table for 90 degree tool orientation Std. Orde r
Run Order Pt. TypeBlocks Feed (mm/rev) Speed (rpm) DOC (mm) MRR
(kg/min) SR (microns) 81215009001.50.1954.4 122130080010.04236.87
4321500100010.1894.66 242150080010.1595.43 115214008001.50.116.46
1360140090010.08655.51 3721300100010.0914.59
1282140010001.50.214.88 99214008000.50.176.99
510213009000.50.045.29 10112140010000.50.0374.22
612215009000.50.1554.55 713213009001.50.1685.77
- Slide 30
- Results and Discussions To find out which factors among the
speed, feed and DOC is significant in increasing MRR and reducing
the SR and at what level Response surface analysis using MINITAB
v16 ANOVA to check adequacy of model Confidence interval = 85 %
Only terms whose p < 0.15 is used to develop empirical model
Analysis done using coded units, so, empirical equation generated
are expressed in coded units
- Slide 31
- Analysis of response for 45 degree tool orientation Regression
analysis for MRR TermsCoeff.SE Coeff.T testP value
Constant0.090.0169185.3200.013 Feed0.0602500.00598110.0730.002*
Speed0.0026330.0059810.4400.690 Depth of
cut0.0135830.0059812.2710.108* Feed *
Feed0.0142570.0111901.2740.292 Speed
*Speed0.0249930.0111902.2330.112* DOC *
DOC0.0094930.0111900.8480.459 Feed *
speed-0.014250.008459-1.6850.191 Feed * depth of
cut0.0087500.0084591.0340.377 Speed *
DOC0.0630850.0084597.4580.005*
- Slide 32
- Analysis of Variance for MRR SourceDFSeq SSAdj SSAdj MSFP
Feed10.029041 101.46 0.002* Speed10.000055 0.19 0.69 Depth of
cut10.001476 5.16 0.108* Feed*Feed10.0000470.000465 1.62 0.292
Speed*Speed10.0012260.001428 4.99 0.112* DOC*DOC10.000206 0.72
0.459 Feed*Speed10.000812 2.84 0.191 Feed*DOC10.000306 1.07 0.377
Speed*DOC10.015919 55.62 0.005* Residual error30.000859 0.000286
Total120.49947
- Slide 33
- Empirical model for MRR MRR = 0.090 + 0.060250* feed +0.013583*
DOC+ 0.024993* speed* speed + 0.063085* speed * DOC
- Slide 34
- Main effect plot for MRR at 45 degree insert orientation
- Slide 35
- Interaction effect
- Slide 36
- Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of
cut (mm) 500 (level 1) 1000 (level 1) 1.5 (Level 1)
- Slide 37
- Regression analysis for surface roughness for insert at 45
degree TermsCoeff.SE Coeff.T testP value
Constant5.70.382414.9070.001 Feed-0.361250.1352-2.6720.076*
Speed-0.79250.1352-5.8620.010* Depth of
cut-0.206250.1352-1.5260.225 Feed * Feed-0.480.2529-1.8980.154
Speed *Speed0.25250.25290.9980.392 DOC * DOC-0.0650.2519-0.2570.814
Feed * speed0.38750.19122.0270.136* Feed * depth of
cut-0.280.1912-1.4650.239 Speed * DOC-0.00250.1912-0.0130.99
- Slide 38
- Analysis of Variance for SR SourceDFSeq SSAdj SSAdj MSFP
Feed11.04401 7.140.076 Speed15.02455 34.370.01 Depth of cut10.34031
2.330.225 Feed*Feed10.88481 3.60.154 Speed*Speed10.22008 10.392
DOC*DOC10.00966 0.070.814 Feed*Speed10.60062 4.110.136
Feed*DOC10.3136 2.140.239 Speed*DOC10.00002 00.99 Residual
error30.43862 0.14621 Total128.87620
- Slide 39
- Empirical model for SR SR = 5.7 0.36125 * feed 0.7925 * speed
+0.38750 *feed* speed
- Slide 40
- Main effect plot for SR at 45 degree insert orientation
- Slide 41
- Interaction effect
- Slide 42
- Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of
cut (mm) 500 (level 1) 1000 (level 1) 1.5 (Level 1)
- Slide 43
- Analysis of response for 90 degree tool orientation Regression
analysis for MRR TermsCoeff.SE Coeff.T testP value
Constant0.08650.029432.9390.061* Feed0.0445880.010414.8250.023*
Speed0.0057120.010410.5490.621 Depth of
cut0.0351250.010413.3760.043* Feed * Feed0.0207880.019471.0680.364
Speed *Speed0.0130370.019470.670.551 DOC *
DOC0.0322120.019471.6550.197 Feed * speed-0.004680.01472-0.3180.772
Feed * depth of cut-0.0220.01472-1.4950.232 Speed *
DOC0.058250.014723.9590.029*
- Slide 44
- Analysis of Variance for MRR SourceDFSeq SSAdj SSAdj MSFP
Feed10.015904 18.360.023 Speed10.000261 0.30.621 Depth of
cut10.00987 11.40.043 Feed*Feed10.000160.000988 1.140.364
Speed*Speed10.0000020.000389 0.450.551 DOC*DOC10.002372 2.740.197
Feed*Speed10.000087 0.10.772 Feed*DOC10.001936 2.240.232
Speed*DOC10.015720.13572 15.670.029 Residual error30.002598
0.000866 Total120.046763
- Slide 45
- Empirical model for MRR MRR = 0.0865 + 0.044588 * feed +
0.03512 * DOC +0.05825 *speed * DOC
- Slide 46
- Main effect plot for MRR at 90 degree insert orientation
- Slide 47
- Interaction effect
- Slide 48
- Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of
cut (mm) 500 (level 1) 1000 (level 1) 1.5 (Level 1)
- Slide 49
- Regression analysis for surface roughness TermsCoeff.SE Coeff.T
testP value Constant5.510.308117.8860 Feed-0.4350.1089-3.9940.028*
Speed-0.9250.1089-8.4930.003* Depth of cut0.05750.10890.5280.634
Feed * Feed-0.378750.2038-1.8590.16 Speed
*Speed0.256250.20381.2580.298 DOC * DOC-0.128750.2038-0.6320.572
Feed * speed0.37750.1542.4510.092* Feed * depth of
cut-0.15750.154-1.0230.382 Speed * DOC0.29750.1541.9310.149*
- Slide 50
- Analysis of Variance for SR SourceDFSeq SSAdj SSAdj MSFP
Feed11.5138 15.950.028 Speed16.845 72.130.003 Depth of cut10.0264
0.026450.280.634 Feed*Feed10.5350.32790.327893.460.16
Speed*Speed10.27160.15010.150091.580.298 DOC*DOC10.0379
0.037890.40.572 Feed*Speed10.57 0.570036.010.092 Feed*DOC10.0992
0.099221.050.382 Speed*DOC10.354 0.354033.730.149 Residual
error30.2847 0.0949 Total1210.5377
- Slide 51
- Empirical model for SR SR = 5.51 0.435 * feed 0.925 *speed +
0.37750* feed* speed + 0.29750* Speed * DOC
- Slide 52
- Main effect plot for SR at 90 degree insert orientation
- Slide 53
- Interaction effect
- Slide 54
- Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of
cut (mm) 500 (level 1) 1000 (level 1) 0.5 (Level -1)
- Slide 55
- Conclusion ANOVA result showed that for both condition of tool
orientation, feed and depth of cut have significant impact on
material removal rate. Interaction of speed and DOC also have
significant impact on it. For surface roughness, feed and speed has
main effect and interaction of feed and speed also has significant
impact for 45 degree tool orientation and for 90 degree orientation
along with above factors a combination of speed and DOC is also
significant.
- Slide 56
- Future works Results obtained can be used as a model for the
selection machining parameters while machining in a VMC in order to
obtain optimum MRR and SR. Also possible to study the effect of
various other parameters like number of passes, tool diameter,
austempering temperature of spheroidal graphite iron and so on.
Another possibility is the use of collected data and results in
order to find the signal to noise ratio by using the Taguchi
method.
- Slide 57
- THANK YOU