In this paper, Fuzzy-Taguchi Method has been used to identify the optimal combination of influential factors by analyzing the multi responses in the face milling. Milling experiment has been performed on AMMC (Aluminium Metal Matrix Composite), according to Taguchi orthogonal array (L27) for various combinations of influential parameters: speed, feed, depth of cut and coolant. Fuzzy logic is applied for the analysis of experimental response data of vibrations, temperature, surface roughness and resultant forces. The Fuzzy grade is calculated from this data and Fuzzy grade is optimized using Taguchi method in order to get the optimal parameter values, and also influence of parameters on individual responses is studied using Taguchi S/N ratio analysis. This work is useful for analysis of machining parameters in face milling.
Face Milling AMMC Fuzzy Logic Taguchi S/N Ratio Analysis1. Introduction
Conventional materials have the limitations in achieving good combination of strength, stiffness, toughness, density, etc. To overcome these limitations and to meet the ever increasing demand of modern day technology, composites are most promising materials in recent days. Metal matrix composites (MMCs) possess high strength, hardness, toughness, and good thermal resistance properties as compared to unreinforced alloys.
Milling is the process of machining flat, curved or irregular surfaces by feeding the work piece against a rotating cutter containing a number of cutting edges. The literature review related to machining of AMMC is presented in the following.
Shivanand et al. (2004) [1] compared Powder Metallurgy method and stir casting method for producing the AMMC through testing of mechanical properties and concluded that stir casting method was best suitable for preparation of AMMC. Dalalah and Bataineh (2009) [2] presented a fuzzy logic approach to the selection of the best silicon crystal slicing technology. Fuzzy reasoning is used to model the expert’s comprehension and uncertainty in the factors used in the decision criteria. Kuttolamadom et al. (2010) [3] studied the effects of machining feed on surface roughness in milling Al-6061. Increase the feed up until a cut-off surface roughness limit is reached and then increase the speed within the roughness range, to maximize productivity. Yazdi and Khorram (2010) [4] investigated the selection of optimal machining parameters for face milling operations in order to minimize the surface roughness and to maximize the material removal rate using RSM and ANN methods. Abuthakeer et al. (2011) [5] carried out a study to obtain the surface roughness and vibration responses were investigated at various parametric levels and combinations using LabVIEW software. On the completion of the experimental test, ANN is used to validate the results. Gunay et al. (2011) [6] focused on study of machining parameters on the cutting forces and surface roughness during face milling of Ti-6Al-4V alloy with carbide tools under dry conditions. Resultant cutting forces and surface roughness increased with an increase in feed rate, whereas decreasing with increase in cutting speed. Çalışkan et al. (2012) [7] showed the influence process parameters on the cutting forces (Fx, Fy, and Fz) and Ra in hard milling. According to the results of variance analysis, the cutting forces are the most sensitive to feed rate fz and then depth of cut. The cutting speed is only influential on Fx. Globocki Lakica et al. (2013) [8] carried out Experimental Research Using of MQL in Metal Cutting. The analysis shows that turning with MQL is a good alternative for conventional lubrication. Al-Zubaidi et al. (2013) [9] proposed a new multi objective optimization approach in the face milling. It is showed that the method provides a robust way of looking at the optimum parameter selection problems. Jatin (2013) [10] studied the effect of different machining on Surface Roughness in milling by Taguchi analysis. Low cutting speed should be used for long cutter life. High cutting speed and low feeds give best surface finishes. Venkata Ramaiah et al. (2013) [11] made an attempt to obtain optimum machining parameters for minimum cutting forces and cutting temperature by using Fuzzy Logic. It is showed that the method provides good results in machining of Al 6061. Das et al. (2014) [12] investigated the application of traditional Taguchi method with fuzzy logic for multi objective optimization of the machining process of Al-5Cu. Experimental results are demonstrated to present the effectiveness of this approach.
To address the lack of research in this issue, the present work has been done on face milling of AMMC with the following objectives:
1) To study the influence of machining parameters on multi responses;
2) To identify the optimal setting of milling process parameters (coolant, cutting speed, feed rate and depth of cut) for optimal responses: vibrations, temperature, surface roughness and resultant forces.
2. Taguchi Orthogonal Array for Conducting Experiments
In this experiment four process parameters at three levels have been considered are shown in Table 1.
L27 orthogonal array as shown in Table 2 has been chosen for conducting experiments. Experiments are performed according to this design and the values of surface roughness, resultant force, vibrations and temperature are recorded and their Normalized responses and response values are shown in Table 3.
3. Milling of AMMC Material3.1. Experimental Procedure
Step by Step procedure used in the experimental work.
1) Keep the milling machine ready for performing the machining operation;
2) Connect the DAQ system to milling machine;
3) Connect the milling tool dynamometer to the milling machine;
4) Prepare the AMMC work piece sample and fix in machine vice;
5) Fix the milling cutter to an arbor and make machine ready for experiment;
6) Perform milling experiments as per Taguchi design on work piece for various combinations of process control parameters like coolant, spindle speed, feed and depth of cut;
7) Measure surface roughness with the help of a portable stylus-type Talysurf (Taylor Hobson, mitutyo);
8) Measure forces such as thrust force, feed force, cross feed force by using milling tool dynamometer;
9) Measure vibrations by using accelerometer sensor (PCB Accelerometer having Sensitivity 100.5 mV/g) and temperature by using temperature sensor (NI-9211Temperature Module) of LabVIEW based DAQ system.
3.2. Measurement of Responses
Experimental responses: surface roughness, vibrations, temperature and resultant forces are measured for different combinations of influential parameters. The measuring instruments and procedure is presented in the following.
Process parameters and their levels
Sl. No.
Parameters
Level 1
Level 2
Level 3
1
Coolant
Dry
Kerosene
Soluble oil
2
Speed, rpm
900
1120
1400
3
Feed, mm/rev
315
500
800
4
Depth of cut, mm
0.8
1.0
1.2
L<sub>27</sub> orthogonal array
Exp. No.
Process parameters
Coolant
Speed (rpm)
Feed (mm/min)
Depth of cut (mm)
1
Dry
900
315
0.8
2
Dry
900
500
1
3
Dry
900
800
1.2
4
Dry
1120
315
0.8
5
Dry
1120
500
1
6
Dry
1120
800
1.2
7
Dry
1400
315
1
8
Dry
1400
500
1.2
9
Dry
1400
800
0.8
10
Kerosene
900
315
1.2
11
Kerosene
900
500
0.8
12
Kerosene
900
800
1
13
Kerosene
1120
315
1
14
Kerosene
1120
500
1.2
15
Kerosene
1120
800
0.8
16
Kerosene
1400
315
1.2
17
Kerosene
1400
500
0.8
18
Kerosene
1400
800
1
19
Soluble oil
900
315
1
20
Soluble oil
900
500
1.2
21
Soluble oil
900
800
0.8
22
Soluble oil
1120
315
1.2
23
Soluble oil
1120
500
0.8
24
Soluble oil
1120
800
1
25
Soluble oil
1400
315
0.8
26
Soluble oil
1400
500
1
27
Soluble oil
1400
800
1.2
Normalized responses
Exp. No.
Responses
Normalized responses
Resultant force (Kgf)
Vibrations (m/sec2)
Temperature (˚C)
Surface roughness (µm)
Resultant force (Kgf)
Vibrations (m/sec2)
Temperature (˚C)
Surface roughness (µm)
1
7.28
8.29
35.1
0.26
0.84860
0.77143
0.8537
0.93631
2
9.27
8.77
35.3
0.27
0.77821
0.63429
0.8469
0.92994
3
10.82
8.94
37.1
0.53
0.72338
0.58571
0.7857
0.76433
4
3.00
9.24
41.7
0.69
1.00000
0.50000
0.6293
0.66242
5
7.87
9.48
43.9
1.15
0.82773
0.43143
0.5544
0.36943
6
8.06
9.68
50.7
0.84
0.82101
0.374 29
0.3231
0.56688
7
12.88
10.35
56.4
0.59
0.65051
0.18286
0.1293
0.72611
8
16.40
10.57
58.4
0.60
0.52600
0.120 00
0.0612
0.71975
9
8.66
10.69
60.2
0.58
0.79979
0.08571
0.0000
0.73248
10
7.87
8.12
30.8
0.66
0.82773
0.82000
1.0000
0.68153
11
8.77
8.43
31.5
0.52
0.79590
0.73143
0.9762
0.77070
12
11.70
8.62
31.8
0.43
0.69225
0.67714
0.9660
0.82803
13
23.02
8.97
39.2
0.16
0.29183
0.57714
0.7143
1.00000
14
26.70
10.93
41.3
0.36
0.16166
0.01714
0.6429
0.87261
15
24.35
9.67
41.7
0.65
0.24478
0.37714
0.6293
0.68790
16
29.09
10.99
43
0.55
0.07711
0.00000
0.5850
0.75159
17
27.29
9.69
43.7
0.51
0.14079
0.37143
0.5612
0.77707
18
31.27
10.89
44.9
0.24
0.00000
0.02857
0.5204
0.94904
19
4.90
7.87
33.6
1.20
0.93279
0.89143
0.9048
0.33758
20
4.90
8.57
33.9
1.41
0.93279
0.69143
0.8946
0.20382
21
4.90
7.49
35
0.81
0.93279
1.00000
0.8571
0.58599
22
8.49
8.89
34.7
1.34
0.80580
0.60000
0.8673
0.24841
23
7.55
7.65
37
1.09
0.83905
0.95429
0.7891
0.40764
24
8.12
8.27
38.4
1.32
0.81889
0.77714
0.7415
0.26115
25
16.55
9.87
37.5
1.73
0.52069
0.32000
0.7721
0.00000
26
16.03
10.43
39.9
0.36
0.53909
0.16000
0.6905
0.87261
27
16.58
10.99
41.6
0.50
0.51963
0.00000
0.6327
0.78344
3.2.1. Measurement of Surface Roughness
The surface roughness values of the machined surface are measured in order to analyze the surface finish quality. Surface Roughness is measured with the help of Talysurf (Figure 1).
3.2.2. Measurement of Vibrations Using PCB Accelerometer
Spindle vibrations are measured using LabVIEW based DAQ. To measure the vibrations of the spindle, PCB Accelerometer (sensitivity 100.5 mv/g) is placed on spindle as shown in Figure 2.
3.2.3. Measurement of Temperature Using NI-9211 Thermocouple
The temperature at the contact of tool and work piece is measured using LabVIEW software based NI-9211 temperature thermocouple. According to the design of experiments at various conditions Dry, Kerosene and soluble oil (Figure 3).
3.2.4. Measurement of Cutting Force Using Milling Tool Dynamometer
In order to measure the forces of thrust, feed and cross feed force, milling tool dynamometer (Figure 4) the resultant force [7] from these forces calculated.
Talysurf (surface roughness measuring machine)
PCB accelerometer mounted on moving spindle
Thermocouple placed at contact of work piece and tool
Milling tool dynamometer for measuring cutting forces
3.3. Data Normalization
Data Normalization is done on data which has different range and unit in one data sequence may differ from the others. Data preprocessing is also necessary when the directions of the target in the sequences are different.
If the target data value characteristic is “smaller the better”. The original sequence can be normalized using the Equation (1) as follows:
where;. m is the number of experimental data items and n is the number of parameters. Denotes the original sequence, the sequence after the data pre-processing, max the largest value of, min the smallest value of, and is the desired value.
4. Analysis of Multi Responses
Deals with analysis of multi responses data shown in Table 3 and optimization of process parameters in milling of AMMC using Fuzzy logic and Taguchi analysis. And also influence of process parameters on individual responses is studied using Taguchi S/N ratio analysis.
4.1. Determinations of Optimum Process Parameters Using Fuzzy Logic
The experimental data is analyzed using Fuzzy logic to determine optimum process parameters as in the following.
4.1.1. Creation of Membership Functions
Figures 5-8 shows the membership function for vibrations, Temperatures, Surface roughness input values in the process parameter.
Figure 9 shows the membership function selected to defuzzify the output (performance), calculated using the simplifying rules. The rules for process parameter for some rules are shown in Table 4.
Using more than three fuzzy sets would cause an explosion in the number of possible expressions. For the current case study 3 fuzzy sets and 4 inputs are considered. This results in a possible 34 = 81 expressions. The five fuzzy sets used in the performance membership function are “very low”, “low”, “medium”, “high”, and “very high”. Again, the trimf shape is employed to map the fuzzy sets. The use of the centroid defuzzification method
Membership function for resultant force
Membership functions for vibrations
Membership functions for temperature
is recommended as it results in a more smoothly shaped rule surface. In other words, the output performance index is less sensitive to slight variations in input values which occur near the fuzzy set overlaps. After the input and output membership functions are all defined and their fuzzy sets properly configured, the next step is to write the simplifying rules used to transform the input into output. As shown in the next section, this is the most crucial step in creating a fuzzy logic process parameter system.
Membership functions for surface roughness
Membership functions for performance
4.1.2. Evaluation of Fuzzy Grade
Fuzzy grade values are determined from Fuzzy logic using Fuzzy rules (Table 4) and normalizing data (Table 3). By using evaluation function of the MATLAB editor.
The evaluation function is: b = [experimental data]; a = readfis (“File name”), t = evalfis (b, a).
After executing above code, the output of FIS editor is obtained as shown in Table 5. These Fuzzy grade values are used for determining optimum parameter values by applying Taguchi techniques as in the following section.
Rules for process parameter
S. No.
Resultant force
Vibrations
Temperature
Surface roughness
Performance
1
If
Low
And
Low
And
Low
And
Low
Then
Very high
2
If
Low
And
Low
And
Low
And
Medium
Then
High
3
If
Low
And
Low
And
Low
And
High
Then
Medium
4
If
Low
And
Low
And
Medium
And
Low
Then
Very high
5
If
Low
And
Low
And
Medium
And
Medium
Then
High
6
If
Low
And
Low
And
Medium
And
High
Then
Medium
7
If
Low
And
Low
And
High
And
Low
Then
High
8
If
Low
And
Low
And
High
And
Medium
Then
Medium
9
If
Low
And
Low
And
High
And
High
Then
Low
10
If
Low
And
Medium
And
Low
And
Low
Then
Very high
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
78
If
High
And
High
And
Medium
And
High
Then
Very low
79
If
High
And
High
And
High
And
Low
Then
Medium
80
If
High
And
High
And
High
And
Medium
Then
Low
81
If
High
And
High
And
High
And
High
Then
Very low
Fuzzy grade for normalized process parameters
Exp. No.
Input parameters
Fuzzy grade
Resultant force (Kgf)
Vibrations (m/sec2)
Temperature (˚C)
Surface roughness (µm)
1
0.84860
0.77143
0.8537
0.93631
0.312
2
0.77821
0.63429
0.8469
0.92994
0.3091
3
0.72338
0.58571
0.7857
0.76433
0.3424
4
1.00000
0.50000
0.6293
0.66242
0.3988
5
0.82773
0.43143
0.5544
0.36943
0.541
6
0.82101
0.37429
0.3231
0.56688
0.4524
7
0.65051
0.18286
0.1293
0.72611
0.416
8
0.52600
0.12000
0.0612
0.71975
0.4777
9
0.79979
0.08571
0.0000
0.73248
0.5
10
0.82773
0.82000
1.0000
0.68153
0.3263
11
0.79590
0.73143
0.9762
0.77070
0.3286
12
0.69225
0.67714
0.9660
0.82803
0.3252
13
0.29183
0.57714
0.7143
1.00000
0.3893
14
0.16166
0.01714
0.6429
0.87261
0.4943
15
0.24478
0.37714
0.6293
0.68790
0.473
16
0.07711
0.00000
0.5850
0.75159
0.5686
17
0.14079
0.37143
0.5612
0.77707
0.447
18
0.00000
0.02857
0.5204
0.94904
0.5144
19
0.93279
0.89143
0.9048
0.33758
0.4091
20
0.93279
0.69143
0.8946
0.20382
0.4605
21
0.93279
1.00000
0.8571
0.58599
0.2908
22
0.80580
0.60000
0.8673
0.24841
0.4837
23
0.83905
0.95429
0.7891
0.40764
0.4157
24
0.81889
0.77714
0.7415
0.26115
0.4906
25
0.52069
0.32000
0.7721
0.00000
0.734
26
0.53909
0.16000
0.6905
0.87261
0.3976
27
0.51963
0.00000
0.6327
0.78344
0.4159
4.2. Taguchi Analysis
Taguchi S/N ratio analysis is performed on Fuzzy grade data shown in Table 6 using Minitab software and optimum parameter values are found (Table 7)and the main effects plot is shown in Figure 10.
From the results (Table 7 and Figure 11), optimum process parameters combination for Fuzzy grade is
Speed 3-Coolant 3-Depth of cut 3-Feed 1
Which means
Speed at level 3 (1400 rpm)
Factors and fuzzy grade for process parameters
S. No.
Coolant
Speed (rpm)
Feed (mm/min)
Depth of cut (mm)
Fuzzy grade
1
Dry
900
315
0.8
0.312
2
Dry
900
500
1
0.3091
3
Dry
900
800
1.2
0.3424
4
Dry
1120
315
0.8
0.3988
5
Dry
1120
500
1
0.541
6
Dry
1120
800
1.2
0.4524
7
Dry
1400
315
1
0.416
8
Dry
1400
500
1.2
0.4777
9
Dry
1400
800
0.8
0.5
10
Kerosene
900
315
1.2
0.3263
11
Kerosene
900
500
0.8
0.3286
12
Kerosene
900
800
1
0.3252
13
Kerosene
1120
315
1
0.3893
14
Kerosene
1120
500
1.2
0.4943
15
Kerosene
1120
800
0.8
0.473
16
Kerosene
1400
315
1.2
0.5686
17
Kerosene
1400
500
0.8
0.447
18
Kerosene
1400
800
1
0.5144
19
Soluble oil
900
315
1
0.4091
20
Soluble oil
900
500
1.2
0.4605
21
Soluble oil
900
800
0.8
0.2908
22
Soluble oil
1120
315
1.2
0.4837
23
Soluble oil
1120
500
0.8
0.4157
24
Soluble oil
1120
800
1
0.4906
25
Soluble oil
1400
315
0.8
0.734
26
Soluble oil
1400
500
1
0.3976
27
Soluble oil
1400
800
1.2
0.4159
Rank of process parameters for fuzzy grade
Level
Coolant
Speed
Feed
Depth of cut
1
7.766
9.334
7.256
7.592
2
7.516
6.794
7.456
7.649
3
7.074
6.227
7.643
7.115
Delta
0.692
3.106
0.387
0.534
Rank
2
1
4
3
Coolant at level 3 (Soluble oil)
Depth of cut at level 3 (1.2 mm)
Feed at level 1 (315 mm/rev)
4.3. Individual Response Analysis
Taguchi S/N ratio analysis is applied for data shown in Table 5 to study the influence of process parameters on individual responses. The results are shown in Figures 11-14 and Tables 8-11.
4.3.1. S/N for Vibration versus Coolant, Speed, Feed and Depth of Cut
From Figure 11 and Table 8, the optimum process parameters combination for individual response (Vibration) is
Speed 3-Depth of cut 3-Coolant1, 2 -Feed 3
Which means
Speed at level 3 (1400 rpm)
Depth of cut at level 3 (1.2 mm)
Coolant at level 1, 2 (Dry, Kerosene)
Feed at level 3 (800 mm/rev)
Main effects plot for vibration
Main effects plot for fuzzy grade
Main effects plot for temperature
Main effects plot for resultant force
Main effects plot for surface roughness
Rank of process parameters for vibration
Level
Coolant
Speed
Feed
Depth of cut
1
−19.58
−18.42
−19.20
−19.03
2
−19.58
−19.23
−19.40
−19.32
3
−18.90
−20.41
−19.46
−19.72
Delta
0.68
2.00
0.26
0.69
Rank
3
1
4
2
Rank of process parameters for temperature
Level
Coolant
Speed
Feed
Depth of cut
1
−33.17
−30.56
−31.72
−31.98
2
−31.66
−32.20
−32.02
−32.00
3
−31.31
−33.37
−32.39
−32.15
Delta
1.87
2.81
0.67
0.17
Rank
2
1
3
4
Rank of process parameters for resultant force
Level
Coolant
Speed
Feed
Depth of cut
1
−18.68
−17.42
−20.02
−19.62
2
−25.50
−20.38
−21.46
−21.56
3
−18.73
−25.11
−21.44
−21.73
Delta
6.83
7.69
1.44
2.11
Rank
2
1
4
3
Rank of process parameters for surface roughness
Level
Coolant
Speed
Feed
Depth of cut
1
5.0958
7.7189
3.9266
3.4718
2
7.6211
2.9283
4.4599
6.1405
3
0.1882
5.2580
4.5187
3.2929
Delta
7.4329
2.3297
0.5921
2.8476
Rank
1
3
4
2
4.3.2. S/N for Temperature versus Coolant, Speed, Feed and Depth of Cut
From Figure 12 and Table 9, the optimum process parameters combination for individual response (Temperature)
Speed3-Coolant1-Feed3 -Depth of cut3
Which means
Speed at level 3 (1400 rpm)
Coolant at level 1 (Dry)
Feed at level 3 (800 mm/rev)
Depth of cut at level 3 (1.2 mm)
4.3.3. S/N for Resultant Force versus Coolant, Speed, Feed and Depth of Cut
From Figure 13 and Table 10, the optimum process parameters combination for individual response (Resultant force) is
Speed3- Coolant2-- Feed2-Depth of cut3
Which means
Speed at level 3 (1400 rpm)
Coolant at level 2 (Kerosene)
Feed at level 2 (500 mm/rev)
Depth of cut at level 3 (1.2 mm)
4.3.4. S/N for Surface Roughness versus Coolant, Speed, Feed and Depth of Cut
From Figure 14 and Table 11, the optimum process parameters combination for individual response (Surface roughness) is
Coolant3- Depth of cut3 -Speed2- Feed1
Which means
Coolant at level 3 (Soluble oil)
Depth of cut at level 3 (1.2 mm)
Speed at level 2 (1120 rpm)
Feed at level 1 (315 mm/rev)
4.4. Conformation Test Results
Conformation experiment is conducted for optimum parameter combination and the values of Vibrations (shown in Figure 15 and Figure 16), Temperature (shown in Figure 17 and Figure 18), surface roughness, and resultant forces are recorded (Table 12).
According to Fuzzy based Taguchi S/N ratio analysis, the optimal combination of input parameters is Coolant = Soluble oil
Speed = 1400 rpm
Depth of cut = 1.2 mm
Feed = 315 mm/rev
Lab VIEW front panel of acceleration(output)
Lab VIEW block diagram of acceleration
Lab VIEW front panel of temperature(output)
Lab VIEW block diagram of temperature
Experimental results
Coolant
Speed (rpm)
Feed (mm/min)
Depth of cut (mm)
Resultant force (Kgf)
Vibrations (m/sec2)
Temperature (˚C)
Surface roughness (µm)
Soluble oil
1400
315
1.2
19.28
10.95
38.6
0.52
5. Conclusions
The influence of machining parameters on the multi responses is studied and the following conclusions are drawn from the results.
1) The order of influenced parameters found from Fuzzy-Taguchi analysis is as follows:
・ Speed (most influential);
・ Coolant (moderately influential);
・ Depth of cut (least influential);
・ Feed (very least influential).
2) Taguchi analysis shows that speed has more influence on vibrations, forces and temperature and that coolant has more influence on surface roughness.
3) Confirmation test has been conducted and results are satisfactory.
However, this work can be extended further by considering the followings:
・ Accuracy of predictions will be enhanced by generating more experimental data for training;
・ Tools with coated materials like Titanium, diamond, etc., are to be used in order to get the best results.
・ Use of CNC machines is for automatic adjustments of parameter values.
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