J. Inst. Eng. India Ser. C DOI 10.1007/s40032-015-0189-6
ORIGINAL CONTRIBUTION
Surface Roughness Prediction Model using Zirconia Toughened Alumina (ZTA) Turning Inserts: Taguchi Method and Regression Analysis Nilrudra Mandal1 • Biswanath Doloi2 • Biswanath Mondal1
Received: 16 October 2014 / Accepted: 29 April 2015 Ó The Institution of Engineers (India) 2015
Abstract In the present study, an attempt has been made to apply the Taguchi parameter design method and regression analysis for optimizing the cutting conditions on surface finish while machining AISI 4340 steel with the help of the newly developed yttria based Zirconia Toughened Alumina (ZTA) inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Experiments have been carried out based on an orthogonal array L9 with three parameters (cutting speed, depth of cut and feed rate) at three levels (low, medium and high). Based on the mean response and signal to noise ratio (SNR), the best optimal cutting condition has been arrived at A3B1C1 i.e. cutting speed is 420 m/min, depth of cut is 0.5 mm and feed rate is 0.12 m/ min considering the condition smaller is the better approach. Analysis of Variance (ANOVA) is applied to find out the significance and percentage contribution of each parameter. The mathematical model of surface roughness has been developed using regression analysis as a function of the above mentioned independent variables. The predicted values from the developed model and experimental values are found to be very close to each other justifying the significance of the model. A confirmation run has been carried out with 95 % confidence level to verify the optimized result and the values obtained are within the prescribed limit.
& Nilrudra Mandal
[email protected];
[email protected] 1
Centre for Advanced Materials Processing, CSIR - Central Mechanical Engineering Research Institute, Durgapur, West Bengal 713209, India
2
Production Engineering Department, Jadavpur University, Kolkata 700032, India
Keywords Zirconia toughened alumina (ZTA) Taguchi method Surface roughness Analysis of variance (ANOVA)
Introduction Surface finish is one of the most important considerations for determining the machinability of materials. Therefore to manufacture components economically, engineers are challenged to find out ways to improve the machinability without harming its performance. Surface roughness and dimensional accuracy are two important factors to predict machining performances of any machining operation. As the newer materials are gradually replacing the conventional cutting tool materials in better machining operations, it is essentially required to optimize the input parameters, such as feed rate, cutting speed and depth of cut for improvement of output variables, such as tool life, cutting forces, surface roughness etc. Design and methods such as factorial design, response surface methodology (RSM), Taguchi methods, Artificial Neural Network (ANN) are now widely applied instead of one factor at a time for experimental approach. Taguchi’s method of experimental design provides a simple efficient and systematic approach [1] for optimization of experimental designs for performance quality and cost [2]. Generally experimental design methods are not too good to handle more no of parameters [3]. Main advantage of Taguchi’s method is that it deals with different orthogonal arrays which require lesser no of experiments [4] to obtain the optimum levels of the process parameters. This technique has been used by several researchers [5, 6] for optimization of surface finish in turning operation with different work tool combinations. These studies give optimal parameter setting for best finish of the job. Nian et al. [7] have also utilized Taguchi experiments
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J. Inst. Eng. India Ser. C
to individually optimize tool life, cutting force and surface roughness through S/N ratio and statistical analysis of variance. Kopac et al. [8] has been attempted to optimize the surface roughness parameters for turning cold drawn steel bars using spindle speed, cutting tool material, work piece condition, depth of cut etc. However, other factors also affect surface roughness, including tool varia-tions (in addition to wear), work piece variations, and setup variations [9]. Taguchi techniques have been widely applied [10–13] to obtain desired surface finish by optimizing the main cutting parameters using different work tool combinations. Rapid development in ceramics introduced ceramic composite for many engineering applications. Zirconia Toughened Alumina (ZTA) is a new addition in this category of ceramics which possess good mechanical strength as well as toughness. ZTA is now extensively used for machining steel and cast iron at high speed. Mondal et al. [14] have tried to develop ZTA ceramic inserts by powder metallurgy route. The performance study was also carried out to standardize the product. In another work [15], Y2O3 based partially stabilized zirconia insert has been developed as well as its machinability study has been carried out to find out the stress induced transformation toughening phenomena in ZTA ceramics. Different response factors like cutting forces, tool wear and surface finish have been studied by Senthil et al. [16, 17] with the developed alumina based ceramic tools. These tools have been prepared by employing ceria to alumina matrix for toughening the ceramic composite. In this effort, different wear mechanisms like adhesive, abrasive, diffusion are also examined for different cutting conditions. In recent studies, the yttria stabilized ZTA cutting inserts have been developed by Mandal et al. [18, 19] and the flank wear modeling of these developed insert has been carried out using RSM [18] and Taguchi Method [19] while machining AISI 4340 steel. Cutting force modeling of this developed insert has been also attempted using RSM [20]. As the non isotropic behavior of the insert is very much predominant, so finding out a set of optimal condition to get a desired surface finish is also a topic of interest. To achieve this goal, Mandal et al. [21] have tried to model the surface finish using this developed insert while machining AISI 4340 steel. The purpose of this paper is to demonstrate an application of Taguchi parameter design to identify a set of optimal turning parameter for achieving best surface finish for turning AISI 4340 steel using yttria stabilized ZTA inserts.
Experimental Details
yttria stabilized zirconia (2 mol% Y2O3) in a-alumina matrix is prepared by wet mixing of aqueous solution of Al(NO3)3. 6H2O (Loba Chemie, India), ZrO(NO3)2.5H2O ([BDH, India) and Y(NO3)3.5H2O (Aldrich, USA) followed by precipitation at pH * 9. The hydrated gelatinous precipitate is washed thoroughly with hot water for removal of nitrate ions. The nitrate free dried mass of gelatinous precipitate is calcined at the temperature range 700–900 °C for 1–2 h. The calcined powders is wet-ball milled in organic media for 40–48 h using high purity (99.5 %) alumina balls in 500 ml jar contained in planetary mill (Fritsch, Germany). The morphology of the powders is characterized through particle size analyzer and FESEM studies. The requisite amount of dried milled powders for the preparation of tool inserts has been compacted uniaxially at a pressure of 2.5 ton cm-2 into square shaped (16 mm 9 16 mm 9 6 mm) pellets in a die. The compacts are sintered at 1550–1650 °C for 1–3 h in an air atmosphere. The sintered specimen is cut to size by a diamond wheel in a tool & cutter grinder machine with a tailor made designed jig-fixture and polished slowly. The final shape and size of the specimen are made very close to the international standard SNUN 120408 (ISO). Finally, the inserts are lapped/polished with fine diamond paste (0.5–1.0 lm) in polishing machine. A flat land of angle 20° and width 0.2 mm has been provided on each cutting edge to impart edge strength. After beveling, the sharp edges are further rounded off, although slowly, as uniformly as possible by light honing. The turning experiments are conducted in a lathe machine (HMT Ltd, India) powered by an 11 KW motor and speed range is 47–1600 RPM. The mechanical properties of the developed cutting insert are presented in Table 1. AISI 4340 steel has been used in this experiment and the details specifications are depicted in Table 2. The initial diameter of the bar was 140 mm and the length was 450 mm. The tool holder used is CSBNR2525N43 (NTK) and the tool angles are -6°, -6°, 6°, 6°, 15°, 15° and 0.8. The surface roughness of the AISI 4340 steel is measured after machining the job for 10 min (each run) by the help of a stylus instrument. The equipment used for measuring the surface roughness is a portable surface roughness tester SURTRONIC 25. The direction of the roughness measurement is perpendicular to the cutting velocity vector. Photo of machined AISI 4340 steel bar after turning in lathe is shown in Fig. 1. A total of five measurement of surface roughness are taken at random on each machined surface and the average value has been used in the analysis.
Materials, Test Conditions & Measurement The Taguchi Method and Design of Experiments Yttria stabilized zirconia toughened alumina ceramic powder has been synthesized by wet chemical synthesis route. The requisite amount of ingredients of 10–12 vol%
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Traditional experimental design procedures are very complicated and also difficult to handle, where a large number
J. Inst. Eng. India Ser. C Table 1 Details of composition and properties of the cutting tool material Details of inserts
Units
Yttria stabilized Zirconia toughened alumina (Y-ZTA)
Composition
Wt (%)
90 vol% a-Al2O3 ? 10 vol% Y-PSZ (2.5 mol% Y2O3)
Theoretical density
(%)
98.5
Hardness
HV
Fracture toughness
MPa m
Flexural strength
MPa
1690 ± 24 -0.5
12.20 ± 0.15 510 ± 16.3
Type & size
SNUN 120408
Geometry
-68, -68, 68, 68, 158, 758, 0.8 mm, Edge bevel width, 0.2 mm, 208
Table 2 Alloying composition (wt%) of work piece material (AISI 4340 steel) C
Mn
P
S
Si
Ni
Cr
Mo
0.45
0.70
0.04
0.03
0.25
1.65
0.85
0.25
Fig. 1 Photo of machined AISI 4340 steel bar after turning in lathe a Roughness measurement in SURTRONIC 25 b Graph in PC
Table 3 Turning parameters and their levels Parameters
Parameter designation
Level 1
Level 2
Level 3
Cutting speed (m/min)
A
140
280
420
Depth of cut (mm)
B
0.5
1.0
1.5
Feed rate (mm/rev)
C
0.12
0.18
0.24
of experiments have to be carried out for more number of variables. To overcome this problem Dr. Genichi Taguchi developed an algorithm known as Taguchi method and implemented at AT&T Bell Laboratories in the year 1980. The Taguchi method is a structured approach for determining the ‘‘best’’ combination of inputs to produce a product or service. The experimental design proposed by Taguchi involves orthogonal arrays to organize the parameters affecting the process and the levels at which they
should be varied. Taguchi’s orthogonal arrays are highly fractional orthogonal designs. These designs can be used to estimate main effects using only a few experimental runs. A loss function is then defined to calculate the difference between the target value of the performance characteristic of a process and the measured value. To determine the effect, each variable has an output, the signal-to-noise (S/ N) ratio g, or the S/N number, that needs to be calculated for each conducted experiment. There are three categories
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J. Inst. Eng. India Ser. C Table 4 L9 standard orthogonal array Experiment no
Factor A
Factor B
Factor C
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
y2i ; si
ð1Þ
where yi is the mean value and si is the variance. yi is the value of the performance characteristic for a given experiment. y2i ¼
Ni 1 X ðyi;u yi Þ Ni 1 u¼1
i experiment number, u trials number, Ni number of trials for experiment i For the case of maximizing the performance characteristic, the S/N ratio was calculated using: " # Ni 1X 1 SNi ¼ 10 log ð2Þ Ni u¼1 y2u
of the performance characteristics in the analysis of the S/N ratio, i.e. the lower-the-better (LB), the higher-the-better (HB) and the nominal-the-better (NB). The S/N ratio for each level of process parameters is computed on the S/N analysis. In this machining study it is required to get good surface finish, so in this case we choose ‘‘lower the better’’ approach. Next, a statistical analysis of variance (ANOVA) is performed to see the percentage of contribution of each parameter. By both S/N and mean response analysis, the optimal combination of process parameters are predicted. Lastly, a confirmation experiment is conducted to validate the optimized parameter in this design space. To determine the best performance characteristic, the S/N ratio was calculated using: SNi ¼ 10 log
s2i ¼
Ni 1X yi;u Ni u¼1
For the case of minimizing the performance characteristic, the S/N ratio was calculated using: " # Ni X y2u SNi ¼ 10 log ð3Þ N u¼1 i The aim of this research is to minimize roughness at the time of machining AISI 4340 steel using ZTA inserts so the smaller- the-better quality characteristic has been used. In this study, the three turning parameters (cutting speed, depth of cut and feed rate) with three different levels (low, medium and high) are used and is shown in Table 3. An L9 standard orthogonal array as shown in Table 4 has been employed for the present investigation.
Analysis, Results & Discussion Mathematical Modeling of Surface Roughness The mean value of the surface roughness is calculated after repeating the experiments thrice using the same operating conditions. The S/N ratios are computed using Eq. 3 for each of the nine trials and is reported in Table 5. The ANOVA is also performed to study the relative significance of the process parameters and result has been
Table 5 Results for quality characteristics and signal-to-noise ratio for surface roughness (Ra) Experiment no
Surface roughness (Ra), (lm)
S/N ratio (db)
Reading 1
Reading 2
Reading 3
Mean
1
3.22
3.23
3.25
3.233
10.193
2
3.65
3.65
3.64
3.647
11.238
3
4.12
4.14
4.10
4.120
12.298
4
3.65
3.63
3.62
3.633
11.206
5
3.86
3.88
3.84
3.860
11.732
6
3.30
3.32
3.28
3.280
10.370
7 8
2.05 2.86
2.08 2.80
2.03 2.92
2.053 2.860
6.250 9.128
9
3.35
3.33
3.40
3.360
10.527
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J. Inst. Eng. India Ser. C Table 6 Analysis of variance (ANOVA) for surface roughness (Ra) Variance (V)
F-ratio
Pure sum of squares (S0 )
Percent contribution (%) (P)
2
2.3280
29.45
4.4979
50.62
1.8876
2
0.9438
11.94
1.7295
19.46
0.7609
2
0.3804
4.81
0.6028
6.78
others/error
1.5811
2
0.0790
2.0555
23.13
Total
8.8857
26
8.8857
100
Factors
Sum of squares (S)
Cutting speed
4.6560
Depth of cut Feed
Degrees of freedom (DOF)
–
–
Fig. 2 a Effect of process parameters on average S/N ratio for surface roughness. b Effect of process parameters on mean value for surface roughness
reported in Table 6. The F ratio & percentage contribution of the various parameters has been calculated and it is observed that the cutting speed has predominant role with the roughness (51 % contribution) followed by depth of cut
(19 % contribution). The average value of S/N ratios of three control factors at each level is shown in Fig. 2a. From this, it can be concluded that optimum condition corresponds to the highest S/N ratio.i.e. A3 (cutting speed: 420),
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J. Inst. Eng. India Ser. C
Sopt ¼ Y þ A3 Y þ B1 Y þ C1 Y ;
Table 7 Confirmation experiments Replication
Surface Roughness (lm)
Mean
1
2
3
1.85
2.54
2.22
2.20
Table 8 Regression statistics & coefficient values for regression equation of Ra Regression statistics Multiple R
Coefficient 0.7989
Intercept
3.317
R
0.6383
Cutting speed
-0.003
Adjusted R2
0.4212
Depth of cut
0.6155
SE
0.4625
Feed
1.7778
2
B1 (depth of cut: 0.5), C1 (feed rate: 0.12). In addition to S/N analysis, main effect of the process parameters on the mean response is also studied. Thus the mean values of feed forces for each factor at three levels has been computed and plotted in Fig. 2b. This figure also revealed the optimum levels of each parameter (A3, B1 & C1) which is in line with the values obtained by S/N analysis. Optimization of Surface Roughness From S/N analysis and mean response characteristics, the optimum level of control factors for surface roughness is calculated as A3, B1 & C1. Hence, the predicted mean of quality characteristics i.e. surface roughness is computed as
Fig. 3 Predicted versus actual values of surface roughness
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ð4Þ where Y is the total average of performance characteristics [corresponding to all the 27 (9 9 3) reading in Table 5]. A3 , B1 , C1 are the average values of the surface roughness with process parameters at their respective optimal levels and Sopt denotes the predicted mean of the surface roughness at optimum condition. The calculated values of various response averages are: Y = 3.34 lm, A3 = 2.75 lm, B1 = 2.97 lm and C1 = 3.13 lm. So, substituting these in Eq. 4, the mean optimum value of the roughness (Ra) has been predicted as Sopt = 2.17 lm. In Taguchi optimization technique confirmation experiment is required to be conducted for validating of the optimized condition. If the reliability of the condition is assumed to be 95 %, then the confidence interval can be calculated using the following equation: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi CI ¼ F ða; 1; feÞ Vc 1 Neff þ ð1=RÞ ð5Þ Neff ¼ 1 1 þ Tdof ð6Þ where, F (a, 1, fc) is the F-ratio required for 100 (1-a) percent confidence interval, fe, the DOF for error = 20, Vc, the error variance = 0.079, R, the number of replications for confirmation experiments = 3, Neff the effective number of replication, N, the total number of experiments = 27 (9 9 3) and Tdof is the total degrees of freedom associated with the mean optimum = 6 (2 9 3). From standard statistical table, the required F ratio for a = 0.05 is F(0.05,1,20) = 4.351. So, substituting the values in Eqs. 5
J. Inst. Eng. India Ser. C
& 6, we get Neff = 3.8571 & CI = ± 0.4513 Thus, 95 % confidence level interval, predicted optimal surface roughness (Ra) come out to be 2.17 ± 0.4513 lm i.e. the confirmation result for surface roughness (Ra) should be in between 1.72 lm \ Sopt \ 2.62 lm.
1.
2.
Confirmation Run For validation of the optimized result, three replication experiments are conducted using the optimal setting of the process parameters i.e. cutting speed at 420 m/min, depth of cut at 0.5 mm and feed rate at 0.12 mm/rev. The result of the experiments is depicted in Table 7. It has been observed experimentally that the mean value of roughness is around 2.20 lm which fall within the predicted 95 % confidence interval.
3.
4.
Development of Regression Model The regression model is calculated by the mean values of different roughness under different operating condition and the equation is depicted as SurfaceRoughness(Ra) ¼ 3:32 0:0032 Cutting Speed þ 0:6155 Depth of Cut þ 1:77 Feed ð7Þ The regression statistics of surface roughness is also calculated using the software Excel 2010 version and is shown in Table 8. From the tables, it could be concluded that the value of Multiple R of surface roughness is around 0.7989. That implies the regression model as fitted explained 80 % of the variability of roughness. The predicted versus actual values of different roughness is shown in Fig. 3, which also suggest that the values are very close to each other.
Conclusion In the present work, Taguchi method has been applied for modeling the surface roughness produced while turning of AISI 4340 steel by zirconia toughened alumina (ZTA) cutting insert. In this study, L9 orthogonal array is applied and S/N ratio has been calculated adopting smaller the better approach. ANOVA technique has been used to visualize the significance of each parameter like cutting speed, feed rate and depth of cut on the surface properties of the finished job. Optimization of parameter is also performed with 95 % confidence level. The confirmation experiments are also performed for validating the model. The following conclusion can be drawn as under:
Based on the ANOVA results, it has been observed that cutting speed dominates the surface roughness with almost 51 % contribution followed by depth of cut with 19 % contribution. Based on the signal to noise (S/N) ratio using smaller is the better approach,it has been found that the best optimal cutting condition for surface roughness is A3B1C1 i.e. cutting speed is 420 m/min, depth of cut is 0.5 mm and feed rate is 0.12 mm/rev. An optimized value of surface roughness with 95 % confidence level has been predicted as 2.17 ± 0.4513 lm. The value of the roughness by confirmation experiments using the optimized cutting condition comes out 2.20 lm. From this data, it can be concluded that the optimization using Taguchi’s technique holds good for roughness estimation in machining operation. The model developed for roughness prediction using regression analysis are also proved good where predicted values of roughness are very close to the experimental values and desirability is also very high (80 %).
Acknowledgments The authors are thankful to CSIR-Central Mechanical Engineering Research Institute, Durgapur, India for constant support in completing this research work. The authors, also acknowledge, with thanks, the Science and Engineering Research Board, Department of Science and Technology, New Delhi, India for providing financial support (SERB sanction order no. SB/S3/MMER/ 0035/2014 dated 22-05-2014) to carry out this work.
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