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Optimization of Surface Roughness and Power Consumption in laser-assisted machining of Inconel 718 by Taguchi based Response Surface Methodology
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ScienceDirect Materials Today: Proceedings 5 (2018) 11326–11335
www.materialstoday.com/proceedings
ICMMM - 2017
Optimization of Surface Roughness and Power Consumption in laser-assisted machining of Inconel 718 by Taguchi based Response Surface Methodology K. Venkatesana* a
School of Mechanical Engineering, VIT University, Vellore, India
Abstract Inconel 718 a difficult-to-machine material is used in critical aero engines components. Surface roughness (product quality) and power consumption (energy efficiency) are two important benchmark factors for manufacturing industry as it deserves to determine the optimal design points in laser assisted machining (LAM) for sustainability performance of the machining process. Moreover, the optimization of laser beam angle and laser power along with technological parameters (cutting speed, feed rate) is not reported in the literature during laser-assisted machining (LAM) of Inconel 718. In this context, it is worthy to investigate the effects of these parameters on these two quality characteristics during the LAM of this fantastic material. Laser aided machinability experiments are carried out with coated carbide insert at three different cutting speeds (60, 105 and 150 m/min), three different feed rates (0.05, 0.0875 and 0.125 mm/rev), three laser power (1250, 1500 and 1750 W) and three laser beam angle (60, 70, 80 deg) under lased-aided dry cutting conditions. The percent contribution of the main effects of the cutting parameters to the quality parameters is determined using analysis of variance (ANOVA), and predictive linear equations are developed for the estimation of all the quality characteristics. Thereafter, optimal cutting parameters are obtained. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).
Keywords: laser parameters; surface roughness; power consumption; analysis of variance
* Corresponding author. Tel.: +91-944-38-10370. E-mail address: [email protected] 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).
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Nomenclature LAM Nd:YAG CM ANOVA R2 CO2
laser-assisted machining neodymium-doped yttrium aluminium garnet conventional machining analysis of variance determination of coefficient carbon dioxide
1. Introduction Inconel 718 is a precipitated nickel based alloy widely found in gas turbines components, petrochemical and heattreating fixtures. This alloy offers corrosion resistant to chemical and oxidation as well as high strength at elevated temperatures [1, 2]. Machining of this alloy found to be difficulty due to high temperature at the tool tip, built-up edge (BUE) formation and presence of hard secondary particles. Additionally, low heat transfer coefficient of this alloy results in high temperature absorption in the cutting area. All these difficulties restrict the machining of Inconel 718 during conventional machining (CM) process [3, 4]. Lasers are widely used to remove materials for laser beam machining. On the other hand, a laser is used partially to soften materials in combination with cutting tools in conventional machining [5]. This hybrid process is known as laser assisted machining (LAM). Nowadays, laser assisted machining is considered as a promising solution for machining of difficult-machine materials (composites, ceramics, titanium alloy and hardened steels) due to precise control of laser heated region in front of cutting tool. This resulted in improvement in machinability benefits such as reduction in cutting force, improvement in surface roughness and tool life [6]. Application of scientific method such as Taguchi orthogonal method to design the experiments and determination of optimal parameters using response surface design is not reported. The literature overcome the above cons on LAHM process parameters and resulted in improved machinability. Anderson et al. [7] used multiple laser units (for 1.5 kW CO2 laser at 90° i.e. normal and for 500 W Nd:YAG at 45° laser source from the normal) to create the desired temperature distribution in the workpiece simultaneously on the unturned surface and chamfer surface to improve the machinability. Attia et. al [8] investigated the influence of input parameters on cutting force, surface roughness and tool wear at high speed finishing operation of Inconel 718 using SiAlON ceramic tool under dry conditions with Nd:YAG laser source. The laser beam is focused on the workpiece and is positioned at a 48-50° angle to the vertical direction (i.e. normal). The effect of laser spot contour configuration with respect to the cutting tool is studied by García Navas et al. [9] for Inconel 718 alloy with a 2.2 kW Nd:YAG laser 718 using CVD (cemented carbide+TiCN+Al2O3+TiN) tool under dry conditions. Venkatesan et. al., [10] investigated the simultaneous effect of the approach angle of laser beam (60°<θ<90°), laser power (1.25kW
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ANN is required for the development of empirical model, whereas RSM can be utilized to find the optimum cutting parameter settings. In the field of machining of Inconel 718, literature is plentiful and widely available but fewer research works are carried out about the effect of laser beam angle along with laser power, speed and feed on cutting power and roughness (arithmetic average Ra value). In this context, the objective is to correlate cutting parameters (θ, P, Vc, and f) with roughness, and cutting power during the LAM of Inconel 718 using the RMS method. The development of a second-order model is adopted to predict the technological parameters (Ra and Pc). The final goal of this work is to optimize cutting conditions using the desirability function. 2. Experimental sections A solid state continuous wave 2 kW Nd:YAG laser source is used for heating the workpiece. The focusing head was focused to the workpiece surface with a focal length of 160 mm through a lens. The focusing head was supported to lathe machine with a specially designed fixture in order to varying the radial position of laser beam axis to tool as shown in Figure 1 (a) and (b). The infrared pyrometer is positioned directly above the cutting tool to continuously measure the surface temperature (500°C-2400°C) of the workpiece as shown in Figure 1 (a). The surface temperature zone at cutting zone is measured continuously using infrared pyrometer and recorded in data acquisition software enabled with pyrometer. Design expert V8.0 software is used for experimental design. The experiments are planned according to the second order central composite rotatable design (CCRD) based L31 (43) on the principle of response surface methodology (RSM).
Fig. 1. (a) Setup for laser heating process ; (b) Approach angle of laser beam.
The four parameters are as follows: S = cutting speed, F = feed rate, P= Laser power and θ = approach angle of laser beam with tool. These parameters are varied at three different levels in CCRD design and totally thirty one experiments were performed using half replication with α=2 (α=k1/4). The process parameters and their levels considered for experimentation is listed in Table 1. Before conduct of actual machining trials, the following laser parameters has been fixed: spot diameter (Dlaser) = 2 mm, laser-tool lead (Llead)= 2 mm and workpiece diameter (Dlaser) = 25 mm. Turning experiments are conducted with a PVD carbide insert (CNMG120408-KCU25) has a significantly higher hot hardness of above 1000°C. The cutting insert is mounted on the tool holder used in this experimental study has the standard designation of PCLNR 2020 K12. The tool signature of the insert in a standard tool holder with back rake angle of -6°, approach angle 95°, the relief angle of 6°, include angle 80°, clearance angle 6°, and nose radius 0.8 mm. The laser was irradiated at the workpiece and once the yield temperature of workpiece material is reached the feed is given to the tool in the axial direction. Then, precipitated age hardened Inconel 718 has been turned with laser assistance for a depth of cut of 0.50 mm. A cutting length of 60 mm was machined during each experiment. The surface roughness Ra (μm) value was measured using the Mahr surf test
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(Model GD120). Three measurements of surface roughness are taken at different locations and the average value is used in the analysis. The experimental layout with the cutting parameters and corresponding response values for the experimental design matrix obtained from experimentation are tabulated in Table 1. Table 1. Levels of parameters, L31 central composite design and its experimental results. Parameter and symbol
50
Level 2 105
150
0.05
0.088
0.125
kW
1250
1500
1750
deg
60°
70°
80°
Unit
Level 1
Cutting speed (Vc)
m/min
Feed rate (f)
mm/rev
Laser power (P) Approach angle (θ) Sl No.
Cutting Parameters Speed (S) (m/min)
Feed rate (f) (mm/rev)
Level 3
Response variables Power (P) (W)
Approach angle (θ) (deg)
cutting force Fz, N
Surface roughness (Ra) (µm)
Power consumption W
1
60
0.05
1250
60
91.63
0.7231
91.63
2
150
0.05
1250
60
66.83
0.5559
167.08
3
60
0.125
1250
60
118.9
0.4642
118.90
4
150
0.125
1250
60
76.16
0.4892
190.40
5
60
0.05
1750
60
83.74
0.5192
83.74
6
150
0.05
1750
60
48.98
0.4670
122.45
7
60
0.125
1750
60
108.6
0.4269
108.60
8
150
0.125
1750
60
62.9
0.6399
157.25
9
60
0.05
1250
80
61.25
0.6726
61.25
10
150
0.05
1250
80
51.95
0.4845
129.88
11
60
0.125
1250
80
89.35
0.4975
89.35
12
150
0.125
1250
80
107
0.5613
267.50
13
60
0.05
1750
80
42.36
0.4976
42.36
14
150
0.05
1750
80
62
0.5162
155.00
15
60
0.125
1750
80
78.84
0.5414
78.84
16
150
0.125
1750
80
90.58
0.7619
226.45
17
60
0.0875
1500
70
91.09
0.3489
91.09
18
150
0.0875
1500
70
74.97
0.4163
187.43
19
105
0.05
1500
70
64.51
0.5355
112.89
20
105
0.125
1500
70
98.82
0.5808
172.94
21
105
0.0875
1250
70
97.44
0.5263
170.52
22
105
0.0875
1750
70
75.52
0.4736
132.16
23
105
0.0875
1500
60
65.48
0.4809
114.59
24
105
0.0875
1500
80
62
0.4721
108.50
25
105
0.0875
1500
70
88.45
0.4315
154.79
26
105
0.0875
1500
70
73.31
0.4315
128.29
27
105
0.0875
1500
70
88.45
0.4315
154.79
28
105
0.0875
1500
70
73.31
0.4315
128.29
29
105
0.0875
1500
70
88.45
0.4915
154.79
30
105
0.0875
1500
70
73.31
0.3904
128.29
31
105
0.0875
1500
70
88.45
0.3691
154.79
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3. Response surface method Response surface method initially developed by Box and Wilson [16] is used for experimental optimization of defined cutting parameters in machining domain. RSM is an experimental-modeling based quantitative approach for the evaluation of relationship between the two variables (i.e. dependent and independent variables). In the present investigation, the RSM based second-degree polynomial mathematical models for roughness (Ra) are developed in order to best bit the effect of cutting parameters (S, F, P, θ). The relationship between input parameters and desired machining attributes can be expressed as follows: = ɸ ( S, F, P, θ )
− − − −(1)
Where Ra is the preferred machining attributes and φ is the objective function. The approximation of Ra is proposed by using a second-quadratic mathematical model, which in turn to study the factor interaction effects on machining attributes. In the present work, the RSM based regression model is given by the following: Yk = a0+∑
+ ∑∑
+∑
± eu----(2)
Where ao is constant, bi, bii, and bij represents the second order regression coefficient of linear; quadratic; and cross product terms, respectively. The xi reveals the coded/actual variables. The response coefficients (bi, bii, and bij) shown in the above model can be computed by means of method of least square technique. The cutting power during laser assisted turning is calculated based on measured cutting force as follows: =
60
− − − − − −(3)
Where Pc is cutting power (W), Fz is the cutting force (N), S is speed (m/min), 4. Results and Discussion 4.1. Analysis of variance The analysis of variance (ANOVA) is a standard statistical test that is commonly used in order to determine the significance of the controllable factors on the output responses [17]. It does not analyze the data directly, but determines the percentage of contribution of each factor in determining the variability (variance) of data. The ANOVA table is composed of the sum of squares (SS) and degrees of freedom (DoF) [18]. Tables 2 and 3 illustrate ANOVA results for surface roughness (Ra) and cutting power (Pc), respectively, for a 95% confidence level. In these tables are listed the values of DoF, the sum of squared deviations (SS), mean square (MS) and percentage of contribution (cont %) of each model terms. The main purpose is to analyze the influence of the cutting parameters (θ, P, S, f) on the total variance of the results. The values of ‘P‘ in the models are less than 0.05, indicating that the models are adequate and that the terms have a significant effect on the responses, which are desirable. The ‘model F value’ of 17.82 (Table 2) and 17.42 (Table 3) with its P-value less than 0.0001 imply that the model is statistically significant. From Table 4, the R-squared value of 0.9397 (Table 4) for Ra and 0.9348 (Table 4) for Pc for responses and their R2 closeness to unity describes the model fitness. Therefore, the proposed model is adequate on the basis of the high values of the determination coefficient in representing the process. The fitness of experimental data to the developed mathematical model for responses is revealed by the descriptive agreement between the ‘predicted Rsquared’ and ‘adjusted R-squared’ value observed from Table 4. Parameter B and D are the most substantial model terms that affecting the Ra. The significant model terms for PC include A, B, C D, AB, AD,BD are significant, whose ‘P-values’ values < 0.05 specify the importance of model terms.
Venkatesan/ Materials Today: Proceedings 5 (2018) 11326–11335 Table 2. Results of ANOVA for surface roughness (Ra). Source
SS
Model
0.2561
A:Cutting speed B:Feed rate C:Laser Power D:Apporach angle
0.0063 0.0024 0.0041 0.0031
A2 B2 C2 D2
0.0118 0.0302 0.0063 0.0017
AB AC AD BC BD CD Error Lack-of-fit Pure Error Total
0.0518 0.0277 0.0057 0.0393 0.0118 0.0049 0.0087 0.0731 0.0014 0.2725
DF
F value
14 17.82 Linear: 1 1.55 1 2.18 1 0.79 1 6.17 Square: 1 11.57 1 29.39 1 6.23 1 1.73 2-Way Interaction 1 50.52 1 27.04 1 0.56 1 38.37 1 11.59 1 4.57 16 10 0.59 6 30
p-value Prob>F 0.000
Significance
0.235 0.051 0.385 0.024
Non-Significant Significant Non-Significant Significant
0.000 0.000 0.000 0.024
Significant Significant Significant Significant
0.000 0.000 0.464 0.000 0.000 0.044
Significant Significant Non-significant Significant Significant Significant
0.836
Non-Significant
Table 3. Results of ANOVA for cutting power (Pc). Source
SS
Model
62418.8
A:Cutting speed B:Feed rate C:Laser Power D:Apporach angle
38982.4 10949.7 1793.0 337.05
A2 B2 C2 D2
30.64 39.3 393.9 1959.1
AB AC AD BC BD CD Error Lack-of-fit Pure Error Total
1411.5 132.9 4648.5 148.5 1668.7 160.3 4049.2 2891.8 1203.4 66513.9
DF
F value
14 17.42 Linear: 1 50.52 1 152.31 1 7.02 1 5.89 Square: 1 0.54 1 0.15 1 1.54 1 7.65 2-Way Interaction 1 5.33 1 0.52 1 18.16 1 0.58 1 6.52 1 0.63 16 10 1.44 6 30
p-value Prob>F 0.000
Significance
0.000 0.000 0.018 0.027
Significant Significant Significant Significant
0.987 0.700 0.233 0.014
Non-Significant Non-Significant Non-Significant Significant
0.032 0.482 0.001 0.457 0.021 0.440
Significant Non-Significant Significant Non-Significant Significant Non-Significant
0.339
Non-Significant
Table 4. Statistical model accuracy estimator for surface roughness (Ra) and cutting power (Pc).
Ra
Standard deviation 0.032
Rsquared 0.9397
Adj Rsquared 0.8870
Pred Rsqaured 0.8365
Pc
15.99
0.9384
0.8846
0.7304
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4.2. Regression analysis In this study, the relationship between the cutting parameters and objective functions of surface roughness, Ra, and cutting power (Pc) are modeled using RSM based quadratic regression equations. The final equation in terms of coded factor for surface roughness (Ra) and cutting power (Pc) are given in Eq. (4) and Eq. (5). = 7.054
0.0006
− 27.85 − 0.00475 − 0.0536 − 0.000042 63.7 0.000272 0.0345 0.0004 0.0052 0.0007 − − − −(4)
0.0001 0.0727
Fig. 2. Normality plot for a) Ra b) PC.
Fig. 3. Comparison of test results between experimental and predicted values for a) Ra b) Pc.
The developed model is used to forecast roughness Ra, and cutting power Pc at the particular design points. Figure 2 and Figure 3 illustrate the normality plot and the variances between predicted and measured values of Ra, and Pc respectively. A check on the normal probability plot reveals that 98% of residuals/errors are falling within three sigma limits. Further, it can be seen in Fig. 3 (b) that the actual values are following the predicted values calculated from the model, therefore confirming the reliability of ANOVA. The comparison results prove that predicted values of different controllable parameters are closer to those readings recorded experimentally. 4.3. Interaction effect on response (2D fitted mean plots) 2D interaction plots for the surface roughness are illustrated in Fig. 4. It can be noticed that with an increase in the speed, the amount of surface roughness increases at higher feed rate and laser beam angle. In LAT, an increase in cutting speed at higher feed rate for fixed laser beam resulted in decrease the interaction time between the laser beam and workpiece and as a consequence resulted in more flank wear. Attia et al. [8] show that the surface roughness increases with increase of cutting speed for the fixed laser parameters. As the speed and laser power increases speed at higher laser power of 1750 W, the magnitude of Ra surface roughness is increased more steadily. An increment in laser power with increase of speed resulted in increased tool wear which subsequently upsurges the
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surface roughness. From illustration of feed rate and laser power on surface roughness (Ra), it can be seen that with increase in feed at higher value of laser power the amount of surface roughness increases linearly. At lower laser beam, the value of surface roughness is found to be decreases, however very large variations are found at higher laser beam angle with increment in laser power. Therefore, lower value of laser beam angle and laser power can result in lower value of surface roughness. Anderson et al. [7] found the surface roughness value drops on increment in speed and drops in feed, and decrement in the laser power or material removal temperature. From the plots, the value of Ra is found to be lower of about 0.3933µm at low cutting speed, moderate feed rate and laser power with low laser beam angle.
Fig. 4. Interaction plots for Ra
Fig. 5. Interaction plots for Pc
2D interactions plots for the cutting power are illustrated in Fig. 5. The magnitude of cutting power is increased with increase in speed and feed. While laser power is kept constant, the increasing cutting speed and lessening feed
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rate increases the interaction time allowed for the laser power to be absorbed by the material. This resulted in higher magnitude in force. Additional it is noticed that, the maximum amount of cutting force is generated at a higher level of feed and speed. But, Anderson et al. [7] found that the cutting forces decreased with increasing feed rate and Navas et al. [10] observed that increases with increasing cutting speed. This is contrary to the present study. It is perceived from Fig. 5 when the feed and laser power increase, the amount of the cutting force tends to increases. The maximum cutting force is observed at a low level of laser power and higher feed. The increase in the flank wear at a higher feed rate with lower laser power creates the damage on the cutting edge, producing the abrasion between the tool edge and workpiece surface, thus increases the cutting force. This result is being a contrary with the experimental results attained by Attia et al. [8]. From Fig. 5, the value of cutting power increases with increases in cutting speed and feed rate for laser beam angle of 70°. The cutting power magnitude is higher for low laser power and moderate laser beam angle. From the plots, the value of Pc is found to be lower of about 83 W at low cutting speed, low feed rate and moderate laser power with moderate laser beam angle. 5. Optimal search using desirability function approach The concept based desirability function approach (DFA) for multi-response optimization (MRO) is proposed by Derringer and Suich [19]. The steps involved for optimal search are detailed below: (i) this method utilize an objective function, D(x), so called the desirability function, and transform the estimated individual response into a corresponding scale free composite desirability (di). Based on the type of response criterion i.e. minimization or maximization, the desirability functions are to be selected. The calculated desirability values lies in between 0 to 1. (ii) after computing the desirability value for each response the composite desirability has been computed by geometric mean of the responses. The factor setting with the maximum composite desirability is to be considered as an optimal parameter conditions that simultaneously satisfy the objective functions of each response. Cutting speed, feed rate, laser power and laser beam angle are taken as input parameters. In the present study desirability objective functions are: (i) minimizing the cutting power, and (ii) minimizing the surface roughness. The formula for calculating an overall desirability value is given in Eq. (6). ( )=(
……
) =
− − − (6)
where, di are represents the individual objective/constraint desirability functions, ‘n’ represents number of objectives, wi are represents the weights of each individual objective. Figure 6 show the optimization results. From the results, the highest composite desirability value of 0.776 which corresponding to the optimum levels of cutting speed, feed rate, laser power and laser beam angle are 60 m/min, 0.0875 mm/rev, 1.5 kW, and 67° respectively.
Fig. 6. Optimization plot for overall desirability.
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6. Conclusion This study focuses on the determination of the optimum cutting conditions leading to minimum surface roughness, and cutting power. For that, the case of the laser assisted turning of the Inconel 718 was studied. From the results discussed above, the flowed conclusions could be drawn: Based on the variance test for surface roughness Ra, it’s found that the feed rate and laser beam angle is the most important factor affecting Ra followed by the cutting speed and feed rate (F-value=50.52), feed and laser power (F-value=38.27) respectively. Nevertheless, other terms have no significant effect. The 2D interaction plot of the surface roughness enables to identify and confirm the effect of feed rate and laser beam (F-value=11.59) on the surface roughness evolution. In cutting power, all the input parameters are significant in cutting power, but feed rate is the most significant factor followed by cutting speed and laser beam angle, respectively. Comparison between predicted and measured values of Ra, and Pc proves that predicted values of different controllable factors studied parameters are very close to those readings recorded experimentally, their coefficients of determination R2 are 93.97% and 93.84%, respectively. Single-objective optimization results was concluded that the optimal values for minimizing surface roughness are (S=60 m/min, f=0.0875mm/rev, P =1.5kW and θ=60°). The optimal values for maximizing cutting power was found to be (S=60 m/min, f=0.05mm/rev, P =1.5kW and θ=80°). Multi-objective optimization results was found that the best combination values for minimizing the surface roughness and cutting power are cutting speed are 60 m/min, 0.0875 mm/rev, 1.5 kW, and 67°. References [1] Ezugwu, E. O. and C. I. Okeke, Tribol T. 43 (2) (2000) 332-336. [2] A. Bhatt, H. Attia, R. Vargas, and V. Thomson, Tribol. Int. 43 (5) (2010) 1113-1121. [3] D. Ulutan, and T. Ozel, . Int. J Mach. Tool Manu. 43 (13) (2011)1391-1396. [4] Zhu, Dahu, Xiaoming Zhang, and Han Ding, . Int. J Mach. Tool Manu 64 (2013) 60-77. [5] Kannan, Venkatesan, Ramanujam Radhakrishnan, and Kuppan Palaniyandi, Engineering Review 34 (2) (2014) 75-84. [6] K.Venkatesan, R. Ramanujam, and P. Kuppan. Procedia Eng. 97 (2014)1626-1636. [7] Anderson, Mark, Rahul Patwa, and Yung C. Shin. Int. J Mach. Tool Manu 46 (2006) 1879-1891. [8] Attia, Helmi, Salar Tavakoli, Raul Vargas, and Vincent Thomson. CIRP Ann Manuf. Techn 59 (1) (2010) 83-88. [9] Virginia García Navas, Iban Arriola, Oscar Gonzalo, Josu Leunda. Int. J Mach. Tool Manu. 74 (2013) 19–28. [10] K.Venkatesan, R. Ramanujam, and P. Kuppan. Opt Laser Technol 78 (2016) 10-18. [11] K.Venkatesan and R. Ramanujan. Measurement 89 (2016) 97-108. [12] C. Sanjay, C. Jyothi, Int. J. Adv. Manuf. Techn. 29 (2006) 846–852. [13] V. Upadhyay, P. K. Jain, N. K. Mehta, Measurement, 46(1) (2013) 154-160. [14] M.S Yazdi, A. Khorram, Int. J. Eng. Technol. 2 (2010) 474-380. [15] A. K. Lakshminarayanan, and V. Balasubramanian, Trans. Nonferrous Met. Soc. China, 19 (2009) 9-18 [16] G. E. Box, and K. B. Wilson, Journal of the Royal Statistical Society. Series B (Methodological), 13(1) (1951) 1-45. [17] Bouzid, Lakhdar, Smail Boutabba, Mohamed Athmane Yallese, Salim Belhadi, and Francois Girardin, Int. J. Adv. Manuf. Techn. 74 (5-8) (2014) 879-891. [18] I Meddour, M. A. Yallese , R. Khattabi, M. Elbah and L. Boulanouar, Int. J. Adv. Manuf. Techn.77 (5-8) (2015) 1387-1399. [19] Derringer, George. J Qual. Technol. 12 (1980) 214-219.
ScienceDirect Materials Today: Proceedings 5 (2018) 11326–11335
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ICMMM - 2017
Optimization of Surface Roughness and Power Consumption in laser-assisted machining of Inconel 718 by Taguchi based Response Surface Methodology K. Venkatesana* a
School of Mechanical Engineering, VIT University, Vellore, India
Abstract Inconel 718 a difficult-to-machine material is used in critical aero engines components. Surface roughness (product quality) and power consumption (energy efficiency) are two important benchmark factors for manufacturing industry as it deserves to determine the optimal design points in laser assisted machining (LAM) for sustainability performance of the machining process. Moreover, the optimization of laser beam angle and laser power along with technological parameters (cutting speed, feed rate) is not reported in the literature during laser-assisted machining (LAM) of Inconel 718. In this context, it is worthy to investigate the effects of these parameters on these two quality characteristics during the LAM of this fantastic material. Laser aided machinability experiments are carried out with coated carbide insert at three different cutting speeds (60, 105 and 150 m/min), three different feed rates (0.05, 0.0875 and 0.125 mm/rev), three laser power (1250, 1500 and 1750 W) and three laser beam angle (60, 70, 80 deg) under lased-aided dry cutting conditions. The percent contribution of the main effects of the cutting parameters to the quality parameters is determined using analysis of variance (ANOVA), and predictive linear equations are developed for the estimation of all the quality characteristics. Thereafter, optimal cutting parameters are obtained. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).
Keywords: laser parameters; surface roughness; power consumption; analysis of variance
* Corresponding author. Tel.: +91-944-38-10370. E-mail address: [email protected] 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).
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Nomenclature LAM Nd:YAG CM ANOVA R2 CO2
laser-assisted machining neodymium-doped yttrium aluminium garnet conventional machining analysis of variance determination of coefficient carbon dioxide
1. Introduction Inconel 718 is a precipitated nickel based alloy widely found in gas turbines components, petrochemical and heattreating fixtures. This alloy offers corrosion resistant to chemical and oxidation as well as high strength at elevated temperatures [1, 2]. Machining of this alloy found to be difficulty due to high temperature at the tool tip, built-up edge (BUE) formation and presence of hard secondary particles. Additionally, low heat transfer coefficient of this alloy results in high temperature absorption in the cutting area. All these difficulties restrict the machining of Inconel 718 during conventional machining (CM) process [3, 4]. Lasers are widely used to remove materials for laser beam machining. On the other hand, a laser is used partially to soften materials in combination with cutting tools in conventional machining [5]. This hybrid process is known as laser assisted machining (LAM). Nowadays, laser assisted machining is considered as a promising solution for machining of difficult-machine materials (composites, ceramics, titanium alloy and hardened steels) due to precise control of laser heated region in front of cutting tool. This resulted in improvement in machinability benefits such as reduction in cutting force, improvement in surface roughness and tool life [6]. Application of scientific method such as Taguchi orthogonal method to design the experiments and determination of optimal parameters using response surface design is not reported. The literature overcome the above cons on LAHM process parameters and resulted in improved machinability. Anderson et al. [7] used multiple laser units (for 1.5 kW CO2 laser at 90° i.e. normal and for 500 W Nd:YAG at 45° laser source from the normal) to create the desired temperature distribution in the workpiece simultaneously on the unturned surface and chamfer surface to improve the machinability. Attia et. al [8] investigated the influence of input parameters on cutting force, surface roughness and tool wear at high speed finishing operation of Inconel 718 using SiAlON ceramic tool under dry conditions with Nd:YAG laser source. The laser beam is focused on the workpiece and is positioned at a 48-50° angle to the vertical direction (i.e. normal). The effect of laser spot contour configuration with respect to the cutting tool is studied by García Navas et al. [9] for Inconel 718 alloy with a 2.2 kW Nd:YAG laser 718 using CVD (cemented carbide+TiCN+Al2O3+TiN) tool under dry conditions. Venkatesan et. al., [10] investigated the simultaneous effect of the approach angle of laser beam (60°<θ<90°), laser power (1.25kW
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ANN is required for the development of empirical model, whereas RSM can be utilized to find the optimum cutting parameter settings. In the field of machining of Inconel 718, literature is plentiful and widely available but fewer research works are carried out about the effect of laser beam angle along with laser power, speed and feed on cutting power and roughness (arithmetic average Ra value). In this context, the objective is to correlate cutting parameters (θ, P, Vc, and f) with roughness, and cutting power during the LAM of Inconel 718 using the RMS method. The development of a second-order model is adopted to predict the technological parameters (Ra and Pc). The final goal of this work is to optimize cutting conditions using the desirability function. 2. Experimental sections A solid state continuous wave 2 kW Nd:YAG laser source is used for heating the workpiece. The focusing head was focused to the workpiece surface with a focal length of 160 mm through a lens. The focusing head was supported to lathe machine with a specially designed fixture in order to varying the radial position of laser beam axis to tool as shown in Figure 1 (a) and (b). The infrared pyrometer is positioned directly above the cutting tool to continuously measure the surface temperature (500°C-2400°C) of the workpiece as shown in Figure 1 (a). The surface temperature zone at cutting zone is measured continuously using infrared pyrometer and recorded in data acquisition software enabled with pyrometer. Design expert V8.0 software is used for experimental design. The experiments are planned according to the second order central composite rotatable design (CCRD) based L31 (43) on the principle of response surface methodology (RSM).
Fig. 1. (a) Setup for laser heating process ; (b) Approach angle of laser beam.
The four parameters are as follows: S = cutting speed, F = feed rate, P= Laser power and θ = approach angle of laser beam with tool. These parameters are varied at three different levels in CCRD design and totally thirty one experiments were performed using half replication with α=2 (α=k1/4). The process parameters and their levels considered for experimentation is listed in Table 1. Before conduct of actual machining trials, the following laser parameters has been fixed: spot diameter (Dlaser) = 2 mm, laser-tool lead (Llead)= 2 mm and workpiece diameter (Dlaser) = 25 mm. Turning experiments are conducted with a PVD carbide insert (CNMG120408-KCU25) has a significantly higher hot hardness of above 1000°C. The cutting insert is mounted on the tool holder used in this experimental study has the standard designation of PCLNR 2020 K12. The tool signature of the insert in a standard tool holder with back rake angle of -6°, approach angle 95°, the relief angle of 6°, include angle 80°, clearance angle 6°, and nose radius 0.8 mm. The laser was irradiated at the workpiece and once the yield temperature of workpiece material is reached the feed is given to the tool in the axial direction. Then, precipitated age hardened Inconel 718 has been turned with laser assistance for a depth of cut of 0.50 mm. A cutting length of 60 mm was machined during each experiment. The surface roughness Ra (μm) value was measured using the Mahr surf test
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(Model GD120). Three measurements of surface roughness are taken at different locations and the average value is used in the analysis. The experimental layout with the cutting parameters and corresponding response values for the experimental design matrix obtained from experimentation are tabulated in Table 1. Table 1. Levels of parameters, L31 central composite design and its experimental results. Parameter and symbol
50
Level 2 105
150
0.05
0.088
0.125
kW
1250
1500
1750
deg
60°
70°
80°
Unit
Level 1
Cutting speed (Vc)
m/min
Feed rate (f)
mm/rev
Laser power (P) Approach angle (θ) Sl No.
Cutting Parameters Speed (S) (m/min)
Feed rate (f) (mm/rev)
Level 3
Response variables Power (P) (W)
Approach angle (θ) (deg)
cutting force Fz, N
Surface roughness (Ra) (µm)
Power consumption W
1
60
0.05
1250
60
91.63
0.7231
91.63
2
150
0.05
1250
60
66.83
0.5559
167.08
3
60
0.125
1250
60
118.9
0.4642
118.90
4
150
0.125
1250
60
76.16
0.4892
190.40
5
60
0.05
1750
60
83.74
0.5192
83.74
6
150
0.05
1750
60
48.98
0.4670
122.45
7
60
0.125
1750
60
108.6
0.4269
108.60
8
150
0.125
1750
60
62.9
0.6399
157.25
9
60
0.05
1250
80
61.25
0.6726
61.25
10
150
0.05
1250
80
51.95
0.4845
129.88
11
60
0.125
1250
80
89.35
0.4975
89.35
12
150
0.125
1250
80
107
0.5613
267.50
13
60
0.05
1750
80
42.36
0.4976
42.36
14
150
0.05
1750
80
62
0.5162
155.00
15
60
0.125
1750
80
78.84
0.5414
78.84
16
150
0.125
1750
80
90.58
0.7619
226.45
17
60
0.0875
1500
70
91.09
0.3489
91.09
18
150
0.0875
1500
70
74.97
0.4163
187.43
19
105
0.05
1500
70
64.51
0.5355
112.89
20
105
0.125
1500
70
98.82
0.5808
172.94
21
105
0.0875
1250
70
97.44
0.5263
170.52
22
105
0.0875
1750
70
75.52
0.4736
132.16
23
105
0.0875
1500
60
65.48
0.4809
114.59
24
105
0.0875
1500
80
62
0.4721
108.50
25
105
0.0875
1500
70
88.45
0.4315
154.79
26
105
0.0875
1500
70
73.31
0.4315
128.29
27
105
0.0875
1500
70
88.45
0.4315
154.79
28
105
0.0875
1500
70
73.31
0.4315
128.29
29
105
0.0875
1500
70
88.45
0.4915
154.79
30
105
0.0875
1500
70
73.31
0.3904
128.29
31
105
0.0875
1500
70
88.45
0.3691
154.79
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3. Response surface method Response surface method initially developed by Box and Wilson [16] is used for experimental optimization of defined cutting parameters in machining domain. RSM is an experimental-modeling based quantitative approach for the evaluation of relationship between the two variables (i.e. dependent and independent variables). In the present investigation, the RSM based second-degree polynomial mathematical models for roughness (Ra) are developed in order to best bit the effect of cutting parameters (S, F, P, θ). The relationship between input parameters and desired machining attributes can be expressed as follows: = ɸ ( S, F, P, θ )
− − − −(1)
Where Ra is the preferred machining attributes and φ is the objective function. The approximation of Ra is proposed by using a second-quadratic mathematical model, which in turn to study the factor interaction effects on machining attributes. In the present work, the RSM based regression model is given by the following: Yk = a0+∑
+ ∑∑
+∑
± eu----(2)
Where ao is constant, bi, bii, and bij represents the second order regression coefficient of linear; quadratic; and cross product terms, respectively. The xi reveals the coded/actual variables. The response coefficients (bi, bii, and bij) shown in the above model can be computed by means of method of least square technique. The cutting power during laser assisted turning is calculated based on measured cutting force as follows: =
60
− − − − − −(3)
Where Pc is cutting power (W), Fz is the cutting force (N), S is speed (m/min), 4. Results and Discussion 4.1. Analysis of variance The analysis of variance (ANOVA) is a standard statistical test that is commonly used in order to determine the significance of the controllable factors on the output responses [17]. It does not analyze the data directly, but determines the percentage of contribution of each factor in determining the variability (variance) of data. The ANOVA table is composed of the sum of squares (SS) and degrees of freedom (DoF) [18]. Tables 2 and 3 illustrate ANOVA results for surface roughness (Ra) and cutting power (Pc), respectively, for a 95% confidence level. In these tables are listed the values of DoF, the sum of squared deviations (SS), mean square (MS) and percentage of contribution (cont %) of each model terms. The main purpose is to analyze the influence of the cutting parameters (θ, P, S, f) on the total variance of the results. The values of ‘P‘ in the models are less than 0.05, indicating that the models are adequate and that the terms have a significant effect on the responses, which are desirable. The ‘model F value’ of 17.82 (Table 2) and 17.42 (Table 3) with its P-value less than 0.0001 imply that the model is statistically significant. From Table 4, the R-squared value of 0.9397 (Table 4) for Ra and 0.9348 (Table 4) for Pc for responses and their R2 closeness to unity describes the model fitness. Therefore, the proposed model is adequate on the basis of the high values of the determination coefficient in representing the process. The fitness of experimental data to the developed mathematical model for responses is revealed by the descriptive agreement between the ‘predicted Rsquared’ and ‘adjusted R-squared’ value observed from Table 4. Parameter B and D are the most substantial model terms that affecting the Ra. The significant model terms for PC include A, B, C D, AB, AD,BD are significant, whose ‘P-values’ values < 0.05 specify the importance of model terms.
Venkatesan/ Materials Today: Proceedings 5 (2018) 11326–11335 Table 2. Results of ANOVA for surface roughness (Ra). Source
SS
Model
0.2561
A:Cutting speed B:Feed rate C:Laser Power D:Apporach angle
0.0063 0.0024 0.0041 0.0031
A2 B2 C2 D2
0.0118 0.0302 0.0063 0.0017
AB AC AD BC BD CD Error Lack-of-fit Pure Error Total
0.0518 0.0277 0.0057 0.0393 0.0118 0.0049 0.0087 0.0731 0.0014 0.2725
DF
F value
14 17.82 Linear: 1 1.55 1 2.18 1 0.79 1 6.17 Square: 1 11.57 1 29.39 1 6.23 1 1.73 2-Way Interaction 1 50.52 1 27.04 1 0.56 1 38.37 1 11.59 1 4.57 16 10 0.59 6 30
p-value Prob>F 0.000
Significance
0.235 0.051 0.385 0.024
Non-Significant Significant Non-Significant Significant
0.000 0.000 0.000 0.024
Significant Significant Significant Significant
0.000 0.000 0.464 0.000 0.000 0.044
Significant Significant Non-significant Significant Significant Significant
0.836
Non-Significant
Table 3. Results of ANOVA for cutting power (Pc). Source
SS
Model
62418.8
A:Cutting speed B:Feed rate C:Laser Power D:Apporach angle
38982.4 10949.7 1793.0 337.05
A2 B2 C2 D2
30.64 39.3 393.9 1959.1
AB AC AD BC BD CD Error Lack-of-fit Pure Error Total
1411.5 132.9 4648.5 148.5 1668.7 160.3 4049.2 2891.8 1203.4 66513.9
DF
F value
14 17.42 Linear: 1 50.52 1 152.31 1 7.02 1 5.89 Square: 1 0.54 1 0.15 1 1.54 1 7.65 2-Way Interaction 1 5.33 1 0.52 1 18.16 1 0.58 1 6.52 1 0.63 16 10 1.44 6 30
p-value Prob>F 0.000
Significance
0.000 0.000 0.018 0.027
Significant Significant Significant Significant
0.987 0.700 0.233 0.014
Non-Significant Non-Significant Non-Significant Significant
0.032 0.482 0.001 0.457 0.021 0.440
Significant Non-Significant Significant Non-Significant Significant Non-Significant
0.339
Non-Significant
Table 4. Statistical model accuracy estimator for surface roughness (Ra) and cutting power (Pc).
Ra
Standard deviation 0.032
Rsquared 0.9397
Adj Rsquared 0.8870
Pred Rsqaured 0.8365
Pc
15.99
0.9384
0.8846
0.7304
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4.2. Regression analysis In this study, the relationship between the cutting parameters and objective functions of surface roughness, Ra, and cutting power (Pc) are modeled using RSM based quadratic regression equations. The final equation in terms of coded factor for surface roughness (Ra) and cutting power (Pc) are given in Eq. (4) and Eq. (5). = 7.054
0.0006
− 27.85 − 0.00475 − 0.0536 − 0.000042 63.7 0.000272 0.0345 0.0004 0.0052 0.0007 − − − −(4)
0.0001 0.0727
Fig. 2. Normality plot for a) Ra b) PC.
Fig. 3. Comparison of test results between experimental and predicted values for a) Ra b) Pc.
The developed model is used to forecast roughness Ra, and cutting power Pc at the particular design points. Figure 2 and Figure 3 illustrate the normality plot and the variances between predicted and measured values of Ra, and Pc respectively. A check on the normal probability plot reveals that 98% of residuals/errors are falling within three sigma limits. Further, it can be seen in Fig. 3 (b) that the actual values are following the predicted values calculated from the model, therefore confirming the reliability of ANOVA. The comparison results prove that predicted values of different controllable parameters are closer to those readings recorded experimentally. 4.3. Interaction effect on response (2D fitted mean plots) 2D interaction plots for the surface roughness are illustrated in Fig. 4. It can be noticed that with an increase in the speed, the amount of surface roughness increases at higher feed rate and laser beam angle. In LAT, an increase in cutting speed at higher feed rate for fixed laser beam resulted in decrease the interaction time between the laser beam and workpiece and as a consequence resulted in more flank wear. Attia et al. [8] show that the surface roughness increases with increase of cutting speed for the fixed laser parameters. As the speed and laser power increases speed at higher laser power of 1750 W, the magnitude of Ra surface roughness is increased more steadily. An increment in laser power with increase of speed resulted in increased tool wear which subsequently upsurges the
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surface roughness. From illustration of feed rate and laser power on surface roughness (Ra), it can be seen that with increase in feed at higher value of laser power the amount of surface roughness increases linearly. At lower laser beam, the value of surface roughness is found to be decreases, however very large variations are found at higher laser beam angle with increment in laser power. Therefore, lower value of laser beam angle and laser power can result in lower value of surface roughness. Anderson et al. [7] found the surface roughness value drops on increment in speed and drops in feed, and decrement in the laser power or material removal temperature. From the plots, the value of Ra is found to be lower of about 0.3933µm at low cutting speed, moderate feed rate and laser power with low laser beam angle.
Fig. 4. Interaction plots for Ra
Fig. 5. Interaction plots for Pc
2D interactions plots for the cutting power are illustrated in Fig. 5. The magnitude of cutting power is increased with increase in speed and feed. While laser power is kept constant, the increasing cutting speed and lessening feed
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rate increases the interaction time allowed for the laser power to be absorbed by the material. This resulted in higher magnitude in force. Additional it is noticed that, the maximum amount of cutting force is generated at a higher level of feed and speed. But, Anderson et al. [7] found that the cutting forces decreased with increasing feed rate and Navas et al. [10] observed that increases with increasing cutting speed. This is contrary to the present study. It is perceived from Fig. 5 when the feed and laser power increase, the amount of the cutting force tends to increases. The maximum cutting force is observed at a low level of laser power and higher feed. The increase in the flank wear at a higher feed rate with lower laser power creates the damage on the cutting edge, producing the abrasion between the tool edge and workpiece surface, thus increases the cutting force. This result is being a contrary with the experimental results attained by Attia et al. [8]. From Fig. 5, the value of cutting power increases with increases in cutting speed and feed rate for laser beam angle of 70°. The cutting power magnitude is higher for low laser power and moderate laser beam angle. From the plots, the value of Pc is found to be lower of about 83 W at low cutting speed, low feed rate and moderate laser power with moderate laser beam angle. 5. Optimal search using desirability function approach The concept based desirability function approach (DFA) for multi-response optimization (MRO) is proposed by Derringer and Suich [19]. The steps involved for optimal search are detailed below: (i) this method utilize an objective function, D(x), so called the desirability function, and transform the estimated individual response into a corresponding scale free composite desirability (di). Based on the type of response criterion i.e. minimization or maximization, the desirability functions are to be selected. The calculated desirability values lies in between 0 to 1. (ii) after computing the desirability value for each response the composite desirability has been computed by geometric mean of the responses. The factor setting with the maximum composite desirability is to be considered as an optimal parameter conditions that simultaneously satisfy the objective functions of each response. Cutting speed, feed rate, laser power and laser beam angle are taken as input parameters. In the present study desirability objective functions are: (i) minimizing the cutting power, and (ii) minimizing the surface roughness. The formula for calculating an overall desirability value is given in Eq. (6). ( )=(
……
) =
− − − (6)
where, di are represents the individual objective/constraint desirability functions, ‘n’ represents number of objectives, wi are represents the weights of each individual objective. Figure 6 show the optimization results. From the results, the highest composite desirability value of 0.776 which corresponding to the optimum levels of cutting speed, feed rate, laser power and laser beam angle are 60 m/min, 0.0875 mm/rev, 1.5 kW, and 67° respectively.
Fig. 6. Optimization plot for overall desirability.
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6. Conclusion This study focuses on the determination of the optimum cutting conditions leading to minimum surface roughness, and cutting power. For that, the case of the laser assisted turning of the Inconel 718 was studied. From the results discussed above, the flowed conclusions could be drawn: Based on the variance test for surface roughness Ra, it’s found that the feed rate and laser beam angle is the most important factor affecting Ra followed by the cutting speed and feed rate (F-value=50.52), feed and laser power (F-value=38.27) respectively. Nevertheless, other terms have no significant effect. The 2D interaction plot of the surface roughness enables to identify and confirm the effect of feed rate and laser beam (F-value=11.59) on the surface roughness evolution. In cutting power, all the input parameters are significant in cutting power, but feed rate is the most significant factor followed by cutting speed and laser beam angle, respectively. Comparison between predicted and measured values of Ra, and Pc proves that predicted values of different controllable factors studied parameters are very close to those readings recorded experimentally, their coefficients of determination R2 are 93.97% and 93.84%, respectively. Single-objective optimization results was concluded that the optimal values for minimizing surface roughness are (S=60 m/min, f=0.0875mm/rev, P =1.5kW and θ=60°). The optimal values for maximizing cutting power was found to be (S=60 m/min, f=0.05mm/rev, P =1.5kW and θ=80°). Multi-objective optimization results was found that the best combination values for minimizing the surface roughness and cutting power are cutting speed are 60 m/min, 0.0875 mm/rev, 1.5 kW, and 67°. References [1] Ezugwu, E. O. and C. I. Okeke, Tribol T. 43 (2) (2000) 332-336. [2] A. Bhatt, H. Attia, R. Vargas, and V. Thomson, Tribol. Int. 43 (5) (2010) 1113-1121. [3] D. Ulutan, and T. Ozel, . Int. J Mach. Tool Manu. 43 (13) (2011)1391-1396. [4] Zhu, Dahu, Xiaoming Zhang, and Han Ding, . Int. J Mach. Tool Manu 64 (2013) 60-77. [5] Kannan, Venkatesan, Ramanujam Radhakrishnan, and Kuppan Palaniyandi, Engineering Review 34 (2) (2014) 75-84. [6] K.Venkatesan, R. Ramanujam, and P. Kuppan. Procedia Eng. 97 (2014)1626-1636. [7] Anderson, Mark, Rahul Patwa, and Yung C. Shin. Int. J Mach. Tool Manu 46 (2006) 1879-1891. [8] Attia, Helmi, Salar Tavakoli, Raul Vargas, and Vincent Thomson. CIRP Ann Manuf. Techn 59 (1) (2010) 83-88. [9] Virginia García Navas, Iban Arriola, Oscar Gonzalo, Josu Leunda. Int. J Mach. Tool Manu. 74 (2013) 19–28. [10] K.Venkatesan, R. Ramanujam, and P. Kuppan. Opt Laser Technol 78 (2016) 10-18. [11] K.Venkatesan and R. Ramanujan. Measurement 89 (2016) 97-108. [12] C. Sanjay, C. Jyothi, Int. J. Adv. Manuf. Techn. 29 (2006) 846–852. [13] V. Upadhyay, P. K. Jain, N. K. Mehta, Measurement, 46(1) (2013) 154-160. [14] M.S Yazdi, A. Khorram, Int. J. Eng. Technol. 2 (2010) 474-380. [15] A. K. Lakshminarayanan, and V. Balasubramanian, Trans. Nonferrous Met. Soc. China, 19 (2009) 9-18 [16] G. E. Box, and K. B. Wilson, Journal of the Royal Statistical Society. Series B (Methodological), 13(1) (1951) 1-45. [17] Bouzid, Lakhdar, Smail Boutabba, Mohamed Athmane Yallese, Salim Belhadi, and Francois Girardin, Int. J. Adv. Manuf. Techn. 74 (5-8) (2014) 879-891. [18] I Meddour, M. A. Yallese , R. Khattabi, M. Elbah and L. Boulanouar, Int. J. Adv. Manuf. Techn.77 (5-8) (2015) 1387-1399. [19] Derringer, George. J Qual. Technol. 12 (1980) 214-219.