- Email: [email protected]
Al2O3 MMC Using RSM Approach
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 5 (2018) 14265–14272
www.materialstoday.com/proceedings
ICAFM_2017
Optimization of Machining Parameters for CNC Turning of Al/Al2O3 MMC Using RSM Approach M.Nataraja, K.Balasubramaniana*, D.Palanisamyb b
a Department of Mechanical Engineering, Government College of Technology, Coimbatore-641013, INDIA Department of Mechanical Engineering, Adhi College of Engineering and Technology, Chennai-631605, INDIA
Abstract This paper discusses the influence of cutting variables such as feed, cutting speed and depth of cut at work-tool interface zone temperature and surface finish while machining aluminium alloy LM6 reinforced with Al2O3 metal matrix composites. Response surface methodology with central composite rotatable design matrix was employed to optimise and analyse the cutting variables. Second order regression models were developed for predicting the output responses and the adequacy of the developed model was tested using analysis of variance (ANOVA). ANOVA results revealed that the feed and depth of cut were the major influencing parameters for the work-tool interface temperature and cutting speed and feed were prominent influential parameters in surface roughness. The optimal parameters for multiple responses were arrived for the specified range of input parameters using overall desirability index. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAFM’17. Keywords: Work-Tool interface temperature; Design of Experiments; RSM; desirability index; LM6; metal matrix composites;
1. Introduction Aluminium based composites have gained significant attention in the manufacturing of automobile and aerospace components due to its improved properties such as higher strength to weight ratio, resistance to corrosion, wear, fatigue and elevated temperature features. Generally, the fabrication of the Aluminium Metal Matrix Composite (AMMC) was done by stir casting process because of its simplicity, flexibility and it was an economical
* Corresponding author. Tel.: +91 9894444578; E-mail address:[email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAFM’17.
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process in addition to its applicability to large of volume production [1]. Preheated reinforcement particles introduced into the molten metal and stirred thoroughly with the help of stirrer to achieve homogeneous mixing with matrix alloy for attaining better mechanical properties [2]. In casting process the components are produced near net shape, however, the machining process is required to maintain dimensional accuracy of the component. The machining of AMMCs was a challenging task since the hard ceramic particulates dispersed in it [3]. It was reported that cutting speed is the most dominant parameter that affects the tool wear in machining of MMC [4]. In turning process, the selections of optimal process parameters were necessary for superior surface finish [5]. The performance of the turning process can be evaluated by performance indicators such as vibration, machining forces, acoustic emission, and temperature. It was also stated that temperature is the most important parameter that directly affects the performance of the work-piece and cutting tool [6]. The unfavourable effects of high temperature and thermal shocks in machining process were plastic deformation of the tool material, rapid tool wear, built-up-edge formation and thermal fracturing of the cutting edges [7]. The possible unfavourable effects of high cutting temperature in machining process affect the dimensional accuracy of the work-piece. It is due to expansion, contraction and thermal distortion during and after machining. The Response Surface Methodology (RSM) is one of the design of experiment techniques that has been used to find optimal machining parameters effectively in machining process. It was reported that RSM technique seemed to have an edge over the Taguchi’s technique [8]. There are different temperature measurement techniques available to acquire temperature during machining. The K-type thermocouples were chosen by considering measurement response time for measuring work-tool interface temperature [9] and also the tool-work thermocouple technique was used for measuring chip-tool interface temperature [10]. The tool-tip temperature study during machining was helpful in eliminating cutting fluid that causes health and environmental problems [11]. In this present investigation, the influence of cutting variables in turning such as feed, cutting speed, and depth of cut on work-tool interface temperature and surface roughness (Ra, Rq and Rz) under dry cutting environment are analysed. The LM6/ Al2O3 MMC has been fabricated by using stir casting method. The experiments have been conducted based on central composite rotatable design (CCRD) and second order regression models were established between the independent parameters for the work- tool interface temperature and surface roughness parameters. 2. Materials and method The metal matrix composite was fabricated using LM6 as matrix material and aluminium oxide (Al2O3) as reinforcement in stir casting process. The matrix LM6 was melted in a crucible with the help of induction furnace of stir casting processing set-up. The melt was maintained at 780°C and a preheated stirrer with a motor designed to vary its output rpm was placed below the surface of the molten metal and rotated with 400 rpm. This action of stirring creates vortex and maintains better reinforcement distribution in the matrix alloy [12]. The aluminium oxide reinforcement preheated up to 450°C in a muffle furnace was introduced into the melt and stirred up for 10 minutes. An argon gas environment was used as a protective shroud on the melt surface to protect chemical reaction of the molten metal with atmosphere. Magnesium 1wt% added into the melt to improve the wettability of the matrix and reinforcement [13] Degassing agent in tablet form was also introduced into the melt and then the composite mixture was poured into a mould made of low-carbon steel. The LM6/Al2O3 MMC samples were processed in a CNC turning machine to optimise the cutting variables and investigate the effect of these variables on work-tool interface temperature and surface roughness parameters Ra, Rq and Rz for the finish turning process. The tests were conducted in CNC lathe with the left-hand tool holder and CNMG 120404 carbide insert under dry machining environment. The process parameters range in two levels used for conducting the experimental study is presented in Table 1. Table 1 Experimental Parameters Parameters Cutting Speed in m/min Feed mm/rev Depth of Cut in mm
Levels Minimum 125 0.05 0.25
Maximum 175 0.1 0.75
Author name / Materials Today: Proceedings 5 (2018) 14265–14272
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The CCRD, an experimental design, was prepared using design expert software. The randomly ordered experimental plan for 20 runs with cutting variables and experimental data was presented in Table 2. Table 2. Experimental data Speed Std Run m/min (A) 11 3 10 7 8 6 13 15 19 12 18 2 16 14 9 17 1 5 4 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
150 125 192 125 175 175 150 150 150 150 150 175 150 150 108 150 125 125 175 150
Feed in mm/Rev (B)
Depth of Cut Mm (C)
Work-Tool Interface temperature
Roughness Ra micro m
Roughness Rq micro m
Roughness micro m
0.033 0.100 0.075 0.100 0.100 0.050 0.075 0.075 0.075 0.117 0.075 0.050 0.075 0.075 0.075 0.075 0.050 0.050 0.100 0.075
0.50 0.25 0.50 0.75 0.75 0.75 0.08 0.50 0.50 0.50 0.50 0.25 0.50 0.92 0.50 0.50 0.25 0.75 0.25 0.50
96.80 96.40 105.20 104.80 113.28 100.50 89.40 92.80 95.70 107.40 90.30 94.20 93.80 98.40 110.30 95.30 96.80 108.02 106.50 89.40
1.88 2.50 1.32 2.58 2.13 1.73 1.62 2.24 2.29 2.63 2.17 1.45 2.21 2.26 2.12 2.28 1.92 2.33 1.86 2.17
2.92 3.45 2.84 3.62 3.23 2.88 2.96 3.13 3.14 3.52 3.07 2.69 3.14 3.24 3.47 3.10 3.19 3.28 2.87 3.04
15.78 21.14 15.02 23.21 16.72 16.52 18.20 19.05 20.05 21.28 20.40 15.89 19.70 20.40 19.75 21.06 16.46 20.80 18.82 18.32
Rz
RSM technique was used to formulate the second order regression model for predicting the output responses work-tool interface temperature and surface roughness (Ra, Rq, Rz). These regression models were establishing the correlation between the cutting variables and the output responses. The relationship between the machining process parameters and the response is given in equation (1). Y
= f (A, B, C) + ε
(1)
Where Y is the output variable, which is the function of independent variables of turning process such as feed, cutting speed, depth of cut and ε is the error. 3. Result and discussions 3.1 Effect of turning process parameters on Work- tool interface temperature The interactive effect of work-tool interface temperature was analysed by using surface plots presented in Fig. 1 (a-c). The figure 1 (a) showed that the responses have a curvilinear relationship with the cutting speed and feed. At low speed and low feed rate the work-tool interface temperature was high this was due to the fact that at low speed, high ploughing force was required and this will generate more heat and at high cutting speed and high feed low shear energy is required for cutting and volume of material removal was high and the generated heat also was high. From the Fig. 1 (b) there was a curvilinear relationship for cutting speed with roughness parameter Ra and linear relationship for depth of cut with roughness Ra. This was due to an increase in the depth of cut increases shear force or ploughing force and increased work-tool interface temperature. In Fig. 1 (c) showed that the feed having curvilinear relationship and depth of cut having linear relationship with the response.
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value
115
110
Work-Tool Interface Temp. (Centigrade)
Work-Tool Interface Temp. (Centigrade)
115
105 100 95 90 85
0.10 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.06 B: Feed (mm/rev) 0.06 0.05
175.00 170.00 165.00 160.00 155.00 150.00 145.00 140.00 135.00 A: Speed (m/min) 130.00 125.00
110 105 100 95 90 85
0.75 0.65 0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
(a)
175.00 170.00 165.00 160.00 155.00 150.00 145.00 140.00 135.00 A: Speed (m/min) 130.00 125.00
(b) Work-Tool Interface Temp. (Centigrade)
115 110 105 100 95 90 85
0.75 0.65 0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
0.10 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.06 B: Feed (mm/rev) 0.06 0.05
(c) Fig.1. Surface plots of work tool interface temperature 3.2 Effect of turning process parameters on surface roughness Ra The surface roughness parameters in turning process have been influenced by a number of factors such as cutting speed and feed including the interactive effect of these parameters. The interactive effect of turning process parameters on surface roughness was studied with the help of surface plots presented in Fig. 2 (a-c). The Fig. 2 (a) and (b) showed that when turning LM6/Al2O3 MMC at low cutting speed 125 m/min the surface roughness notices were high and it decreased as the speed increased to 175 m/min. At low cutting speed 125m/min there was high shear energy or ploughing force required and also the friction in the flow of work material on the cutting edge is high. All these effects were deteriorating the surface parameters at low speed. During machining at high speed 175 m/min it is found that the fabricated LM6/Al2O3 MMC have low surface roughness and it is due to the lesser deformation of the work material and in this high speed the formation of BUE was eliminated. This was due to the heat generated at the tool work interface during machining at high cutting speed that decreases the sticking property of work metal with tool thus reduces value of surface roughness. The Fig 2 (c) presented the interactive effect of feed and depth of cut for cutting speed 150 m/min. At low feed and low depth of cut, low surface roughness was achieved. From this analysis, it was concluded that the better surface finish was obtained at the parameter level of higher cutting speed, low feed rate and low depth of cut. The same types of analyses were done for roughness parameters Rq and Rz. The curvature and roughness value surface plots for these parameters were different in magnitude but the inference drawn in the surface roughness Ra analysis is also suitable for the roughness parameters Rq and Rz.
Author name / Materials Today: Proceedings 5 (2018) 14265–14272
2.8
2.8
2.6
2.6
2.4
Roughness Ra (Micro m)
2.4
Roughness Ra (Micro m )
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2.2 2 1.8 1.6 1.4 1.2
2.2 2 1.8 1.6 1.4 1.2
0.75 0.65
0.10 175.00 0.10 170.00 0.09 165.00 0.09 160.00 0.08 155.00 0.08 150.00 0.07 145.00 0.07 140.00 0.06 135.00 B: Feed (mm/rev) A: Speed (m/min) 0.06 130.00 0.05 125.00
0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
(a)
175.00 170.00 165.00 160.00 155.00 150.00 145.00 140.00 135.00 A: Speed (m/min) 130.00 125.00
(b) 2.8 2.6
Roughness Ra (Micro m)
2.4 2.2 2 1.8 1.6 1.4 1.2
0.75 0.65 0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
0.10 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.06 B: Feed (mm/rev) 0.06 0.05
(c) Fig. 2 Surface plots of surface roughness 3.3 Evolution of mathematical model The regression model fitted for output responses were obtained and represented in the equations (2) – (5). T = 358.5936-2.96994(A)-1545.36(B) +28.67183(C) +5.74(AB)-0.1308(AC)-46.8(BC) + 0.008682 (A2) +5485.525 (B2) + 8.469046 (C2) (2) Ra=0.064568 (A) + 4.789037 (B) + 2.224906 (C) -0.00382 (AB) + 0.001094 (AC) + -6.6576 (BC) 0.00025 (A2) + 50.66429 (B2) + -1.27102 (C2)-3.05724 (3) Rq=5.119869-0.01797 (A) -3.56943 (B) -0.86815(C) -0.01403 (AB) + 0.006033 (AC) + 5.1726(BC) + 2.47E-05 (A2) + 62.17723 (B2) -0.0508 (C2) (4) Rz= 19.75129-1.58261 (A) + 1.425794 (B) + 0.632499(C)-0.49525 (AB) -0.98475 (AC) -0.62525 (BC) -0.76251 (A2) -0.35734 (B2) -0.0851 (C2) (5) 3.4 ANOVA analysis The ANOVA analysis results for the surface roughness Ra was presented in table 3. The low p-value (<0.05) indicates the statistical significance of the corresponding response. The F-value indicates the contribution of the parameter influence for the output response. From the Table 3 the factor cutting speed was the most influence parameter, feed and depth of cut were also significant parameters for the surface roughness and the interaction of cutting speed with feed contributes influence of output response. These finding were in close agreement with the research findings presented in references [14, 15]. The regression co-efficient R2 for all output responses falls within the range 0.93 which provides evidence that the model developed from the experimental study was reasonably accurate and the relationships generated were satisfactory.
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Table 3 ANOVA table for the response surface roughness Ra Source Sum of DOF Mean Squares Square Model 2.309762 9 0.257 A-Speed 0.900757 1 0.901 B-Feed 0.614703 1 0.615 C-Depth of Cut 0.326629 1 0.327 AB 4.57E-05 1 0.000 AC 0.000374 1 0.000 BC 0.013851 1 0.014 A^2 0.352787 1 0.353 B^2 0.01445 1 0.014 C^2 0.090943 1 0.091
F Value 33.707 118.306 80.735 42.900 0.006 0.049 1.819 46.335 1.898 11.945
p-value Prob> F 0.000003 0.000001 0.000004 0.000065 0.939777 0.829119 0.207157 0.000047 0.198365 0.006163
significant
3.5 Optimisation of Cutting variables The optimal combinations of the cutting variables for better output responses were obtained through numerical and graphical optimisation. 3.5.1
Numerical optimisation
The numerical optimisation, being a very significant part of RSM provides the best as well as various solutions for a combination of explanatory variables to achieve the most optimal responses. The constraints are set such that the software optimises within the parameters limits and according to their importance and minimises the response variables. The selection of the optimal value of the parameters was based on overall desirability index. The overall desirability index value for this analysis was 0.934, the individual desirability for the responses range from 0.858 and it shows that the optimal values yields a good set of cutting variables. 3.5.2
Graphical optimisation by overlay plot
The Fig. 3 showed the overlay plot for the responses and the yellow coloured area defines the optimal process parameters locations. From the overlay plot, the best combinational values of machining process parameters were Cutting speed is equal to 175 m/min, Feed is equal to 0.05 mm/rev and depth of cut is equal to 0.25 mm Design-Expert® Software Factor Coding: Actual Overlay Plot
Overlay Plot
0.10
Work-Tool Interface Temp. Roughness Ra Roughness Rq Roughness Rz Design Points
0.10 0.09
Actual Factor C: Depth of Cut = 0.25
B: Feed (mm/rev)
0.09
X1 = A: Speed X2 = B: Feed
0.08 0.08 0.07 0.07
Work-Tool Interface 92.79 Roughness Ra: 1.35 Roughness Rq: 2.69 Roughness Rz: 15.76 X1 175.00 X2 0.05
0.06 0.06 0.05
175.00
170.00
165.00
160.00
155.00
150.00
145.00
140.00
135.00
130.00
125.00
A: Speed (m/min)
Fig.3 Overlay plot
Author name / Materials Today: Proceedings 5 (2018) 14265–14272
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3.6 Confirmation test The confirmation tests were conducted to validate the mathematical model developed using regression equation and the average values of the results were presented in Table 4. The predicted values were close to the experimental data with acceptable error % and hence the mathematical model will be efficient to predict the responses. Table 4 Confirmation test results Exp. No
Cutting speed
1
165
Feed mm/rev
m/min 0.07
Depth of cut
Work-tool temperature
interface Roughness Ra (micro m)
mm
Predicted
Exp.
% error
Predicted
Exp.
% error
0.4
92.79
94.68
1.99%
1.89
1.94
2.3%
2
175
0.05
0.25
92.79
95.6
2.94%
1.35
1.36
0.9%
3
130
0.1
0.75
104.17
102.7
-1.43%
2.60
2.54
-2.6%
4
185
0.075
0.5
103.48
104.7
1.16%
1.56
1.58
1.5%
5
175
0.1
0.75
111.09
114.2
2.72%
2.09
2.03
-3.2%
4. Conclusions In this study, the surface roughness Ra, Rq and Rz and temperature at work-tool interface were analysed by optimising feed, cutting speed, and depth of cut. The following conclusions were drawn from the experimental work. • The work-tool interface temperature gets affected if cutting speed and feed are not chosen correctly. The work-tool temperature will be maximum when cutting speed is 175 m/min, feed is 0.1 mm/rev and depth of cut is 0.5 mm. • The surface roughness is influenced by feed rate and cutting speed. The best surface finish was obtained for cutting speed =175 mm/min, feed rate = 0.05 mm/rev and depth of cut = 0.25 mm. • The confirmation results of the developed models predicted maximum error of 2.94 % for the specified experimental set-up which proved that the models are good enough to predict the responses. • The regression models arrived for optimal parameters provide useful guidelines for fabrication and machining aspects of the aerospace, automobile and military applications. References [1] Y. LI, Q. lin LI, D. LI, W. LIU, and G. gang SHU, “Fabrication and characterization of stir casting AA6061-31%B4C composite,” Trans. Nonferrous Met. Soc. China (English Ed., vol. 26, no. 9, pp. 2304–2312, 2016. [2] J. J. Moses, I. Dinaharan, and S. J. Sekhar, “Prediction of influence of process parameters on tensile strength of AA6061/TiC aluminum matrix composites produced using stir casting,” Trans. Nonferrous Met. Soc. China (English Ed., vol. 26, no. 6, pp. 1498–1511, 2016. [3] M. Nataraj and K. Balasubramanian, “Parametric optimization of CNC turning process for hybrid metal matrix composite,” Int. J. Adv. Manuf. Technol. DOI 10.1007/s00170-016-8780-4 [4] A.Srinivasan, R. M. Arunachalam, S. Ramesh, and J. S. Senthilkumaar, “Machining Performance Study on Metal Matrix Composites-A Response Surface Methodology Approach., American Journal of Applied Sciences vol. 9, no. 4, pp. 478–483, 2012. [5] A. K. Sahoo and S. Pradhan, “Modeling and optimization of Al/SiCp MMC machining using Taguchi approach,” Meas. J. Int. Meas. Confed., vol. 46, no. 9, pp. 3064–3072, 2013. [6] A. Kannan, K. Esakkiraja, and M. Nataraj, “Modeling and Analysis for Cutting Temperature in Turning of Aluminium 6063 Using Response Surface Methodology,” vol. 9, no. 4, pp. 59–64, 2013.
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[7] M. Mia and N. R. Dhar, “Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method,” Int. J. Adv. Manuf. Technol., pp. 1–15, 2016. [8] A. Aggarwal, H. Singh, P. Kumar, and M. Singh, “Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique — A,” vol. 10, no. 1997, pp. 373–384, 2007. [9] D. O’Sullivan and M. Cotterell, “Temperature measurement in single point turning,” J. Mater. Process. Technol., vol. 118, no. 1–3, pp. 301–308, 2001. [10] L. B. Abhang and M. Hameedullah, “Chip-Tool Interface Temperature Prediction Model for Turning Process,” Int. J. Eng. Sci. Technol., vol. 2, no. 4, pp. 382–393, 2010. [11] B. Davoodi and A. H. Tazehkandi, “Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid,” J. Clean. Prod., vol. 68, pp. 234–242, 2014. [12] J. Hashim, L. Looney, M. S. J. Hashmi, “Metal matrix composites: production by the stir casting method” J. Mater. Process. Technol., vol. 92–93, pp. 1–7, 1999. [13] J. Hashim, L. Looney, and M. S. J. Hashmi, “The enhancement of wettability of SiC particles in cast aluminium matrix composites,” J. Mater. Process. Technol., vol. 119, no. 1–3, pp. 329–335, 2001. [14] T.Sasimurugan and K.Palanikumar “Analysis of the Machining Characteristics on Surface Roughness of a Hybrid,” J. Minerals & Mater Charact.&Engg, vol. 10, no. 13, pp. 1213–1224, 2011. [15] P. Senthil, T. Selvaraj, and K. Sivaprasad, “Influence of turning parameters on the machinability of homogenized Al-Cu/TiB2 in situ metal matrix composites,” Int. J. Adv. Manuf. Technol., vol. 67, no. 5–8, pp. 1589–1596, 2013.
ScienceDirect Materials Today: Proceedings 5 (2018) 14265–14272
www.materialstoday.com/proceedings
ICAFM_2017
Optimization of Machining Parameters for CNC Turning of Al/Al2O3 MMC Using RSM Approach M.Nataraja, K.Balasubramaniana*, D.Palanisamyb b
a Department of Mechanical Engineering, Government College of Technology, Coimbatore-641013, INDIA Department of Mechanical Engineering, Adhi College of Engineering and Technology, Chennai-631605, INDIA
Abstract This paper discusses the influence of cutting variables such as feed, cutting speed and depth of cut at work-tool interface zone temperature and surface finish while machining aluminium alloy LM6 reinforced with Al2O3 metal matrix composites. Response surface methodology with central composite rotatable design matrix was employed to optimise and analyse the cutting variables. Second order regression models were developed for predicting the output responses and the adequacy of the developed model was tested using analysis of variance (ANOVA). ANOVA results revealed that the feed and depth of cut were the major influencing parameters for the work-tool interface temperature and cutting speed and feed were prominent influential parameters in surface roughness. The optimal parameters for multiple responses were arrived for the specified range of input parameters using overall desirability index. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAFM’17. Keywords: Work-Tool interface temperature; Design of Experiments; RSM; desirability index; LM6; metal matrix composites;
1. Introduction Aluminium based composites have gained significant attention in the manufacturing of automobile and aerospace components due to its improved properties such as higher strength to weight ratio, resistance to corrosion, wear, fatigue and elevated temperature features. Generally, the fabrication of the Aluminium Metal Matrix Composite (AMMC) was done by stir casting process because of its simplicity, flexibility and it was an economical
* Corresponding author. Tel.: +91 9894444578; E-mail address:[email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAFM’17.
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process in addition to its applicability to large of volume production [1]. Preheated reinforcement particles introduced into the molten metal and stirred thoroughly with the help of stirrer to achieve homogeneous mixing with matrix alloy for attaining better mechanical properties [2]. In casting process the components are produced near net shape, however, the machining process is required to maintain dimensional accuracy of the component. The machining of AMMCs was a challenging task since the hard ceramic particulates dispersed in it [3]. It was reported that cutting speed is the most dominant parameter that affects the tool wear in machining of MMC [4]. In turning process, the selections of optimal process parameters were necessary for superior surface finish [5]. The performance of the turning process can be evaluated by performance indicators such as vibration, machining forces, acoustic emission, and temperature. It was also stated that temperature is the most important parameter that directly affects the performance of the work-piece and cutting tool [6]. The unfavourable effects of high temperature and thermal shocks in machining process were plastic deformation of the tool material, rapid tool wear, built-up-edge formation and thermal fracturing of the cutting edges [7]. The possible unfavourable effects of high cutting temperature in machining process affect the dimensional accuracy of the work-piece. It is due to expansion, contraction and thermal distortion during and after machining. The Response Surface Methodology (RSM) is one of the design of experiment techniques that has been used to find optimal machining parameters effectively in machining process. It was reported that RSM technique seemed to have an edge over the Taguchi’s technique [8]. There are different temperature measurement techniques available to acquire temperature during machining. The K-type thermocouples were chosen by considering measurement response time for measuring work-tool interface temperature [9] and also the tool-work thermocouple technique was used for measuring chip-tool interface temperature [10]. The tool-tip temperature study during machining was helpful in eliminating cutting fluid that causes health and environmental problems [11]. In this present investigation, the influence of cutting variables in turning such as feed, cutting speed, and depth of cut on work-tool interface temperature and surface roughness (Ra, Rq and Rz) under dry cutting environment are analysed. The LM6/ Al2O3 MMC has been fabricated by using stir casting method. The experiments have been conducted based on central composite rotatable design (CCRD) and second order regression models were established between the independent parameters for the work- tool interface temperature and surface roughness parameters. 2. Materials and method The metal matrix composite was fabricated using LM6 as matrix material and aluminium oxide (Al2O3) as reinforcement in stir casting process. The matrix LM6 was melted in a crucible with the help of induction furnace of stir casting processing set-up. The melt was maintained at 780°C and a preheated stirrer with a motor designed to vary its output rpm was placed below the surface of the molten metal and rotated with 400 rpm. This action of stirring creates vortex and maintains better reinforcement distribution in the matrix alloy [12]. The aluminium oxide reinforcement preheated up to 450°C in a muffle furnace was introduced into the melt and stirred up for 10 minutes. An argon gas environment was used as a protective shroud on the melt surface to protect chemical reaction of the molten metal with atmosphere. Magnesium 1wt% added into the melt to improve the wettability of the matrix and reinforcement [13] Degassing agent in tablet form was also introduced into the melt and then the composite mixture was poured into a mould made of low-carbon steel. The LM6/Al2O3 MMC samples were processed in a CNC turning machine to optimise the cutting variables and investigate the effect of these variables on work-tool interface temperature and surface roughness parameters Ra, Rq and Rz for the finish turning process. The tests were conducted in CNC lathe with the left-hand tool holder and CNMG 120404 carbide insert under dry machining environment. The process parameters range in two levels used for conducting the experimental study is presented in Table 1. Table 1 Experimental Parameters Parameters Cutting Speed in m/min Feed mm/rev Depth of Cut in mm
Levels Minimum 125 0.05 0.25
Maximum 175 0.1 0.75
Author name / Materials Today: Proceedings 5 (2018) 14265–14272
14267
The CCRD, an experimental design, was prepared using design expert software. The randomly ordered experimental plan for 20 runs with cutting variables and experimental data was presented in Table 2. Table 2. Experimental data Speed Std Run m/min (A) 11 3 10 7 8 6 13 15 19 12 18 2 16 14 9 17 1 5 4 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
150 125 192 125 175 175 150 150 150 150 150 175 150 150 108 150 125 125 175 150
Feed in mm/Rev (B)
Depth of Cut Mm (C)
Work-Tool Interface temperature
Roughness Ra micro m
Roughness Rq micro m
Roughness micro m
0.033 0.100 0.075 0.100 0.100 0.050 0.075 0.075 0.075 0.117 0.075 0.050 0.075 0.075 0.075 0.075 0.050 0.050 0.100 0.075
0.50 0.25 0.50 0.75 0.75 0.75 0.08 0.50 0.50 0.50 0.50 0.25 0.50 0.92 0.50 0.50 0.25 0.75 0.25 0.50
96.80 96.40 105.20 104.80 113.28 100.50 89.40 92.80 95.70 107.40 90.30 94.20 93.80 98.40 110.30 95.30 96.80 108.02 106.50 89.40
1.88 2.50 1.32 2.58 2.13 1.73 1.62 2.24 2.29 2.63 2.17 1.45 2.21 2.26 2.12 2.28 1.92 2.33 1.86 2.17
2.92 3.45 2.84 3.62 3.23 2.88 2.96 3.13 3.14 3.52 3.07 2.69 3.14 3.24 3.47 3.10 3.19 3.28 2.87 3.04
15.78 21.14 15.02 23.21 16.72 16.52 18.20 19.05 20.05 21.28 20.40 15.89 19.70 20.40 19.75 21.06 16.46 20.80 18.82 18.32
Rz
RSM technique was used to formulate the second order regression model for predicting the output responses work-tool interface temperature and surface roughness (Ra, Rq, Rz). These regression models were establishing the correlation between the cutting variables and the output responses. The relationship between the machining process parameters and the response is given in equation (1). Y
= f (A, B, C) + ε
(1)
Where Y is the output variable, which is the function of independent variables of turning process such as feed, cutting speed, depth of cut and ε is the error. 3. Result and discussions 3.1 Effect of turning process parameters on Work- tool interface temperature The interactive effect of work-tool interface temperature was analysed by using surface plots presented in Fig. 1 (a-c). The figure 1 (a) showed that the responses have a curvilinear relationship with the cutting speed and feed. At low speed and low feed rate the work-tool interface temperature was high this was due to the fact that at low speed, high ploughing force was required and this will generate more heat and at high cutting speed and high feed low shear energy is required for cutting and volume of material removal was high and the generated heat also was high. From the Fig. 1 (b) there was a curvilinear relationship for cutting speed with roughness parameter Ra and linear relationship for depth of cut with roughness Ra. This was due to an increase in the depth of cut increases shear force or ploughing force and increased work-tool interface temperature. In Fig. 1 (c) showed that the feed having curvilinear relationship and depth of cut having linear relationship with the response.
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value
115
110
Work-Tool Interface Temp. (Centigrade)
Work-Tool Interface Temp. (Centigrade)
115
105 100 95 90 85
0.10 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.06 B: Feed (mm/rev) 0.06 0.05
175.00 170.00 165.00 160.00 155.00 150.00 145.00 140.00 135.00 A: Speed (m/min) 130.00 125.00
110 105 100 95 90 85
0.75 0.65 0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
(a)
175.00 170.00 165.00 160.00 155.00 150.00 145.00 140.00 135.00 A: Speed (m/min) 130.00 125.00
(b) Work-Tool Interface Temp. (Centigrade)
115 110 105 100 95 90 85
0.75 0.65 0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
0.10 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.06 B: Feed (mm/rev) 0.06 0.05
(c) Fig.1. Surface plots of work tool interface temperature 3.2 Effect of turning process parameters on surface roughness Ra The surface roughness parameters in turning process have been influenced by a number of factors such as cutting speed and feed including the interactive effect of these parameters. The interactive effect of turning process parameters on surface roughness was studied with the help of surface plots presented in Fig. 2 (a-c). The Fig. 2 (a) and (b) showed that when turning LM6/Al2O3 MMC at low cutting speed 125 m/min the surface roughness notices were high and it decreased as the speed increased to 175 m/min. At low cutting speed 125m/min there was high shear energy or ploughing force required and also the friction in the flow of work material on the cutting edge is high. All these effects were deteriorating the surface parameters at low speed. During machining at high speed 175 m/min it is found that the fabricated LM6/Al2O3 MMC have low surface roughness and it is due to the lesser deformation of the work material and in this high speed the formation of BUE was eliminated. This was due to the heat generated at the tool work interface during machining at high cutting speed that decreases the sticking property of work metal with tool thus reduces value of surface roughness. The Fig 2 (c) presented the interactive effect of feed and depth of cut for cutting speed 150 m/min. At low feed and low depth of cut, low surface roughness was achieved. From this analysis, it was concluded that the better surface finish was obtained at the parameter level of higher cutting speed, low feed rate and low depth of cut. The same types of analyses were done for roughness parameters Rq and Rz. The curvature and roughness value surface plots for these parameters were different in magnitude but the inference drawn in the surface roughness Ra analysis is also suitable for the roughness parameters Rq and Rz.
Author name / Materials Today: Proceedings 5 (2018) 14265–14272
2.8
2.8
2.6
2.6
2.4
Roughness Ra (Micro m)
2.4
Roughness Ra (Micro m )
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2.2 2 1.8 1.6 1.4 1.2
2.2 2 1.8 1.6 1.4 1.2
0.75 0.65
0.10 175.00 0.10 170.00 0.09 165.00 0.09 160.00 0.08 155.00 0.08 150.00 0.07 145.00 0.07 140.00 0.06 135.00 B: Feed (mm/rev) A: Speed (m/min) 0.06 130.00 0.05 125.00
0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
(a)
175.00 170.00 165.00 160.00 155.00 150.00 145.00 140.00 135.00 A: Speed (m/min) 130.00 125.00
(b) 2.8 2.6
Roughness Ra (Micro m)
2.4 2.2 2 1.8 1.6 1.4 1.2
0.75 0.65 0.55 0.45
C: Depth of Cut (mm)
0.35 0.25
0.10 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.06 B: Feed (mm/rev) 0.06 0.05
(c) Fig. 2 Surface plots of surface roughness 3.3 Evolution of mathematical model The regression model fitted for output responses were obtained and represented in the equations (2) – (5). T = 358.5936-2.96994(A)-1545.36(B) +28.67183(C) +5.74(AB)-0.1308(AC)-46.8(BC) + 0.008682 (A2) +5485.525 (B2) + 8.469046 (C2) (2) Ra=0.064568 (A) + 4.789037 (B) + 2.224906 (C) -0.00382 (AB) + 0.001094 (AC) + -6.6576 (BC) 0.00025 (A2) + 50.66429 (B2) + -1.27102 (C2)-3.05724 (3) Rq=5.119869-0.01797 (A) -3.56943 (B) -0.86815(C) -0.01403 (AB) + 0.006033 (AC) + 5.1726(BC) + 2.47E-05 (A2) + 62.17723 (B2) -0.0508 (C2) (4) Rz= 19.75129-1.58261 (A) + 1.425794 (B) + 0.632499(C)-0.49525 (AB) -0.98475 (AC) -0.62525 (BC) -0.76251 (A2) -0.35734 (B2) -0.0851 (C2) (5) 3.4 ANOVA analysis The ANOVA analysis results for the surface roughness Ra was presented in table 3. The low p-value (<0.05) indicates the statistical significance of the corresponding response. The F-value indicates the contribution of the parameter influence for the output response. From the Table 3 the factor cutting speed was the most influence parameter, feed and depth of cut were also significant parameters for the surface roughness and the interaction of cutting speed with feed contributes influence of output response. These finding were in close agreement with the research findings presented in references [14, 15]. The regression co-efficient R2 for all output responses falls within the range 0.93 which provides evidence that the model developed from the experimental study was reasonably accurate and the relationships generated were satisfactory.
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Table 3 ANOVA table for the response surface roughness Ra Source Sum of DOF Mean Squares Square Model 2.309762 9 0.257 A-Speed 0.900757 1 0.901 B-Feed 0.614703 1 0.615 C-Depth of Cut 0.326629 1 0.327 AB 4.57E-05 1 0.000 AC 0.000374 1 0.000 BC 0.013851 1 0.014 A^2 0.352787 1 0.353 B^2 0.01445 1 0.014 C^2 0.090943 1 0.091
F Value 33.707 118.306 80.735 42.900 0.006 0.049 1.819 46.335 1.898 11.945
p-value Prob> F 0.000003 0.000001 0.000004 0.000065 0.939777 0.829119 0.207157 0.000047 0.198365 0.006163
significant
3.5 Optimisation of Cutting variables The optimal combinations of the cutting variables for better output responses were obtained through numerical and graphical optimisation. 3.5.1
Numerical optimisation
The numerical optimisation, being a very significant part of RSM provides the best as well as various solutions for a combination of explanatory variables to achieve the most optimal responses. The constraints are set such that the software optimises within the parameters limits and according to their importance and minimises the response variables. The selection of the optimal value of the parameters was based on overall desirability index. The overall desirability index value for this analysis was 0.934, the individual desirability for the responses range from 0.858 and it shows that the optimal values yields a good set of cutting variables. 3.5.2
Graphical optimisation by overlay plot
The Fig. 3 showed the overlay plot for the responses and the yellow coloured area defines the optimal process parameters locations. From the overlay plot, the best combinational values of machining process parameters were Cutting speed is equal to 175 m/min, Feed is equal to 0.05 mm/rev and depth of cut is equal to 0.25 mm Design-Expert® Software Factor Coding: Actual Overlay Plot
Overlay Plot
0.10
Work-Tool Interface Temp. Roughness Ra Roughness Rq Roughness Rz Design Points
0.10 0.09
Actual Factor C: Depth of Cut = 0.25
B: Feed (mm/rev)
0.09
X1 = A: Speed X2 = B: Feed
0.08 0.08 0.07 0.07
Work-Tool Interface 92.79 Roughness Ra: 1.35 Roughness Rq: 2.69 Roughness Rz: 15.76 X1 175.00 X2 0.05
0.06 0.06 0.05
175.00
170.00
165.00
160.00
155.00
150.00
145.00
140.00
135.00
130.00
125.00
A: Speed (m/min)
Fig.3 Overlay plot
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3.6 Confirmation test The confirmation tests were conducted to validate the mathematical model developed using regression equation and the average values of the results were presented in Table 4. The predicted values were close to the experimental data with acceptable error % and hence the mathematical model will be efficient to predict the responses. Table 4 Confirmation test results Exp. No
Cutting speed
1
165
Feed mm/rev
m/min 0.07
Depth of cut
Work-tool temperature
interface Roughness Ra (micro m)
mm
Predicted
Exp.
% error
Predicted
Exp.
% error
0.4
92.79
94.68
1.99%
1.89
1.94
2.3%
2
175
0.05
0.25
92.79
95.6
2.94%
1.35
1.36
0.9%
3
130
0.1
0.75
104.17
102.7
-1.43%
2.60
2.54
-2.6%
4
185
0.075
0.5
103.48
104.7
1.16%
1.56
1.58
1.5%
5
175
0.1
0.75
111.09
114.2
2.72%
2.09
2.03
-3.2%
4. Conclusions In this study, the surface roughness Ra, Rq and Rz and temperature at work-tool interface were analysed by optimising feed, cutting speed, and depth of cut. The following conclusions were drawn from the experimental work. • The work-tool interface temperature gets affected if cutting speed and feed are not chosen correctly. The work-tool temperature will be maximum when cutting speed is 175 m/min, feed is 0.1 mm/rev and depth of cut is 0.5 mm. • The surface roughness is influenced by feed rate and cutting speed. The best surface finish was obtained for cutting speed =175 mm/min, feed rate = 0.05 mm/rev and depth of cut = 0.25 mm. • The confirmation results of the developed models predicted maximum error of 2.94 % for the specified experimental set-up which proved that the models are good enough to predict the responses. • The regression models arrived for optimal parameters provide useful guidelines for fabrication and machining aspects of the aerospace, automobile and military applications. References [1] Y. LI, Q. lin LI, D. LI, W. LIU, and G. gang SHU, “Fabrication and characterization of stir casting AA6061-31%B4C composite,” Trans. Nonferrous Met. Soc. China (English Ed., vol. 26, no. 9, pp. 2304–2312, 2016. [2] J. J. Moses, I. Dinaharan, and S. J. Sekhar, “Prediction of influence of process parameters on tensile strength of AA6061/TiC aluminum matrix composites produced using stir casting,” Trans. Nonferrous Met. Soc. China (English Ed., vol. 26, no. 6, pp. 1498–1511, 2016. [3] M. Nataraj and K. Balasubramanian, “Parametric optimization of CNC turning process for hybrid metal matrix composite,” Int. J. Adv. Manuf. Technol. DOI 10.1007/s00170-016-8780-4 [4] A.Srinivasan, R. M. Arunachalam, S. Ramesh, and J. S. Senthilkumaar, “Machining Performance Study on Metal Matrix Composites-A Response Surface Methodology Approach., American Journal of Applied Sciences vol. 9, no. 4, pp. 478–483, 2012. [5] A. K. Sahoo and S. Pradhan, “Modeling and optimization of Al/SiCp MMC machining using Taguchi approach,” Meas. J. Int. Meas. Confed., vol. 46, no. 9, pp. 3064–3072, 2013. [6] A. Kannan, K. Esakkiraja, and M. Nataraj, “Modeling and Analysis for Cutting Temperature in Turning of Aluminium 6063 Using Response Surface Methodology,” vol. 9, no. 4, pp. 59–64, 2013.
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[7] M. Mia and N. R. Dhar, “Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method,” Int. J. Adv. Manuf. Technol., pp. 1–15, 2016. [8] A. Aggarwal, H. Singh, P. Kumar, and M. Singh, “Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique — A,” vol. 10, no. 1997, pp. 373–384, 2007. [9] D. O’Sullivan and M. Cotterell, “Temperature measurement in single point turning,” J. Mater. Process. Technol., vol. 118, no. 1–3, pp. 301–308, 2001. [10] L. B. Abhang and M. Hameedullah, “Chip-Tool Interface Temperature Prediction Model for Turning Process,” Int. J. Eng. Sci. Technol., vol. 2, no. 4, pp. 382–393, 2010. [11] B. Davoodi and A. H. Tazehkandi, “Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid,” J. Clean. Prod., vol. 68, pp. 234–242, 2014. [12] J. Hashim, L. Looney, M. S. J. Hashmi, “Metal matrix composites: production by the stir casting method” J. Mater. Process. Technol., vol. 92–93, pp. 1–7, 1999. [13] J. Hashim, L. Looney, and M. S. J. Hashmi, “The enhancement of wettability of SiC particles in cast aluminium matrix composites,” J. Mater. Process. Technol., vol. 119, no. 1–3, pp. 329–335, 2001. [14] T.Sasimurugan and K.Palanikumar “Analysis of the Machining Characteristics on Surface Roughness of a Hybrid,” J. Minerals & Mater Charact.&Engg, vol. 10, no. 13, pp. 1213–1224, 2011. [15] P. Senthil, T. Selvaraj, and K. Sivaprasad, “Influence of turning parameters on the machinability of homogenized Al-Cu/TiB2 in situ metal matrix composites,” Int. J. Adv. Manuf. Technol., vol. 67, no. 5–8, pp. 1589–1596, 2013.