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CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA S.P. Palaniappan a, K. Muthukumar a, R.V. Sabariraj b, S. Dinesh Kumar c, T. Sathish d,⇑ a
Department of Mechanical Engineering, Chendhuran College of Engineering and Technology, Pudukkottai 622507, Tamilnadu, India Department of Mechanical Engineering, Lakshmi Subramanian Polytechnic College, Madurai 625324, Tamilnadu, India c St. Peter’s Institute of Higher Education and Research, Avadi, Chennai 600 054, Tamil Nadu, India d SMR East Coast College of Engineering and Technology, Thanjavur 614 612, Tamil Nadu, India b
a r t i c l e
i n f o
Article history: Received 8 September 2019 Accepted 1 October 2019 Available online xxxx Keywords: Al6082 Turing parameters Optimization Taguchi ANOVA Chip temperature
a b s t r a c t This paper experimentally investigates the machining of Aluminium 6082 alloy to identify the optimal parameters for CNC turning process. A plan of experiments based on L27 orthogonal array was established and turning experiments were conducted with prefixed cutting parameters for Aluminium 6082 using tungsten carbide cutting tool. The turning parameters were spindle speed, feed rate and depth of cut for the responses of surface roughness and material removal rate by using Taguchi and ANOVA and also the temperature on chip compared for each experimental condition. Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the International Conference on Recent Trends in Nanomaterials for Energy, Environmental and Engineering Applications.
1. Introduction Optimization of parameters in turning operation were studied and discussed by a variety of researchers with respect to a choice of conditions for various materials by using different methods [16]. Franko Puh et al., experimentally investigate through Greybased Taguchi method and ANOVA for the multiple performance characteristics of turning operations by L9 orthogonal array. They considered main factors such as speed (400–500 m/min), feed (0.1–0.2 mm/min) and depth of cut (0.4–1.2 mm) [1]. H Aouici et al., evidently explained about hard turning with respect to the possessions of cutting situation on surface roughness and cutting forces three level and three factors through ANOVA and RSM [2]. They undoubtedly explained the various consequences of cutting speed on surface roughness and cutting forces at different feed rates [17]. They also clearly compared the measured and predicted values for surface roughness, cutting forces. Drawish S M et al., experimentally investigate about the outcome of the tools and cutting parameters on surface roughness of 718 Ni alloy [3]. They also evidently showed and concluded that the rate of feed have the
⇑ Corresponding author. E-mail address: [email protected] (T. Sathish).
most important cause on surface roughness between the parameters considered for the investigation [18]. Prof. Sande A. N concluded that from the experimental result statistically point towards that the feature levels are disastrous to recognize the error for the reason that of the lesser amount of range among the outputs [4]. Chorng-Jyh Tzeng et al., experimentally studied about turning operations with various speed (125– 185 m/min), feed (0.12–0.20 mm/min), depth of cut (0.50– 0.80 mm) and cutting fluid ratio (4–12%) by using Taguchi method and 3 level 4 factor Grey relational analysis [5]. They concluded that main contribution for corresponding study were the depth of cut, the cutting speed, the cutting fluid mixture ratios, and the feed rate in order. DOC was the most influencing factor [19]. Dyi-Cheng Chen et al., clearly explained about Taguchi method for an experimental investigation with the help of various tables and diagrams [6]. C. L. Lin, experimentally investigate turning with speed (135–285 m/min), feed (0.08–0.32 mm/min), depth of cut (0.6–1.6 mm) through the Taguchi method and grey relational analysis for the tool life, cutting force, and surface roughness [7]. He was clearly explained about the step by step process of this study and proposed the optimized characteristic values for the best performance of turning operation [20]. He mentioned that the feed rate create higher contribution among the factors [21]. K. Mani lavanya et al., experimentally studied about Turning Operation of
https://doi.org/10.1016/j.matpr.2019.10.053 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the International Conference on Recent Trends in Nanomaterials for Energy, Environmental and Engineering Applications.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
2
S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Nomenclatures N f DOC dc MRR SR T
Spindle speed, rpm Feed rate, mm/rev Depth of cut, mm Depth of cut, mm Material Removal Rate, mm3/min Surface Roughness, mm Temperature of chip, °C
RSM CNC SN S R-Sq R-Sq (adj)
AISI-1016 Alloy Steels with CBN for the speed (360–1150 m/min), feed (0.05–0.13 mm/min), depth of cut (0.5–1.0 mm) through the Taguchi method and ANOVA for surface roughness [8]. They provide the main contribution order feed rate (64%), speed and depth of cut respectively with the validation that the error occurred was less than that 2.0% between calculated values by regression equation and actual value [22]. Ranganath M S was reviewed and entirely explained about consequence procedures for Taguchi Method and ANOVA for turning with various parameters [9]. Narendra Kumar Verma et al., without a doubt studied about Turning of AISI 1045 steel with coated cemented carbide tool under dry cutting condition by Taguchi Method Under Various Machining Parameters such as the speed (160–620 m/min), feed (0.3–0.5 mm/min) and depth of cut (0.7– 0.9 mm) [10]. They manually have done the calculations of S/N Ratios through formula for Surface Roughness and MRR. They completed the conformation test also for the same [23]. Sujit Kumar Jha experimentally investigated about machining on CNC machine of Al material with the cutting speed (600– 1000 m/min), feed (0.1 to 0.2 mm/min) and depth of cut (0.5– 1.5 mm) on material removal rate (MRR) by using Taguchi method and ANOVA analysis with confirmation test [11]. They concluded that through the F-test which showed that the DOC has major factor on MRR of Al turning [24]. R Rudrapati et al., explained about turning operation of aluminium alloy by CNC machine with the cutting speed (600–700 m/min), feed (25–50 mm/min) and depth of cut (0.2–0.4 mm) by Box-Behnken design method for surface roughness and numerical modelling has been completed by RSM [12]. They also mentioned the parameters that affect surface roughness through Fishbone diagram. Horvath R et al., completed experimental investigation to take full advantage of productivity and decrease the surface roughness through turning of two different aluminium materials by using diamond tool [13]. Upinder kumar, explained about high speed turning operation of Medium Carbon Steel AISI 1045 with the cutting speed (150–226 m/min), feed (0.1–0.3 mm/min) and depth of cut (0.5–1.5 mm) for Surface roughness and Material Removal Rate by Taguchi method and Grey Relational Analysis [14]. From Response Table for MRR and Surface Roughness most significant factor was DOC and feed rate respectively [25]. 2. Methods and methodology The Aluminium 6082 turning experiments were conducted by using tungsten carbide cutting tool with following process param-
Table 1 Process parameters with three levels. Factors
Process parameters
Unit
Level 1
Level 2
Level 3
A B C
Spindle Speed (N) Feed Rate (f) Depth of cut (dc)
Rpm mm/rev mm
800 0.15 1
1200 0.2 1.5
1600 0.25 2
Response Surface Methodology Computer Numerical Control Signal to Noise SN ratio square root of MSR R2 adjusted R2
eters with three levels listed in Table 1 were considered by CNC machine [26]. Turning operation was done with respect to the L27 array listed in Table 2 [27]. There were 27 turning operation was completed and the MRR and SR of the corresponding specimen were noted and corresponding temperature of chip is measured through thermocouples [28]. 3. Results and discussion For the above experimental conditions results were explained through the following figures by using MAT lab software [29]. 3.1. MRR Fig. 1 mentioned the relation between mean of SN ratios to the parameters speed, feed and DOC with larger is better method because high MRR needed for any machining process [30]. The main effect plot for MRR was showed in Fig. 2 [31]. The maximum MRR would obtain at 1600 rpm speed with 0.25 mm/rev of feed and 2.0 mm of DOC for the both Figs. 1 and 2. The corresponding design in Table 2 was 27th parameter combination [32]. Interaction plot for MRR with respect to speed, feed and DOC in a single combination diagram was visibly shown in Fig. 3. All the relation showed that the MRR were directly proportional to paramTable 2 L27 Array for experiment. Experiment No.
Spindle Speed (N) Rpm
Feed Rate (f) mm/rev
Depth of cut (dc) mm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
800 800 800 800 800 800 800 800 800 1200 1200 1200 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600
0.15 0.15 0.15 0.2 0.2 0.2 0.25 0.25 0.25 0.15 0.15 0.15 0.2 0.2 0.2 0.25 0.25 0.25 0.15 0.15 0.15 0.2 0.2 0.2 0.25 0.25 0.25
1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx
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Fig. 1. Main effect plot for SN ratio for MRR.
Fig. 2. Main effect plot for MRR.
eters [33]. High Speed and high DOC produced highest MRR values. Similarly same relation produced lowest MRR for low speed with low DOC. Fig. 4 showed relation normal probability plot (Residual Vs percentage), versus plot (fitted values to the residuals), histogram (residual to the frequency) and versus order plot (observation order to residual) in single combination diagram representation for MRR. Normal probability values were most
nearly closed to the average line in normal probability plot. Versus plot also mentioned that the values closed to the average line. Histogram also produced the considerable results like bell type histogram. Versus order also provided that the acceptable result more than 17 values crossed the zero line. From Table 3 rank for the parameters were identified. The most significant parameter was first Speed second DOC and feed was last
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 3. Interaction plot for MRR.
Fig. 4. Residual plot for MRR.
for signal to noise response table and Means response table. So the Speed and feed provided supplementary contribution in MRR which was shown in Fig. 5. 3.2. Surface roughness Fig. 6 point out the relation between main effects plot for Signal to Noise (SN) ratios to the parameters such as speed, feed
and DOC with smaller is better method for surface roughness because low SR was needed for any machining process. The optimum SR would be obtained at 800 rpm speed with 0.15 mm/rev of feed and 1.0 mm of DOC for the Fig. 6. The corresponding design in Table 2 was 1st parameter combination. This combination produced the smoothness in surface when compare to others. Fig. 7 showed the main effect plot for SR for the data means.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 3 MRR - Response Table (Larger is better). Level
1 2 3 Delta Rank
Response Table for Signal to Noise Ratios
Response Table for Means
N
Feed
DOC
N
feed
DOC
84.19 87.25 89.33 5.15 1
85.61 87.04 88.12 2.51 3
84.65 86.99 89.13 4.48 2
16,619 23,749 30,247 13,628 1
20,226 23,635 26,753 6527 3
17,664 23,183 29,767 12,103 2
Fig. 5. Contour plot of MRR.
Fig. 6. Main effect plot for SN ratio for SR.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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Fig. 7. Main effect plot for SR.
Fig. 8. Interaction plot for SR.
Fig. 8 showed Interaction plot for SR which clearly showed the inter relation of speed, feed and DOC with the combination of one to another in a single figure. Residual plot for SR was shown in Fig. 9 which have the relation normal probability plot (Residual Vs percentage), versus plot (fitted values to the residuals), histogram (residual to the frequency) and versus order plot (observation order to residual) in single combination graphical representation for SR. The normal probability mentioned that the closest mentions of point to the average probability line with respect to the residual to the percentages. Versus diagram also explained about the relations between the fitted values to the residual values initial final values were closer to
the zero line. Then histogram showed the relation between residuals to the frequency as a bar diagram which produced in the bell shape. At last the versus order more than 15 orders crossed the zero line so the work was acceptable. The highest residual value reached in 16th order. The Table 4 provided the responses for SR values in Smaller is better manner because we need very less amount of surface roughness for any kind of machining operations. The response of SN ratio and the Means mentioned that the most significant parameter was feed but there was controversy in least significant factor. The response of SN ratio mentioned that the DOC was least significant factor but the response of means showed that the speed as a least
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 9. Residual plots for SR.
Table 4 Response Table for SR - Smaller is better. Level
1 2 3 Delta Rank
Response Table for Signal to Noise Ratios
Response Table for Means
N
Feed
DOC
N
feed
DOC
3.5657 5.6562 4.2954 2.0905 2
0.6777 5.4735 8.7215 9.3993 1
3.3641 4.8189 5.3344 1.9703 3
1.7144 2.0278 1.7767 0.3133 3
0.9000 1.8933 2.7256 1.8256 1
1.6756 1.8511 1.9922 0.3167 2
Fig. 10. Contour plot for SR.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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Table 5 Values of Response and regression equations. Response
Regression equation 3
MRR (mm /min) SR (mm)
31471 + 18.0 N + 68337f + 13112 dc 2.38 + 0.000078 N + 18.3f + 0.317 dc
S
R-Sq
R-Sq (adj)
278,724 0.306160
96.0% 87.8%
95.5% 86.2%
Fig. 11. Temperature measurement for various speed values.
Fig. 12. Experiment wise temperature measurement.
significant factor. The contour plot for SR was shown in Fig. 10 with respect to feed to speed. Various colour showed that the SR values (Table 5). Akhil C et al. [15], explained about the temperature during machining similarly the temperature measurement for these experimental arrangements measured with the help of thermocouples which are showed in Figs. 11 and 12. 4. Conclusion From the above experimental investigation study on machining of Aluminium 6082 alloy in CNC turning process using tungsten carbide cutting tool was completed and the conclusions were listed below. The most significant parameter for MRR was speed similarly feed was the most significant parameter for SR.
The maximum value of MRR was 41925.82 mm3/min for the optimum parameters such as 1600 rpm speed, 0.25 mm/rev feed and 1.0 mm depth of cut without considering about SR for corresponding parameters was 2.82 mm and corresponding temperature is 86OC. For the minimum value of SR was 0.63 mm for the optimum parameters such as 1200 rpm speed, 0.15 mm/rev feed and 1.5 mm depth of cut without considering the corresponding MRR was 19375.62 mm3/min and corresponding temperature is 76 °C. But comparing the other combinations satisfying response were 1600 rpm speed, 0.15 mm/rev feed and 2 mm depth of cut these leads to MRR as 36102.79 mm3/min and SR as 0.76 mm and corresponding temperature is 91 °C. The maximum and minimum temperature values are 50 °C and 93.3 °C respectively.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA S.P. Palaniappan a, K. Muthukumar a, R.V. Sabariraj b, S. Dinesh Kumar c, T. Sathish d,⇑ a
Department of Mechanical Engineering, Chendhuran College of Engineering and Technology, Pudukkottai 622507, Tamilnadu, India Department of Mechanical Engineering, Lakshmi Subramanian Polytechnic College, Madurai 625324, Tamilnadu, India c St. Peter’s Institute of Higher Education and Research, Avadi, Chennai 600 054, Tamil Nadu, India d SMR East Coast College of Engineering and Technology, Thanjavur 614 612, Tamil Nadu, India b
a r t i c l e
i n f o
Article history: Received 8 September 2019 Accepted 1 October 2019 Available online xxxx Keywords: Al6082 Turing parameters Optimization Taguchi ANOVA Chip temperature
a b s t r a c t This paper experimentally investigates the machining of Aluminium 6082 alloy to identify the optimal parameters for CNC turning process. A plan of experiments based on L27 orthogonal array was established and turning experiments were conducted with prefixed cutting parameters for Aluminium 6082 using tungsten carbide cutting tool. The turning parameters were spindle speed, feed rate and depth of cut for the responses of surface roughness and material removal rate by using Taguchi and ANOVA and also the temperature on chip compared for each experimental condition. Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the International Conference on Recent Trends in Nanomaterials for Energy, Environmental and Engineering Applications.
1. Introduction Optimization of parameters in turning operation were studied and discussed by a variety of researchers with respect to a choice of conditions for various materials by using different methods [16]. Franko Puh et al., experimentally investigate through Greybased Taguchi method and ANOVA for the multiple performance characteristics of turning operations by L9 orthogonal array. They considered main factors such as speed (400–500 m/min), feed (0.1–0.2 mm/min) and depth of cut (0.4–1.2 mm) [1]. H Aouici et al., evidently explained about hard turning with respect to the possessions of cutting situation on surface roughness and cutting forces three level and three factors through ANOVA and RSM [2]. They undoubtedly explained the various consequences of cutting speed on surface roughness and cutting forces at different feed rates [17]. They also clearly compared the measured and predicted values for surface roughness, cutting forces. Drawish S M et al., experimentally investigate about the outcome of the tools and cutting parameters on surface roughness of 718 Ni alloy [3]. They also evidently showed and concluded that the rate of feed have the
⇑ Corresponding author. E-mail address: [email protected] (T. Sathish).
most important cause on surface roughness between the parameters considered for the investigation [18]. Prof. Sande A. N concluded that from the experimental result statistically point towards that the feature levels are disastrous to recognize the error for the reason that of the lesser amount of range among the outputs [4]. Chorng-Jyh Tzeng et al., experimentally studied about turning operations with various speed (125– 185 m/min), feed (0.12–0.20 mm/min), depth of cut (0.50– 0.80 mm) and cutting fluid ratio (4–12%) by using Taguchi method and 3 level 4 factor Grey relational analysis [5]. They concluded that main contribution for corresponding study were the depth of cut, the cutting speed, the cutting fluid mixture ratios, and the feed rate in order. DOC was the most influencing factor [19]. Dyi-Cheng Chen et al., clearly explained about Taguchi method for an experimental investigation with the help of various tables and diagrams [6]. C. L. Lin, experimentally investigate turning with speed (135–285 m/min), feed (0.08–0.32 mm/min), depth of cut (0.6–1.6 mm) through the Taguchi method and grey relational analysis for the tool life, cutting force, and surface roughness [7]. He was clearly explained about the step by step process of this study and proposed the optimized characteristic values for the best performance of turning operation [20]. He mentioned that the feed rate create higher contribution among the factors [21]. K. Mani lavanya et al., experimentally studied about Turning Operation of
https://doi.org/10.1016/j.matpr.2019.10.053 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the International Conference on Recent Trends in Nanomaterials for Energy, Environmental and Engineering Applications.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
2
S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Nomenclatures N f DOC dc MRR SR T
Spindle speed, rpm Feed rate, mm/rev Depth of cut, mm Depth of cut, mm Material Removal Rate, mm3/min Surface Roughness, mm Temperature of chip, °C
RSM CNC SN S R-Sq R-Sq (adj)
AISI-1016 Alloy Steels with CBN for the speed (360–1150 m/min), feed (0.05–0.13 mm/min), depth of cut (0.5–1.0 mm) through the Taguchi method and ANOVA for surface roughness [8]. They provide the main contribution order feed rate (64%), speed and depth of cut respectively with the validation that the error occurred was less than that 2.0% between calculated values by regression equation and actual value [22]. Ranganath M S was reviewed and entirely explained about consequence procedures for Taguchi Method and ANOVA for turning with various parameters [9]. Narendra Kumar Verma et al., without a doubt studied about Turning of AISI 1045 steel with coated cemented carbide tool under dry cutting condition by Taguchi Method Under Various Machining Parameters such as the speed (160–620 m/min), feed (0.3–0.5 mm/min) and depth of cut (0.7– 0.9 mm) [10]. They manually have done the calculations of S/N Ratios through formula for Surface Roughness and MRR. They completed the conformation test also for the same [23]. Sujit Kumar Jha experimentally investigated about machining on CNC machine of Al material with the cutting speed (600– 1000 m/min), feed (0.1 to 0.2 mm/min) and depth of cut (0.5– 1.5 mm) on material removal rate (MRR) by using Taguchi method and ANOVA analysis with confirmation test [11]. They concluded that through the F-test which showed that the DOC has major factor on MRR of Al turning [24]. R Rudrapati et al., explained about turning operation of aluminium alloy by CNC machine with the cutting speed (600–700 m/min), feed (25–50 mm/min) and depth of cut (0.2–0.4 mm) by Box-Behnken design method for surface roughness and numerical modelling has been completed by RSM [12]. They also mentioned the parameters that affect surface roughness through Fishbone diagram. Horvath R et al., completed experimental investigation to take full advantage of productivity and decrease the surface roughness through turning of two different aluminium materials by using diamond tool [13]. Upinder kumar, explained about high speed turning operation of Medium Carbon Steel AISI 1045 with the cutting speed (150–226 m/min), feed (0.1–0.3 mm/min) and depth of cut (0.5–1.5 mm) for Surface roughness and Material Removal Rate by Taguchi method and Grey Relational Analysis [14]. From Response Table for MRR and Surface Roughness most significant factor was DOC and feed rate respectively [25]. 2. Methods and methodology The Aluminium 6082 turning experiments were conducted by using tungsten carbide cutting tool with following process param-
Table 1 Process parameters with three levels. Factors
Process parameters
Unit
Level 1
Level 2
Level 3
A B C
Spindle Speed (N) Feed Rate (f) Depth of cut (dc)
Rpm mm/rev mm
800 0.15 1
1200 0.2 1.5
1600 0.25 2
Response Surface Methodology Computer Numerical Control Signal to Noise SN ratio square root of MSR R2 adjusted R2
eters with three levels listed in Table 1 were considered by CNC machine [26]. Turning operation was done with respect to the L27 array listed in Table 2 [27]. There were 27 turning operation was completed and the MRR and SR of the corresponding specimen were noted and corresponding temperature of chip is measured through thermocouples [28]. 3. Results and discussion For the above experimental conditions results were explained through the following figures by using MAT lab software [29]. 3.1. MRR Fig. 1 mentioned the relation between mean of SN ratios to the parameters speed, feed and DOC with larger is better method because high MRR needed for any machining process [30]. The main effect plot for MRR was showed in Fig. 2 [31]. The maximum MRR would obtain at 1600 rpm speed with 0.25 mm/rev of feed and 2.0 mm of DOC for the both Figs. 1 and 2. The corresponding design in Table 2 was 27th parameter combination [32]. Interaction plot for MRR with respect to speed, feed and DOC in a single combination diagram was visibly shown in Fig. 3. All the relation showed that the MRR were directly proportional to paramTable 2 L27 Array for experiment. Experiment No.
Spindle Speed (N) Rpm
Feed Rate (f) mm/rev
Depth of cut (dc) mm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
800 800 800 800 800 800 800 800 800 1200 1200 1200 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600
0.15 0.15 0.15 0.2 0.2 0.2 0.25 0.25 0.25 0.15 0.15 0.15 0.2 0.2 0.2 0.25 0.25 0.25 0.15 0.15 0.15 0.2 0.2 0.2 0.25 0.25 0.25
1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2 1 1.5 2
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Fig. 1. Main effect plot for SN ratio for MRR.
Fig. 2. Main effect plot for MRR.
eters [33]. High Speed and high DOC produced highest MRR values. Similarly same relation produced lowest MRR for low speed with low DOC. Fig. 4 showed relation normal probability plot (Residual Vs percentage), versus plot (fitted values to the residuals), histogram (residual to the frequency) and versus order plot (observation order to residual) in single combination diagram representation for MRR. Normal probability values were most
nearly closed to the average line in normal probability plot. Versus plot also mentioned that the values closed to the average line. Histogram also produced the considerable results like bell type histogram. Versus order also provided that the acceptable result more than 17 values crossed the zero line. From Table 3 rank for the parameters were identified. The most significant parameter was first Speed second DOC and feed was last
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Fig. 3. Interaction plot for MRR.
Fig. 4. Residual plot for MRR.
for signal to noise response table and Means response table. So the Speed and feed provided supplementary contribution in MRR which was shown in Fig. 5. 3.2. Surface roughness Fig. 6 point out the relation between main effects plot for Signal to Noise (SN) ratios to the parameters such as speed, feed
and DOC with smaller is better method for surface roughness because low SR was needed for any machining process. The optimum SR would be obtained at 800 rpm speed with 0.15 mm/rev of feed and 1.0 mm of DOC for the Fig. 6. The corresponding design in Table 2 was 1st parameter combination. This combination produced the smoothness in surface when compare to others. Fig. 7 showed the main effect plot for SR for the data means.
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S.P. Palaniappan et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 3 MRR - Response Table (Larger is better). Level
1 2 3 Delta Rank
Response Table for Signal to Noise Ratios
Response Table for Means
N
Feed
DOC
N
feed
DOC
84.19 87.25 89.33 5.15 1
85.61 87.04 88.12 2.51 3
84.65 86.99 89.13 4.48 2
16,619 23,749 30,247 13,628 1
20,226 23,635 26,753 6527 3
17,664 23,183 29,767 12,103 2
Fig. 5. Contour plot of MRR.
Fig. 6. Main effect plot for SN ratio for SR.
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Fig. 7. Main effect plot for SR.
Fig. 8. Interaction plot for SR.
Fig. 8 showed Interaction plot for SR which clearly showed the inter relation of speed, feed and DOC with the combination of one to another in a single figure. Residual plot for SR was shown in Fig. 9 which have the relation normal probability plot (Residual Vs percentage), versus plot (fitted values to the residuals), histogram (residual to the frequency) and versus order plot (observation order to residual) in single combination graphical representation for SR. The normal probability mentioned that the closest mentions of point to the average probability line with respect to the residual to the percentages. Versus diagram also explained about the relations between the fitted values to the residual values initial final values were closer to
the zero line. Then histogram showed the relation between residuals to the frequency as a bar diagram which produced in the bell shape. At last the versus order more than 15 orders crossed the zero line so the work was acceptable. The highest residual value reached in 16th order. The Table 4 provided the responses for SR values in Smaller is better manner because we need very less amount of surface roughness for any kind of machining operations. The response of SN ratio and the Means mentioned that the most significant parameter was feed but there was controversy in least significant factor. The response of SN ratio mentioned that the DOC was least significant factor but the response of means showed that the speed as a least
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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Fig. 9. Residual plots for SR.
Table 4 Response Table for SR - Smaller is better. Level
1 2 3 Delta Rank
Response Table for Signal to Noise Ratios
Response Table for Means
N
Feed
DOC
N
feed
DOC
3.5657 5.6562 4.2954 2.0905 2
0.6777 5.4735 8.7215 9.3993 1
3.3641 4.8189 5.3344 1.9703 3
1.7144 2.0278 1.7767 0.3133 3
0.9000 1.8933 2.7256 1.8256 1
1.6756 1.8511 1.9922 0.3167 2
Fig. 10. Contour plot for SR.
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Table 5 Values of Response and regression equations. Response
Regression equation 3
MRR (mm /min) SR (mm)
31471 + 18.0 N + 68337f + 13112 dc 2.38 + 0.000078 N + 18.3f + 0.317 dc
S
R-Sq
R-Sq (adj)
278,724 0.306160
96.0% 87.8%
95.5% 86.2%
Fig. 11. Temperature measurement for various speed values.
Fig. 12. Experiment wise temperature measurement.
significant factor. The contour plot for SR was shown in Fig. 10 with respect to feed to speed. Various colour showed that the SR values (Table 5). Akhil C et al. [15], explained about the temperature during machining similarly the temperature measurement for these experimental arrangements measured with the help of thermocouples which are showed in Figs. 11 and 12. 4. Conclusion From the above experimental investigation study on machining of Aluminium 6082 alloy in CNC turning process using tungsten carbide cutting tool was completed and the conclusions were listed below. The most significant parameter for MRR was speed similarly feed was the most significant parameter for SR.
The maximum value of MRR was 41925.82 mm3/min for the optimum parameters such as 1600 rpm speed, 0.25 mm/rev feed and 1.0 mm depth of cut without considering about SR for corresponding parameters was 2.82 mm and corresponding temperature is 86OC. For the minimum value of SR was 0.63 mm for the optimum parameters such as 1200 rpm speed, 0.15 mm/rev feed and 1.5 mm depth of cut without considering the corresponding MRR was 19375.62 mm3/min and corresponding temperature is 76 °C. But comparing the other combinations satisfying response were 1600 rpm speed, 0.15 mm/rev feed and 2 mm depth of cut these leads to MRR as 36102.79 mm3/min and SR as 0.76 mm and corresponding temperature is 91 °C. The maximum and minimum temperature values are 50 °C and 93.3 °C respectively.
Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053
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Please cite this article as: S. P. Palaniappan, K. Muthukumar, R. V. Sabariraj et al., CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.10.053