- Email: [email protected]
N Ratio
Available online at www.sciencedirect.com
ScienceDirect Procedia Technology 24 (2016) 399 – 405
International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST - 2015)
Optimization of Process Parameters for Increasing Material Removal Rate for Turning Al6061 Using S/N ratio Rajendra B a,b
a,
Deepak Db*
Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal University, Manipal -576104,karnataka, India
Abstract
Modern industries strive to improve the quality of their product by choosing proper materials and methods. Selection of materials of high strength to weight ratio like aluminum and setting of optimum machining parameters ensures the desired quality of product at affordable cost. Industries look for high productivity and better surface finish in machining operations which depends on process parameters. In this article the process parameters such as feed rate, cutting speed and depth of cut are selected to optimize the material removal rate in turning of Al-6061. The analysis is carried out using signal to noise ratio for predicting optimum process parameters. The effect of process parameters on material removal is discussed in this article. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). committee of ICETEST Peer-reviewunder under responsibility of the organizing Peer-review responsibility of the organizing committee of ICETEST – 2015– 2015. Keywords:Al6061; Material removal rate ; Turning Process; S/N ratio; ANOVA
1. Introduction Aluminium used in variety of applications such as automobile and aerospace components, missile parts, storage containers, marine applications, etc. This being a light weight material possesses excellentcorrosion resistance, thermal and mechanical properties, has replaced steel in many engineering applications. Its high strength to weight ratio and low specific cutting energy (0.4 - 1.1 Ws/mm3) has made this material as the best in automobile industry. Al 6061 is heat treatable material which has gained importance in the manufacturing industries.In this view there
* Corresponding author. Tel.: 91-9480345644; E-mail address:Email:[email protected]
2212-0173 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICETEST – 2015 doi:10.1016/j.protcy.2016.05.055
400
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
Nomenclature A Cutting Speed (m/min) B Feed (mm/rev) C Depth of Cut (mm) Ra Surface roughness µm SN Signal to Noise ratio nTotal number of trial runs at ith setting yiValue of the response at ith setting kTotal number of replications. DF Degrees of freedom SS Sum of squares MS Mean sum of squares F Fisher’s Number d Confidence interval K݂݂݁ Effective sample size have been many attempts by researchers to optimize the manufacturing process either by improving the composition of the material or by selecting proper process parameters for machining. Machinability is the ease with which the material is machined. The performance measure of machinability includes MRR, specific energy consumption, surface finish, tool life and chip flow pattern (Songmene et. al, 2011). To enhance certain properties aluminum is alloyed with copper, manganese, silicon, magnesium, zinc, etc. Aluminium alloys with magnesium and silicon are designated by 6XXX series. Al6061 has excellent mechanical properties and corrosion resistance (Xuewu Lia,2015). It also exhibits good weldability (Omega Research, 2002). There are cited literatures on optimization of the machining parameters which has the focus on surface roughness. The effect of process parameter like cutting speed, feed rate, depth of cut, amount of lubrication, type of tool, tool overhang and ageing of work material are investigated. The surface integrity effects of turned Al 6061 and Al6061-T6 is studied by Tohet. al. (2004). Sreejith (2008) found that machining with minimum quantity lubricant improved machinability compared with dry or flooded lubricant machining. Mukesh Kumar et. al. (2009) studied the effect of speed, feed rate and depth of cut using a coated carbide tool on Al6061-t4. HalilDemir et al (2009) investigated the effect of artificial ageing of aluminum on the surface finish of Al6061. Authors have reported the effect of ageing and cutting speed on the surface roughness. Optimization of cutting parameters was made by Carmita (2013) to minimize the energy consumption in turning process. Higher feed rate was found to reduce energy consumptionbut it increased the surface roughness. The influence of tool overhang on surface roughness was studied by Vinod et. al. (2014). The authors have reported that too small or too large overhang lead to poor surface finish. If the surface finish is within the acceptable range, it is desirable to improve the production rate by increasing MRR. In consideration of these factors, the present work attempts to optimize the process parameters to improve the MRR and hence productivity. 2. Experimental set up and methods Figure 1 shows the experimental setup used in the present work. Al6061 work pieces of diameter 40 mm and length 150 mm were used in the experiments. The cutting tool used is SiC insert. Al6061 contains 95% Al along with the traces of other elements like Fe, Si, Cu, Mn, Mg, Cr, Ti and Zn. Figure 2 shows the sample work pieces machined.The process parameters such as cutting speed, feed and depth of cut were chosen with each parameter having three levels as shown in the Table 1. Settings for levels of each process parameter is chosen based on the pilot study. For the present experimental condition the best suitable design is Taguchi’s L 9orthogonal array which is shown in Table 2. Taguchi’s orthogonal array enables the researchers to conduct the minimum number of experiments by optimum use of resources. Experiments were conducted with two replications with the supply of coolant SAE 40. The material MRR obtained in each run is determined by the weight loss method and corresponding values of MRR are shown in Table 2. The analysis is carried out using S/N ratio for Higher the Better (HB) criteria using the formula as given below. The SN ratio obtained in each run is also shown in Table 2.The
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
experimental results were analyzed using Signal to Noise ratio with the Higher the Better criteria as given by the equation (1).The relative effect of each process parameter is analyzed by Analysis of Variance (ANOVA). S N HB
ª§ 1 · 1 «¨© k ¸¹ ¦ ® y ¬ ¯ k
10 log
2
i 1
i
½º ¾» ¿¼
(1)
Fig. 1 Experimental set up
Fig. 2 Sample work-piece Table 1 Signal parameter and their levels Factor/Level 1 2 Cutting Speed (m/min) A 308 369 Feed (mm/rev) B 0.05 0.1 Depth of Cut (mm) C 1 1.5
Run 1 2 3 4 5 6 7 8 9
Table 2: Experimental designs with results Process parameters MRR (gm/minute) A B C k=1 k=2 1 1 1 20.00 24.97 1 2 2 59.99 59.99 1 3 3 119.9 134.9 2 1 2 35.99 41.98 2 2 3 95.98 95.98 2 3 1 72.06 90.07 3 1 3 55.99 55.99 3 2 1 56.05 56.05 3 3 2 125.8 150.0
3 429 0.15 2
SN ratio 26.88 35.56 42.06 31.74 39.64 38.01 34.96 34.97 42.69
401
402
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
3. Results and discussion 3.1 The analysis of effect of operating parameters on MRR using S/N ratio The present work investigates the effect of operating parameters such as cutting speed, feed rate and depth of cut. Table 2 shows the results of the replicated response in each trial. To assess the variability of the response, the analysis is carried out by S/N ratio using larger the better criteria. The effect of each process parameter at different levels is calculated using S/N ratio and is shown in Table 3. The effect of each factor on the response is also shown in the form of delta statistics in the same table. Based on the delta values the ranks are assigned to the factors in the ascending order of their influence. Among the chosen machining parameters, cutting speed, feed rate and depth of cut, the maximumSN ratio is seen for feed rate. This indicates that feed rate is the most predominant factor in MRR followed by depth of cut and the cutting speed. From the Table 3 it is seen that optimum combination of the process parameters to increase the MRR are A3B3C3. Table 3 Mean of S/N ratios Cutting Speed Feedrate Depthof cut 1 2 3 Delta Rank
34.83564 36.45585 37.81649 2.71 3
31.19531 36.72602 40.92461 9.73 1
33.28953 36.66595 38.89045 5.60 2
Figure 3 shows the main effect plot of SN ratio for MRR as response. It is seen from the plot that higher MRR is obtained at the process settings, cutting speed 429 m/min, Feed 0.15 mm/rev and depth of cut 2 mm. This combination of process parameter produces maximum SN ratio and hence the settings can be considered as optimum settings to the process parameters.Further, relative effect of each process parameter is determined by the Analysis of Variance (ANOVA) which is a statistical technique. In this technique the total variability of the response is separated with respect to individual contributions of each of the factors and the error. Table 4 shows the ANOVA values for the experimental results. The significance of the effect of each factor is evaluated using F test at 95 % confidence level. It is seen that F values of the all chosen process parameters are higher than the F critical values from the standard table. Hence all the chosen process parameters are considered as significantly affecting process parameters which have their influence on the material removal rate.
Fig. 3. Effect of operating parameters on MRR Further, Figure 4-6 shows the combined effect of process parameters on the MRR. As seen from the Figure 4, higher MRR is found to be produced at the settings of level 3 to both feed rate and the cutting speed. Further increase in these parameters increases the MRR almost linearly. The effect of feed rate seems to be highly significant compared
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
to cutting speed.Figure 5 shows the plot of MRR with respect to feed rate and depth of cut. It is seen that increase in the feed rate at any settings of cutting speed increases the MRR significantly. But it increases slightly with increase in depth of cut even at higher values of feed rate. It is also seen from the Figure 6 that the MRR exhibits more complex relationship with respect to the depth of cut and cutting speed. At the settings of the depth of cut at level 2 and cutting speed at level 3 exhibits better MRR. This shows that increasing depth of cut does not ensure increase in metal removal rate. The above phenomena is due to following facts: at higher cutting speeds more strain energy is available for the work-piece which leads to continuous and easy removal of the material thus producing higher MRR. But increase in depth of cut may cause tool chatter which reduces MRR. The higher feed rates promotes the axial movement of the cutting tool resulting in shifting of the cutting tool to new location at a faster rate thereby increasing MRR,but it creates rougher surface. Higher depth of cut creates higher thrust force on the cutting tool which push the tool radially outward. As a result the cutting tool will be released from the work-piece causing vibrations which results in decrease in MRR.
Source Cutting Speed Feed Rate Depth of Cut Residual Error Total
Table 4. ANOVA for the experimental data DF Adj SS Adj MS F 2 11.150 5.574 141.41 2 142.87 71.437 1812.10 2 47.719 23.859 605.22 2 0.079 0.039 8
150
MRR
100 3
50 2 1
2 Feed Rate
Cutting Speed
1
3
Fig. 4. The effect of feed rate and cutting speed on MRR
150
MRR
100 3
50 2 1
2 Depth of Cut
3
Feed Rate
1
Fig. 5. The effect of feed rate and depth of cut on MRR
403
404
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
150
MRR
100 3
50 2 1
2
3
Depth of Cut
Cutting Speed
1
Fig. 6. The effect of cutting speed and depth of cut on MRR 3.2 Prediction of MRR at optimum settings The expected MRR at optimum settings of the process parameters i.e., at A 3B3C3 is calculated as below. neff
Total number of experiments
(2)
1+Total degree of freedom of all factors
¦ Response value
T=
(3)
Total number of experiments
MRRae = Response at [A3+ B3+C3] –2T (4) Error mean square º ª d = F α, Error degree of freedom «¬ Effective sample size »¼
Confidence interval
MRR ae r d
1 2
(5) (6)
The confidence interval (d) is estimated to be 0.53 at 95 % confidence level. Hence the expected range of MRR is 141.54 to 142.64 gm/min. Further confirmation experiments were conducted at the optimum settings. The MRR obtained is found within the range established. 4. Conclusion Following conclusions are drawn from the present experimental work. x It is observed that the feed rate is most influential process parameters that influence MRR while turning of Aluminum 6061 followed by depth of cut and cutting speed. x The combined effect of feed rate and cutting speed and feed rate and depth of cut indicate that the maximum values of these factors yield maximum MRR. On the other hand the combined effect of depth of cut and cutting speed shows that depth of cut obtained at level 2 and cutting speed at level 3 results in better MRR. This shows that depth of cut is not the significant factor in increasing MRR. The feed rate controls the MRR.
References CarmitaCamposeco-Negrete, 2013, Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA, Journal of Cleaner Production, Volume 53, 15 pp. 195–203 Deepak, D., Rajendra, B. (2015). Investigations on the surface roughness produced in turning of Al6061 (as-cast) by taguchi method, International Journal of Research in Engineering and Technology, Volume 4 (8). HalilDemir, SüleymanGündüz, 2009, The effects of aging on machinability of 6061 aluminium alloy, Materials and Design vol 30, 1480–1483 Mukesh Kumar Barua, Anbuudayasankar, Measurement of surface roughness through RSM: effect of coated carbide tool on 6061-t4 aluminium, International Journal of Enterprise Network Management, Volume 4, Issue 2, DOI: 10.1504/IJENM.2010.037931
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
Paneerselvam, 2012, Design and analysis of experiments, PHI learning private ltd., ISBN 978-81-203-4499-0 C. J. Rao, D. Nageswara Rao, P. Srihari, 2013, Influence of cutting parameters on cutting force and surface finish in turning operation, International Conference On design and manufacturing, IConDM 2013, Procedia Engineering 64, pp. 1405 – 1415 P.S. Sreejith, Machining of 6061 aluminium alloy with MQL, dry and flooded lubricant conditions, 2008, Materials Letters, Volume 62, Issue 2, Pp.276–278 V. Songmene, R. Khettabi, I. Zaghbani, J. Kouam, and A. Djebara, 2011, Machining and Machinability of Aluminum Alloys, Aluminium Alloys, Theory and Applications, Tibor Kvackaj (Ed.), ISBN: 978-953-307- 244-9 Vinod Mishraa, Gufran S. Khanb, K.D. Chattopadhyaya, Keshva Nanda, RamaGopal V. Sarepakaa, 2014, Effects of tool overhang on selection of machining parameters and surface finish during diamond turning, Measurement, Volume 55, Pp. 353–361 Xuewu Lia, QiaoxinZhanga, Zheng Guo, Tian Shi, Jingui Yu, Mingkai Tang, Xingjiu Huang, 2015, Fabrication of superhydrophobic surface with improved corrosion inhibition on 6061 aluminum alloy substrate, Applied Surface Science, Volume 342, pp. 76–83
405
ScienceDirect Procedia Technology 24 (2016) 399 – 405
International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST - 2015)
Optimization of Process Parameters for Increasing Material Removal Rate for Turning Al6061 Using S/N ratio Rajendra B a,b
a,
Deepak Db*
Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal University, Manipal -576104,karnataka, India
Abstract
Modern industries strive to improve the quality of their product by choosing proper materials and methods. Selection of materials of high strength to weight ratio like aluminum and setting of optimum machining parameters ensures the desired quality of product at affordable cost. Industries look for high productivity and better surface finish in machining operations which depends on process parameters. In this article the process parameters such as feed rate, cutting speed and depth of cut are selected to optimize the material removal rate in turning of Al-6061. The analysis is carried out using signal to noise ratio for predicting optimum process parameters. The effect of process parameters on material removal is discussed in this article. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). committee of ICETEST Peer-reviewunder under responsibility of the organizing Peer-review responsibility of the organizing committee of ICETEST – 2015– 2015. Keywords:Al6061; Material removal rate ; Turning Process; S/N ratio; ANOVA
1. Introduction Aluminium used in variety of applications such as automobile and aerospace components, missile parts, storage containers, marine applications, etc. This being a light weight material possesses excellentcorrosion resistance, thermal and mechanical properties, has replaced steel in many engineering applications. Its high strength to weight ratio and low specific cutting energy (0.4 - 1.1 Ws/mm3) has made this material as the best in automobile industry. Al 6061 is heat treatable material which has gained importance in the manufacturing industries.In this view there
* Corresponding author. Tel.: 91-9480345644; E-mail address:Email:[email protected]
2212-0173 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICETEST – 2015 doi:10.1016/j.protcy.2016.05.055
400
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
Nomenclature A Cutting Speed (m/min) B Feed (mm/rev) C Depth of Cut (mm) Ra Surface roughness µm SN Signal to Noise ratio nTotal number of trial runs at ith setting yiValue of the response at ith setting kTotal number of replications. DF Degrees of freedom SS Sum of squares MS Mean sum of squares F Fisher’s Number d Confidence interval K݂݂݁ Effective sample size have been many attempts by researchers to optimize the manufacturing process either by improving the composition of the material or by selecting proper process parameters for machining. Machinability is the ease with which the material is machined. The performance measure of machinability includes MRR, specific energy consumption, surface finish, tool life and chip flow pattern (Songmene et. al, 2011). To enhance certain properties aluminum is alloyed with copper, manganese, silicon, magnesium, zinc, etc. Aluminium alloys with magnesium and silicon are designated by 6XXX series. Al6061 has excellent mechanical properties and corrosion resistance (Xuewu Lia,2015). It also exhibits good weldability (Omega Research, 2002). There are cited literatures on optimization of the machining parameters which has the focus on surface roughness. The effect of process parameter like cutting speed, feed rate, depth of cut, amount of lubrication, type of tool, tool overhang and ageing of work material are investigated. The surface integrity effects of turned Al 6061 and Al6061-T6 is studied by Tohet. al. (2004). Sreejith (2008) found that machining with minimum quantity lubricant improved machinability compared with dry or flooded lubricant machining. Mukesh Kumar et. al. (2009) studied the effect of speed, feed rate and depth of cut using a coated carbide tool on Al6061-t4. HalilDemir et al (2009) investigated the effect of artificial ageing of aluminum on the surface finish of Al6061. Authors have reported the effect of ageing and cutting speed on the surface roughness. Optimization of cutting parameters was made by Carmita (2013) to minimize the energy consumption in turning process. Higher feed rate was found to reduce energy consumptionbut it increased the surface roughness. The influence of tool overhang on surface roughness was studied by Vinod et. al. (2014). The authors have reported that too small or too large overhang lead to poor surface finish. If the surface finish is within the acceptable range, it is desirable to improve the production rate by increasing MRR. In consideration of these factors, the present work attempts to optimize the process parameters to improve the MRR and hence productivity. 2. Experimental set up and methods Figure 1 shows the experimental setup used in the present work. Al6061 work pieces of diameter 40 mm and length 150 mm were used in the experiments. The cutting tool used is SiC insert. Al6061 contains 95% Al along with the traces of other elements like Fe, Si, Cu, Mn, Mg, Cr, Ti and Zn. Figure 2 shows the sample work pieces machined.The process parameters such as cutting speed, feed and depth of cut were chosen with each parameter having three levels as shown in the Table 1. Settings for levels of each process parameter is chosen based on the pilot study. For the present experimental condition the best suitable design is Taguchi’s L 9orthogonal array which is shown in Table 2. Taguchi’s orthogonal array enables the researchers to conduct the minimum number of experiments by optimum use of resources. Experiments were conducted with two replications with the supply of coolant SAE 40. The material MRR obtained in each run is determined by the weight loss method and corresponding values of MRR are shown in Table 2. The analysis is carried out using S/N ratio for Higher the Better (HB) criteria using the formula as given below. The SN ratio obtained in each run is also shown in Table 2.The
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
experimental results were analyzed using Signal to Noise ratio with the Higher the Better criteria as given by the equation (1).The relative effect of each process parameter is analyzed by Analysis of Variance (ANOVA). S N HB
ª§ 1 · 1 «¨© k ¸¹ ¦ ® y ¬ ¯ k
10 log
2
i 1
i
½º ¾» ¿¼
(1)
Fig. 1 Experimental set up
Fig. 2 Sample work-piece Table 1 Signal parameter and their levels Factor/Level 1 2 Cutting Speed (m/min) A 308 369 Feed (mm/rev) B 0.05 0.1 Depth of Cut (mm) C 1 1.5
Run 1 2 3 4 5 6 7 8 9
Table 2: Experimental designs with results Process parameters MRR (gm/minute) A B C k=1 k=2 1 1 1 20.00 24.97 1 2 2 59.99 59.99 1 3 3 119.9 134.9 2 1 2 35.99 41.98 2 2 3 95.98 95.98 2 3 1 72.06 90.07 3 1 3 55.99 55.99 3 2 1 56.05 56.05 3 3 2 125.8 150.0
3 429 0.15 2
SN ratio 26.88 35.56 42.06 31.74 39.64 38.01 34.96 34.97 42.69
401
402
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
3. Results and discussion 3.1 The analysis of effect of operating parameters on MRR using S/N ratio The present work investigates the effect of operating parameters such as cutting speed, feed rate and depth of cut. Table 2 shows the results of the replicated response in each trial. To assess the variability of the response, the analysis is carried out by S/N ratio using larger the better criteria. The effect of each process parameter at different levels is calculated using S/N ratio and is shown in Table 3. The effect of each factor on the response is also shown in the form of delta statistics in the same table. Based on the delta values the ranks are assigned to the factors in the ascending order of their influence. Among the chosen machining parameters, cutting speed, feed rate and depth of cut, the maximumSN ratio is seen for feed rate. This indicates that feed rate is the most predominant factor in MRR followed by depth of cut and the cutting speed. From the Table 3 it is seen that optimum combination of the process parameters to increase the MRR are A3B3C3. Table 3 Mean of S/N ratios Cutting Speed Feedrate Depthof cut 1 2 3 Delta Rank
34.83564 36.45585 37.81649 2.71 3
31.19531 36.72602 40.92461 9.73 1
33.28953 36.66595 38.89045 5.60 2
Figure 3 shows the main effect plot of SN ratio for MRR as response. It is seen from the plot that higher MRR is obtained at the process settings, cutting speed 429 m/min, Feed 0.15 mm/rev and depth of cut 2 mm. This combination of process parameter produces maximum SN ratio and hence the settings can be considered as optimum settings to the process parameters.Further, relative effect of each process parameter is determined by the Analysis of Variance (ANOVA) which is a statistical technique. In this technique the total variability of the response is separated with respect to individual contributions of each of the factors and the error. Table 4 shows the ANOVA values for the experimental results. The significance of the effect of each factor is evaluated using F test at 95 % confidence level. It is seen that F values of the all chosen process parameters are higher than the F critical values from the standard table. Hence all the chosen process parameters are considered as significantly affecting process parameters which have their influence on the material removal rate.
Fig. 3. Effect of operating parameters on MRR Further, Figure 4-6 shows the combined effect of process parameters on the MRR. As seen from the Figure 4, higher MRR is found to be produced at the settings of level 3 to both feed rate and the cutting speed. Further increase in these parameters increases the MRR almost linearly. The effect of feed rate seems to be highly significant compared
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
to cutting speed.Figure 5 shows the plot of MRR with respect to feed rate and depth of cut. It is seen that increase in the feed rate at any settings of cutting speed increases the MRR significantly. But it increases slightly with increase in depth of cut even at higher values of feed rate. It is also seen from the Figure 6 that the MRR exhibits more complex relationship with respect to the depth of cut and cutting speed. At the settings of the depth of cut at level 2 and cutting speed at level 3 exhibits better MRR. This shows that increasing depth of cut does not ensure increase in metal removal rate. The above phenomena is due to following facts: at higher cutting speeds more strain energy is available for the work-piece which leads to continuous and easy removal of the material thus producing higher MRR. But increase in depth of cut may cause tool chatter which reduces MRR. The higher feed rates promotes the axial movement of the cutting tool resulting in shifting of the cutting tool to new location at a faster rate thereby increasing MRR,but it creates rougher surface. Higher depth of cut creates higher thrust force on the cutting tool which push the tool radially outward. As a result the cutting tool will be released from the work-piece causing vibrations which results in decrease in MRR.
Source Cutting Speed Feed Rate Depth of Cut Residual Error Total
Table 4. ANOVA for the experimental data DF Adj SS Adj MS F 2 11.150 5.574 141.41 2 142.87 71.437 1812.10 2 47.719 23.859 605.22 2 0.079 0.039 8
150
MRR
100 3
50 2 1
2 Feed Rate
Cutting Speed
1
3
Fig. 4. The effect of feed rate and cutting speed on MRR
150
MRR
100 3
50 2 1
2 Depth of Cut
3
Feed Rate
1
Fig. 5. The effect of feed rate and depth of cut on MRR
403
404
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
150
MRR
100 3
50 2 1
2
3
Depth of Cut
Cutting Speed
1
Fig. 6. The effect of cutting speed and depth of cut on MRR 3.2 Prediction of MRR at optimum settings The expected MRR at optimum settings of the process parameters i.e., at A 3B3C3 is calculated as below. neff
Total number of experiments
(2)
1+Total degree of freedom of all factors
¦ Response value
T=
(3)
Total number of experiments
MRRae = Response at [A3+ B3+C3] –2T (4) Error mean square º ª d = F α, Error degree of freedom «¬ Effective sample size »¼
Confidence interval
MRR ae r d
1 2
(5) (6)
The confidence interval (d) is estimated to be 0.53 at 95 % confidence level. Hence the expected range of MRR is 141.54 to 142.64 gm/min. Further confirmation experiments were conducted at the optimum settings. The MRR obtained is found within the range established. 4. Conclusion Following conclusions are drawn from the present experimental work. x It is observed that the feed rate is most influential process parameters that influence MRR while turning of Aluminum 6061 followed by depth of cut and cutting speed. x The combined effect of feed rate and cutting speed and feed rate and depth of cut indicate that the maximum values of these factors yield maximum MRR. On the other hand the combined effect of depth of cut and cutting speed shows that depth of cut obtained at level 2 and cutting speed at level 3 results in better MRR. This shows that depth of cut is not the significant factor in increasing MRR. The feed rate controls the MRR.
References CarmitaCamposeco-Negrete, 2013, Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA, Journal of Cleaner Production, Volume 53, 15 pp. 195–203 Deepak, D., Rajendra, B. (2015). Investigations on the surface roughness produced in turning of Al6061 (as-cast) by taguchi method, International Journal of Research in Engineering and Technology, Volume 4 (8). HalilDemir, SüleymanGündüz, 2009, The effects of aging on machinability of 6061 aluminium alloy, Materials and Design vol 30, 1480–1483 Mukesh Kumar Barua, Anbuudayasankar, Measurement of surface roughness through RSM: effect of coated carbide tool on 6061-t4 aluminium, International Journal of Enterprise Network Management, Volume 4, Issue 2, DOI: 10.1504/IJENM.2010.037931
B. Rajendra and D. Deepak / Procedia Technology 24 (2016) 399 – 405
Paneerselvam, 2012, Design and analysis of experiments, PHI learning private ltd., ISBN 978-81-203-4499-0 C. J. Rao, D. Nageswara Rao, P. Srihari, 2013, Influence of cutting parameters on cutting force and surface finish in turning operation, International Conference On design and manufacturing, IConDM 2013, Procedia Engineering 64, pp. 1405 – 1415 P.S. Sreejith, Machining of 6061 aluminium alloy with MQL, dry and flooded lubricant conditions, 2008, Materials Letters, Volume 62, Issue 2, Pp.276–278 V. Songmene, R. Khettabi, I. Zaghbani, J. Kouam, and A. Djebara, 2011, Machining and Machinability of Aluminum Alloys, Aluminium Alloys, Theory and Applications, Tibor Kvackaj (Ed.), ISBN: 978-953-307- 244-9 Vinod Mishraa, Gufran S. Khanb, K.D. Chattopadhyaya, Keshva Nanda, RamaGopal V. Sarepakaa, 2014, Effects of tool overhang on selection of machining parameters and surface finish during diamond turning, Measurement, Volume 55, Pp. 353–361 Xuewu Lia, QiaoxinZhanga, Zheng Guo, Tian Shi, Jingui Yu, Mingkai Tang, Xingjiu Huang, 2015, Fabrication of superhydrophobic surface with improved corrosion inhibition on 6061 aluminum alloy substrate, Applied Surface Science, Volume 342, pp. 76–83
405