Study on the effect of process parameters with the use of minimum quantity lubrication and solid lubricants in turning

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(1)Indian Journal of Engineering & Materials Sciences Vol. 26, June-August 2019, pp. 220-225. Study on the effect of process parameters with the use of minimum quantity lubrication and solid lubricants in turning Mayur A Makhesana*, B K Mawandiya & K M Patel Mechanical Engineering Department, Institute of Technology, Nirma University, Ahmedabad 382 481, India Received 20 July 2017; Accepted 9 July 2019 Effective cooling and lubrication is necessary in order to control the amount of heat generated during machining. As the heat generated if not controlled, adversely affects the surface integrity and tool wear. Looking at the problems involved with the application of conventional cutting fluid in machining, an effort has been made in present work to assess the performance of solid lubricant applied with minimum quantity lubrication approach. The process performance has been measured in the form of surface roughness and power consumption in turning operation. The effect of process parameters on surface roughness and power consumption has been checked and the regression equation has been developed to predict the value of the same. The predicted values have been compared with that of actual experimental value for validation. Keywords: Turning, Solid lubricants, Minimum quantity lubrication. 1 Introduction The enormous reduction in the quantity of the lubricant as compared to conventional lubricating process is the main advantage of this technique. Minimum quantity solid lubrication can be considered as nearly dry machining process which uses very small amount of cutting fluid. There are fewer chances of human hazards in this technique as compared to conventional process as this technique is nearly dry machining process. The lubricant mixture is supplied through minimum quantity lubrication system containing air compressor, lubricant reservoir, nozzle, mixing chamber and control knobs. The lubricant mixture leaving the nozzle in form of aerosol can be efficiently diverted between the tool-work interfaces. The solid lubricants can be mixed with cutting fluid in lubricant mixture and applied in to the machining zone. The application of lubricant mixture with compressed air results in combined effect of cooling and lubrication and helps to reduce the friction between too and work interface. MQSL technique and conventional flood coolant process were compared by Reddy et al.1. Encouraged results reported with the use of MQSL over flood coolant. Minimum values of thrust force were —————— *Corresponding author (E-mail: [email protected]). reported with 15% to 20% concentration of solid lubricant in experiments. Due to the high heat generation, the quality of products gets reduced. So heat produced in machining is critical in terms of workpiece quality. To reduce the heat generation, there is a need for cutting fluid. Cutting fluid can be helpful in reducing the heat generation at cutting zone. But, it has been reported that cutting fluid can pollute the environment and it can be dangerous to human health. MQSL technique can also use to reduce the wastage of the cutting fluid. Sharma et al.2 have studied the effect of minimum quantity lubrication (MQL)/near dry machining (NDM), high- pressure coolant (HPC), cryogenic cooling, compressed air cooling and use of solid lubricants for the reduction in friction and heat at the cutting zone. Tests were conducted to obtain flank wear. ANOVA was used to obtain important parameter affecting heat generation. Surface characteristic, tool wear, fluid aerosol generation under different cooling condition was predicted using a mathematical model and application of FEA techniques. From experiments and analytical results, it was concluded that material having low machinability can be machined with cryogenic cooling which helps in increasing tool life without compromising on the environmental conditioning. The performance assessment of Minimum fluid application was done by Varadarajan et al.3 and the results were compared.

(2) MAKHESANA et al.: PROCESS PARAMETERS WITH USE OF SOLID LUBRICATION IN TURNING. with that of dry and conventional lubrication approach. Lower values of cutting forces is reported with the application of MQSL in machining. Pretorius et al.4 have checked the performance of the various range of polycrystalline diamond tools (PCD) for turning of Ti-6Al-2Sn-4Zr-6Mo with pressurized cutting fluid of 150 bar. Five different grades of PCD insert tools were used. Tools were differentiated by their grain size. Flank wear was taken as end criteria for test keeping all other parameters constant. From experimental readings, it was found that tool with the grain size of approximately 14 µm leads to an increase in tool life. Graphite as solid lubricant is applied by Singh et al.5 in turning operation. The improvement in process performance was reported in form of reduction in surface roughness and tool wear compared to that of dry machining. The previous research work clarifies that MQSL is the best technique to overcome the high heat generation in any machining process. If the lubricant is used in the right quantity, then appreciable results can be achieved. Hence, we have tried to develop an experimental setup for supplying minimum quantity lubricant during the machining process. Minimum quantity solid lubrication technique can be helpful to get good surface finish of the workpiece because solid lubricant particles adhere to the surface and most of the heat will be carried away with the chip. But in manufacturing industry, machining performance has been assessed by the different machining parameters like speed, feed, and depth of cut, cutting force and tool wear. These parameters greatly affect the workpiece quality. Kamata et al.6 investigated the performance of different coated tools namely CVD three-layers coating of TiCN/Al2O3/TiN, PVD super lattice coating of TiN/AlN and PVD monolayer coating of TiAlN and the results were compared for dry, wet and MQL machining. Reduction in tool life with an increase in cutting speed was reported for all inserts. The use of a combined technique of orthogonal array and analysis of variance was reported to investigate the effects of speed, feed and depth of cut on surface finish and power consumption during highspeed machining by Anirban et al.7 Response surface methodology was used to model the machining of homogenized 20% SiCpLM25 Al MMC manufactured through stircast route by Seeman et al.8. The effect of four machining parameters including cutting speed, feed rate, depth of cut, and machining time on flank wear and surface roughness were investigated. The. 221. influences of cutting speed, feed and depth of cut on cutting forces and surface roughness using the Taguchi method during turning of AISI 52100 bearing steel with CBN tool was investigated by Bouacha et al.9 Cutting forces and surface roughness was greatly affected by the depth of cut during machining. Taguchi design was used to optimize the surface roughness in turning operation by Kirby et al.10. Results showed the significant effect of feed and tool nose radius compared to other control factors. Negligible effect of noise factors was seen on the measured response. It was also concluded that the Taguchi design is an effective method to optimize surface roughness in turning process. During the machining of AISI 304 stainless steel, optimization of machining parameters was done by using Design of Experiment by Mahdavinejad et al.11 ANOVA was used to determine the effect of each parameter on surface roughness and tool wear. Results showed that cutting speed has a main influence on the flank wear and feed rate on surface roughness. The process parameters were optimized using desirability-based approach response surface methodology. Vikram et al.12 investigated the performance of coated tool and minimum quantity lubrication was assessed during hard turning. The improvement of process performance was reported in form of improved surface finish and reduction in tool wear. Looking to the status of available literature, an effort has been made in current work to assess the performance of minimum quantity lubrication and statistical models have been developed in order to predict the value of surface roughness and power consumption by considering the effect of process parameters in turning. 2 Experimental Details 2.1 Work piece material selection. EN8 material widely used in various industrial applications is selected for the present investigation. Chemical composition of the material is given in below Table 1. 2.2 Experiment details. The experimental setup is shown in Fig. 1. The main parts of the MQL system is connected with pipes. The compressed air supplied through air Table 1 — Chemical composition of the material C 0.4. Si 0.25. Mn 0.8. S 0.015. P 0.015.

(3) INDIAN J ENG MATER SCI, JUNE-AUGUST 2019. 222. compressor takes lubricant mixture from the reservoir. The lubricant mixture and compressed air is mixed in mixing block of the nozzle and leaves the nozzle in form of tiny aerosol particles. Graphite is added in lubricant mixture to perform the minimum quantity solid lubrication experiments. The levels of process parameters are identified based on the results of preliminary experiments and literature review. The different value of cutting speed is selected as 60, 90 and 120 m/min. Selected parameters and there levels are shown in the Table 2. 2.3 Statistical modelling. Using experimental data, a sustainable statistical model has been developed. For that Taguchi’s quality characteristics method is used. Statistical analysis of. Fig.1 — Experimental setup. Table 2 — Level of parameters Level. Cutting speed (m/min). Feed (mm/rev). Depth of cut (mm). -1 0 1. 60 90 120. 0.1 0.2 0.3. 0.5 1.0 1.5. the obtained experimental results is carried out by Analysis of Variance (ANOVA). The effect or relative contribution of each input factor on measured response can be obtained by ANOVA. It also permits to gain insight into which factors have main effects, interaction effects, less significant and noise. 3 Results and Discussion 3.1 Analysis of variance (ANOVA). The ANOVA test and statistical modelling are carried out using Design Expert 10.0 and Minitab 17. The ANOVA test is carried out. The significance level is kept α= 0.05 and confidence interval of 95%. The results are shown in the Table 3 and 4. The values of SS. R2 and Adjusted R2 show the fitting of model for the data given. SS (Sum of Squares) shows deviation of regression line from data. P-value decides the significance of a factor and interactions. The p-value more than 0.05 are insignificance. The interactions AB, A2, B2 and C2 are having values higher than 0.05 for response power hence neglected during test. For surface roughness those are AC, B2 and C2. The value F shows the contribution of factors in producing responses. Here the value for Depth of cut is highest in case of power. It shows that change in depth of cut is most likely to change the Power consumption relative to other factors. For surface roughness, the value of F is highest for feed. That means Feed is the most significant contributing factor. Also the value of residual is <1.5% for both the responses. This suggests that the factors avoided are negligibly significant. In the model, along with factors, interactions are also included. This shows that the model proposed is quadratic not linear. Table 5 and 6 shows the values of R2, adjusted R2 and predicted R2. These values measure the accuracy of the model. Higher the values higher the accuracy.. Table 3 — Summary table for power consumption Source Model A-Speed B-Feed C-Depth of Cut AC BC Residual Lack of Fit Pure Error Cor Total. Sum of Squares. df. Mean Square. F Value. p-value Prob > F. 7.513E+005 1.240E+005 53868.00 3.411E+005 12100.00 10816.00 10814.40 10782.40 32.00 7.621E+005. 5 1 1 1 1 1 9 7 2 14. 1.503E+005 1.240E+005 53868.00 3.411E+005 12100.00 10816.00 1201.60 1540.34 16.00. 125.05 103.22 44.83 283.90 10.07 9.00. < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0113 0.0150. 96.27. 0.0103.

(4) MAKHESANA et al.: PROCESS PARAMETERS WITH USE OF SOLID LUBRICATION IN TURNING. 223. Table 4 — Summary table for surface roughness. Source. Sum of Squares. df. Mean Square. F Value. p-value Prob > F. Model A-Speed B-Feed C-Depth of Cut AB BC A2 Residual Lack of Fit Pure Error Cor Total. 20.41 0.96 8.94 0.079 0.65 0.49 0.96 0.43 0.43 1.397E-003 20.84. 6 1 1 1 1 1 1 8 6 2 14. 3.40 0.96 8.94 0.079 0.65 0.49 0.96 0.054 0.071 6.985E-004. 63.47 17.99 166.83 1.48 12.16 9.08 17.92. < 0.0001 0.0028 < 0.0001 0.2584 0.0082 0.0167 0.0029. 101.97. 0.0097. Table 5 — Model summary for power consumption. R-Squared Adj R-Squared Pred R-Squared. 0.9858 0.9779 0.9397. Table 6 — Model summary for surface roughness. R-Squared Adj. R-Squared Pred. R-Squared. 0.9794 0.9640 0.9260. The value of R2 shows how well the model fits the data. Value of 0.9858 and 0.9794 suggests that the model is accurate. Adjusted R2 is the variation of R2 to compare the different models of different number of factors. Predicted R2 uses predicted values by using model along with the actual experimental values. As all the values are more than 90%, the model is reasonably adequate. Along with the statistical summary in the table above the following figure shows a graph between two values: experimental runs for both the actual and the predicted values by this model. As we can see that all the points quite fall on the 45-degree line. This shows that the model proposed is reasonably adequate. This is true for both the responses that are power and surface roughness. 3.2 Regression equation. The statistical model resulted in a quadratic equation as below. It depicts the relationship between the responses (power, surface roughness) and factors (cutting speed Cs, Feed F, depth of cut Dc). Power = -12 - 5.21 Cs + 965 F - 360 Dc + 0.0378 Cs*Cs -1900 F*F + 84.0 Dc*Dc + 5.00 Cs*F + 3.667 Cs*Dc + 1040 F*Dc ... (1) Surface Roughness = 11.96 - 0.1606 Cs - 9.67 F 4.211 Dc + 0.000579 Cs*Cs + 11.04 F*F + 0.315 Dc*Dc + 0.1345 Cs*F + 0.01612 Cs*Dc + 6.98 F* Dc ... (2). Table 7 — For power consumption. Predicted. Actual. Difference. 104.35 327.35 434.65 717.65 104.175 407.525 354.465 877.835 101.8 411.16 250.8 768.16 380.98 380.98 380.98. 80 320 440 740 120 420 340 860 108 420 240 760 380 384 374. -24.35 -7.35 5.35 22.35 15.825 12.475 -14.465 -17.835 6.2 8.84 -10.8 -8.16 -0.98 3.02 -6.98. The predicted values of power and surface roughness are shown in the Table 7 and 8. The differences are well below 2.5 SDs, which is reasonable. 3.3 Relationship plots. Figure 2 and 3 shows the contour plots for the interaction that has been found significant for Power and surface roughness, respectively. These plots are extremely useful when dealing with the interactions. From the contour plot given below for a given interaction combination, we can find out the value of the response. The model proposed is quadratic, not linear. For that along with single factor effects, it is required to take into consideration the various interactions. These are speed-feed, speed-depth of cut and feeddepth of cut. The 3D surfaces in the figures below are another way to show factor- response relationship. Compared to the earlier plots this method is more visually appealing and didactic. It is also called the response.

(5) INDIAN J ENG MATER SCI, JUNE-AUGUST 2019. 224. Table 8 — For surface roughness. Predicted. Actual. Difference. 3.5361 5.6679 2.0397 5.7855 4.91085 4.22985 3.73785 4.02405 2.79325 1.89785 5.03405 5.53465 3.6258 3.6258 3.6258. 3.586667 5.791667 1.902 5.7215 4.896333 4.052167 3.901667 4.024833 2.744 2.0085 4.910667 5.5705 3.632167 3.637 3.589. 0.050566667 0.123766667 -0.1377 -0.064 -0.014516667 -0.177683333 0.163816667 0.000783333 -0.04925 0.11065 -0.123383333 0.03585 0.006366667 0.0112 -0.0368. Fig. 4 — Plots for power consumption.. Fig. 2 — Contour plots for power consumption.. Fig. 3 — Contour plots for surface roughness.. Fig. 5 — Plots for surface roughness..

(6) MAKHESANA et al.: PROCESS PARAMETERS WITH USE OF SOLID LUBRICATION IN TURNING. surface. It is created by keeping one control factor at constant level (mostly at the mean position) and varying the other two variables respectively. These two variables are plotted on x and y-axis. The response is plotted on the Z axis. Hence it creates a curved surface, known as a response surface. Same has been carried out for both the responses power and surface roughness, in Fig 4 and 5, respectively. The conclusions of 3D surfaces are same as the 3D contour plots.. 4 Conclusions The study showcased the effectiveness of MQSL as an alternative to the use of conventional cutting fluid in machining. It has the advantages like lesser hazardous effects and better product quality. It is revealed that, feed is the most significant factor for surface roughness whereas depth of cut plays an important role for the value of power consumption. Furthermore, the ANOVA test reveals that the statistical model developed for further optimization is reasonably valid and the final optimum parameters settings obtained are shown in table below and have been verified with help of experimentation.. 225. References 1. Reddy N S K, Nouari M & Yang Minyang, Int J Mach Tool Manuf, 50 (2010) 789. 2 Sharma V S, Dogra M & Suri N M, Int J Mach Tool Manuf, 49 (2009) 435. 3 Varadarajan M A S, Philip P K & Ramamoorthy B, Int J Mach Tool Manuf, 42 (2002) 193. 4 Pretorius C J, Sein Leung Soo, Aspinwall D K, Harden P M, M'Saoubi R & Mantle A L, CIRP Annals-Manuf Technol, 64 (2015) 109. 5 Dilbagh S M & Rao P V, Int J Adv Manuf Tech, 38 (2008) 529. 6 Kamata Y & Obikawa T, J Mater Process Technol, 192 (2007) 281. 7 Bhattacharya, A., Das, S., Majumder, P & Batish A, Prod Eng Res Devel, 3 (2009) 31. 8 Seeman M, Ganesan, G, Karthikeyan R & Velayudham A, Int J Adv Manuf Technol, 48 (2010) 613. 9 Bouacha K, Yallese Ma, Mabrouki T & Rigak, Int J Refract Met Hard Mater, 28 (2010) 349 10 Kirby E D, Zhang Z, Chen J C & Chen J, Int J Adv Manuf Technol, 30 (2006) 1021. 11 Mahdavinejad R A, Saeedy S, Sadhana, 36 (2011) 963. 12 Vikram C H, Kumar R & Ramamoorthy B, J Mat Process Technol,185 (2007) 210..

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