Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 13, December 2018, pp. 431–441, Article ID: IJMET_09_13_045 Available online at http://iaeme.com/Home/issue/IJMET?Volume=9&Issue=13 ISSN Print: 0976-6340 and ISSN Online: 0976-6359

ANALYSIS AND OPTIMIZATION OF MACHINING PARAMETERS IN THROUGH FEED CENTERLESS GRINDING OF HIGH CARBON STEEL

S.Dinesh Department of Mechanical Engineering, K.Ramakrishnan College of Technology, Kariyamanikam Road, Samayapuram, Tiruchirappalli – 621112, Tamilnadu, India.

T. Rajkumar Department of Mechanical Engineering, K.Ramakrishnan College of Technology, Kariyamanikam Road, Samayapuram, Tiruchirappalli – 621112, Tamilnadu, India.

S. Muthukumarasamy Assistant Professor Department of Mechanical Engineering, Vel Tech, Avadi, Chennai- 600062, India.

G.Sathish Kumar Department of Mechanical Engineering, K.Ramakrishnan College of Technology, Kariyamanikam Road, Samayapuram, Tiruchirappalli – 621112, Tamilnadu, India.

S.V.Kajendirakumar Department of Mechanical Engineering, K.Ramakrishnan College of Technology, Kariyamanikam Road, Samayapuram, Tiruchirappalli – 621112, Tamilnadu, India.

B.Suresh Kumar Department of Mechanical Engineering, K.Ramakrishnan College of Technology, Kariyamanikam Road, Samayapuram, Tiruchirappalli – 621112, Tamilnadu, India.

ABSTARCT Surface texture is one of the important responses for the machine tool design because poor surface texture causes nucleation, subsurface cracks etc. For this reason, machining process is introduced such as cylindrical grinding, surface grinding, Centerless grinding etc. for achieving better surface texture. In that, centerless grinding plays a vital role in getting better surface finish through infeed, endfeed and through feed methodology. So, the main aim of this work is to investigate the machining parameters such as regulating wheel speed, regulating wheel angle and depth of cut on the surface roughness. Simultaneously the productivity and the surfaces roughness are the conflictive objective because the high productivity affects the surface texture properties. So, the selection of

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the machining parameters is important for process planning engineers in decision making. In this regard, there are cost of mathematical modeling techniques where utilized. In that, Response surface methodology and regression analysis draws more attention among researchers. Grey relational analysis is used to predict the optimal machining parameters. Key words: Machining time; Surface roughness; Response surface methodology; Productivity;Grey relational analysis. Cite this Article: S.Dinesh, T. Rajkumar, S. Muthukumarasamy, G.Sathish Kumar, V.Kajendirakumar and B.Suresh Kumar, Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel, Journal of Mechanical Engineering and Technology, 9(13), 2018, pp. 431–441 http://iaeme.com/Home/issue/IJMET?Volume=9&Issue=13 1. INTRODUCTION Prabhu Sethuramalingam [1] optimized the grinding parameters when ground with carbon nanotube based wheel by developing an empirical model with regression analysis and grey relational analysis. The feed was found to be the significant parameter using Analysis by variance method. Phan Bui Khoi et al [2] represented that to improve centerless grinding process, it is necessary to optimize surface roughness and roundness error which are the critical quality constraints for the selection of grinding factors in process planning. Ohmori et al [3] said that use of Electrolytic in-process dressing and Electro-discharge truing in centerless grinding process improved the surface roughness and precision when compared to conventional grinding process. Yongho WU et al [4] has represented that centerless grinding operation is a complicated rounding mechanism. It was proved that the frequency characteristics of the grinding force can be used to predict the waviness decrease rate.Arshad Noor Siddiquee et al [5] revealed that combining grey relational analysis with principal component analysis shall be utilized to attain the optimal arrangement of parameters in centerless grinding. Dhavlikar et al [6] discovered the optimal parameters for minimizing the roundness error by opting taguchi and dual response methodology. Fukuo Hashimoto [7] et al discussed the theory of centerless grinding process including modeling, simulation, advancements in process monitoring and developments in grinding processes. Xu et al [8] improved the roundness of the component by performing infeed – grinding in surface grinder and found that the machining accuracy was at its peak at least feed rate, higher stock removal and rotational speed. Hashimoto et al [9] developed an algorithm to predict the optimal set-up conditions depending upon the process. He also explained the guidelines for set- up in order to avoid the stability criteria such as spinners, chatter vibration and roundness error. Xu et al [10] examined the effect of process parameters on roundness and revealed that the roundness can be improved when high material removal is set. Also, better roundness was encountered at lower feed rate and elevated workpiece rotational speed.Jorge Alvarez et al [11]improved the grinding process by continuously varying feed rate concerning production rate, geometrical and surface tolerance which lead to competent cycles without stating material removal rate and feed rate. Krajnik [12] stated that the response surface methodology used for designing grinding factors involves incorporation of regression analysis, design of experiments for fixing the model to pragmatic data and optimization. Garitaonandia et al [13] developed a FE model to improve the chatter stability and to optimize the active chatter control system, angular velocity and center height angle based on the roundness’ of the machined components. Alessandro Rascalha [14] developed a empirical model and optimized the machining parameters with taguchi method based on surface roughness. The results proved to reduce the dressing time, increase the production rate and reduced surface roughness. Kim et al [15] developed a AE

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Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel monitoring system to monitor the abnormalities caused due to improper set-up conditions in grinding process that facilitate the operator to ensure proper working of the machine. 2. EXPERIMENTAL SET UP The experimentation was done in Star make model center less grinding machine. The specification of machine tool is shown table 1.Stop watch was used for measuring the machining time and MITUTOYO make surface roughness tester was used for measuring the surface roughness of the ground components. Vernier caliper was used to measure the length and diameter of the work piece. The surface roughness tester is shown in fig 2.

Table 1 Machine tool specification Abrasive speed 1219 surface m/min (4000 surface ft/min) Regulating wheel speed 50 rpm Through – feed rate 3.05 m/min (10 ft/min) Grinding pressure 0.148 amp/cm (0.375 amp/inch) Coolant Water based soluble oil

Figure 1– Centerless Grinding Machine Figure 2 – Surface Roughness Tester

2.1. Process variables and their limits The process parameters that have been considered for experimentation are regulating wheel speed, regulating wheel angle and depth of cut. The operational array of every parameter with their units is listed in table 1. High Carbon Steel of 16 mm diameter and 100 mm length were used for the experimentation processes.

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Table 2 - Process Variables and their Limits Input Least Point Modest Point Utmost Point Parameters Regulating wheel 12 25 46 speed(rpm) Regulating wheel 2 3 4 angle (degree) Depth of cut 0.1 0.16 0.2 (mm)

2.2. Selection of experimental design A random design containing 27experiments were conducted and the combinations for experimentation are mentioned in the table 2.

Table 3 - Process Variables and their Limits Regulating Regulating Experiment Depth of cut wheel Speed wheel angle number (mm) (rpm) (degrees) 1 12 2 0.1 2 12 2 0.16 3 12 2 0.2 4 12 3 0.1 5 12 3 0.16 6 12 3 0.2 7 12 4 0.1 8 12 4 0.16 9 25 4 0.2 10 25 2 0.1 11 25 2 0.16 12 25 2 0.2 13 25 3 0.1 14 25 3 0.16 15 25 3 0.2 16 25 4 0.1 17 25 4 0.16 18 25 4 0.2 19 46 2 0.1 20 46 2 0.16 21 46 2 0.2 22 46 3 0.1 23 46 3 0.16 24 46 3 0.2 25 46 4 0.1 26 46 4 0.16 27 46 4 0.2

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Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel 3. ANALYSIS OF RESULTS The impact of the regulating wheel speed, regulating wheel angle, depth of cut over the machining time and Surface roughness by conducting the experiments based on the combinations mentioned in the above table were tabulated in table 3.

Table 4 - Response values from experimental work Surface Experiment Machining Roughness no. Time (sec) Ra (µm) 1 28.6 2.97 2 83.7 1.71 3 25.4 2.31 4 14.2 4.2 5 20.9 3.14 6 68.6 2.21 7 53.3 2.23 8 27.6 3.13 9 23.9 2.74 10 11 2.54 11 15 3.22 12 19.6 2.12 13 9.9 1.79 14 5.8 3.69 15 27.6 2.34 16 6.1 5.04 17 16.7 8.51 18 16.9 1.88 19 7.6 5.34 20 5.3 4.35 21 4.3 5.04 22 5.9 4.52 23 3.8 2.95 24 4.8 4.13 25 2.9 4.69 26 4.7 6.25 27 7.76 5.39

3.1. Grey Relational Analysis Grey relational Analysis involves data representation and data normalization. The Grey relational Grade (GRG) is used to optimize the machining parameters after location of grey relational coefficient. The normalized values of the responses fall between 0 and 1. The average GRG is calculated and the ranking is done from maximum to minimum, to represent the desirability. The values are tabulated in table 4.

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Table 4 – Grey relational grades for the responses NOR NOR DEL DEL TOTAL S.NO GRC 1 GRC 2 GRG RANK MRR SR MRR SR GRC 1 0.32 0.81 0.68 0.19 0.42 0.73 1.15 0.58 11 2 1.00 1.00 0.00 0.00 1.00 1.00 2.00 1.00 1 3 0.28 0.91 0.72 0.09 0.41 0.85 1.26 0.63 8 4 0.14 0.63 0.86 0.37 0.37 0.58 0.94 0.47 17 5 0.22 0.79 0.78 0.21 0.39 0.70 1.10 0.55 13 6 0.81 0.93 0.19 0.07 0.73 0.87 1.60 0.80 2 7 0.62 0.92 0.38 0.08 0.57 0.87 1.44 0.72 3 8 0.31 0.79 0.69 0.21 0.42 0.71 1.12 0.56 12 9 0.26 0.85 0.74 0.15 0.40 0.77 1.17 0.59 9 10 0.10 0.88 0.90 0.12 0.36 0.80 1.16 0.58 10 11 0.15 0.78 0.85 0.22 0.37 0.69 1.06 0.53 15 12 0.21 0.94 0.79 0.06 0.39 0.89 1.28 0.64 6 13 0.09 0.99 0.91 0.01 0.35 0.98 1.33 0.67 4 14 0.04 0.71 0.96 0.29 0.34 0.63 0.97 0.49 16 15 0.31 0.91 0.69 0.09 0.42 0.84 1.26 0.63 7 16 0.04 0.51 0.96 0.49 0.34 0.51 0.85 0.42 22 17 0.17 0.00 0.83 1.00 0.38 0.33 0.71 0.35 26 18 0.17 0.98 0.83 0.03 0.38 0.95 1.33 0.66 5 19 0.06 0.47 0.94 0.53 0.35 0.48 0.83 0.42 24 20 0.03 0.61 0.97 0.39 0.34 0.56 0.90 0.45 19 21 0.02 0.51 0.98 0.49 0.34 0.51 0.84 0.42 23 22 0.04 0.59 0.96 0.41 0.34 0.55 0.89 0.44 20 23 0.01 0.82 0.99 0.18 0.34 0.73 1.07 0.53 14 24 0.02 0.64 0.98 0.36 0.34 0.58 0.92 0.46 18 25 0.00 0.56 1.00 0.44 0.33 0.53 0.87 0.43 21 26 0.02 0.33 0.98 0.67 0.34 0.43 0.77 0.38 25 The optimized parameters are tabulated in table 5 based on the average Grey relational grades that were obtained from analysis of machining parameters using Grey relational analysis.

Table 5 – Selected machining parameters based on GRG Regulating Regulating Depth of Wheel wheel angle cut speed Least Point 5.89233 4.43413 4.73039 Modest 4.97782 5.04364 4.85166 Point Upmost 3.54468 4.51585 4.83278 Point

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Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel

6 5.89233

5.04364

4.97782

4.85166 4.83278

5 4.73039

4.51585 4.43413

4 G 3.54468 Least Point R G 3 Modest Point Upmost Point 2

0 Regulating Wheel Regulating wheel Depth of cut speed angle

Figure 3 – Optimized machining parameters based on GRG The optimized machining parameters are shown in fig 3. The regulating wheel speed, regulating wheel angle and depth of cut were identified as 12 rpm, 3 degree and 0.16 mm.

3.2. Analysis by Variance

3.2.1. Analysis of machining time The model is significant for an F-Value of 11.48. The chance of “Model F- value” to be large due to noise is 0.01%. This indicates that the model terms are significant. The value of “Adj-Squared” is 0.7905 and the signal to noise ratio is measured using “Adeq Precision”. The model possesses a ratio of 11.52 showing an adequate signal.

3.2.2. Analysis of Surface Roughness The significance of model is achieved at an F- value of 3.75.The possibility of “Model F- Value” to be peak due to noise is 2.58%. This represents that the model terms are significant. The “Adj – Squared” value is 0.2480 and the S/N ratio is calculated by “Adeq Precision”. The adequate signal is confirmed with a value of 5.737for this model.

Table 6 ANOVA table for Machining time and Surface Roughness Machining time p-value Source Sum of Squares DOF Mean square F value Prob > F Model 8879.391 9 986.599 11.48243 < 0.0001 A-Regulating Wheel Speed 5082.322 1 5082.322 59.15008 < 0.0001 B-Regulating Wheel Angle 1104.337 1 1104.337 12.85271 0.0025 C-Depth of cut 156.3663 1 156.3663 1.819853 0.1961 AB 1284.126 1 1284.126 14.94517 0.0014 AC 77.68485 1 77.68485 0.904127 0.3558 BC 0.130542 1 0.130542 0.001519 0.9694 A^2 911.4417 1 911.4417 10.60772 0.0049 B^2 127.8614 1 127.8614 1.488101 0.2402 C^2 20.26116 1 20.26116 0.235807 0.6338

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S.Dinesh, T. Rajkumar, S. Muthukumarasamy, G.Sathish Kumar, V.Kajendirakumar and B.Suresh Kumar

Residual 1374.76 16 85.92249 Surface Roughness (SR) p-value Source Sum of Squares DOF Mean square F value Prob > F Model 22.0689 3 7.3563 3.747695 0.0258 A-Regulating Wheel Speed 15.95723 1 15.95723 8.129469 0.0093 B-Regulating Wheel Angle 0.690518 1 0.690518 0.351787 0.5592 C-Depth of cut 5.450288 1 5.450288 2.77667 0.1098 Residual 43.1835 22 1.962887

3.3. Regression analysis The basic requirement to define the interaction of reliant and sovereign variable is a statement of statistical model. The need of regression model is so that, this work contains more than one sovereign variable. The below mentioned equations (1) and (2|) demonstrate the pragmatic relationship between dependent and independent variables. The regulating wheel speed, regulating wheel angle and depth of cut are denoted by N,A and D. Machining time = +226.82376 − 6.84287 ∗ N − 62.67517 ∗ A + 86.46189 ∗ D + 0.73552 ∗ N ∗ A + 3.58525 ∗ N ∗ D + 0.052417 ∗ N2 + 5.31784 ∗ A2 − 880.07938 ∗ D2 (1) Surface Roughness = −0.47280 + 0.058144 ∗ N + 0.20882 ∗ A + 11.75551 ∗ D (2)

3.3.1. Comparison of Experimental and RSM value of Machining time

Figure 4 Comparison of predicted and empirical value of Machining time. The model was developed using 26 data sets of experimental design and was validated. The empirical values of the machining time were compared with the predicted values and the percentage of digression is tabulated in table 7. The mean digression between the predicted and empirical results is 0.00483. Hence, the equation 1 can be used to predict the Machining time for centerless grinding process. The deviation between the empirical and predicted values was very smaller. The validation of the empirical values is shown in figure 4.

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Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel

Table 7 Empirical (vs) Predicted value of machining time. Exp. No. Experimental MT Predicted MT % of Deviation 1 28.6 29.26 0.02 2 83.7 64.02 -0.31 3 25.4 29.26 0.13 4 14.2 20.13 0.29 5 20.9 20.13 -0.04 6 68.6 64.02 -0.07 7 53.3 42.72 -0.25 8 27.6 42.72 0.35 9 23.9 30.02 0.20 10 11.0 9.16 -0.20 11 15.0 21.23 0.29 12 19.6 21.23 0.08 13 9.9 9.16 -0.08 14 5.8 -3.54 2.64 15 27.6 30.02 0.08 16 6.1 -3.54 2.73 17 16.7 21.23 0.21 18 16.9 5.26 -2.21 19 7.6 4.25 -0.79 20 5.3 4.25 -0.25 21 4.3 0.64 -5.71 22 5.9 -0.62 10.46 23 3.8 0.64 -4.93 24 4.8 -0.62 8.70 25 2.9 10.38 0.72 26 4.7 10.38 0.55

3.3.2. Comparison of Experimental and RSM value of Surface Roughness

Figure 5 Comparison of predicted and empirical value of SR.

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The data sets of experimental design was used to develop and validate the model. The observed values of the surface roughness were compared with the predicted values and the percentage of excursion is tabulated in table 8. The mean deviation between the predicted and the observed value was zero. Therefore the equation 2 can be used in prediction of surface roughness. The validated empirical values are shown in figure 5.

Table 8 Empirical (vs) Predicted values of Surface roughness Experimental SR Predicted SR Exp. No. % of Deviation (microns) (microns) 1 2.97 3.20 0.07 2 1.71 2.52 0.32 3 2.31 3.20 0.28 4 4.20 2.94 -0.43 5 3.14 2.94 -0.07 6 2.21 2.52 0.12 7 2.23 2.03 -0.10 8 3.13 2.03 -0.54 9 2.74 2.57 -0.06 10 2.54 3.49 0.27 11 3.22 3.75 0.14 12 2.12 3.75 0.43 13 1.79 3.49 0.49 14 3.69 4.17 0.11 15 2.34 2.57 0.09 16 5.04 4.17 -0.21 17 8.51 3.75 -1.27 18 1.88 2.99 0.37 19 5.34 4.50 -0.19 20 4.35 4.50 0.03 21 5.04 4.00 -0.26 22 4.52 5.18 0.13 23 2.95 4.00 0.26 24 4.13 5.18 0.20 25 4.69 4.92 0.05 26 6.25 4.92 -0.27 4. CONCLUSION The machining parameters were optimized by developing an empirical model using grey relational analysis and regression analysis. Grey relational analysis revealed that the optimum machining parameters of regulating wheel speed, regulating wheel angle and depth of cut were 12 rpm, 3 degree and 0.16 mm [16-17]. The best results for surface roughness and machining time can be achieved at minimum regulating wheel speed, maximum regulating wheel angle and modest depth of cut. The regression model showed that depth of cut proved to have more effect on the machining time and regulating wheel angle was found to be the most significant parameter for surface roughness. The results embrace the fact the percentage of average deviation for machining time between predicted and experimental values was 0.49 and for that of surface roughness was found to be zero.

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Analysis and Optimization of Machining Parameters in Through Feed Centerless Grinding of High Carbon Steel REFERENCES [1] Prabhu Sethuramalingam & Babu Kupusamy Vinayagam (2016),’ Multi Objective Optimization of Multi Wall Carbon Nanotube Based Nanogrinding Wheel Using Grey Relational and Regression Analysis’, Journal of Industrial Engineering, India Series C. [2] Phan Bui Khoi, Do Duc Trung,, Ngo Cuong & Nguyen Dinh Man (2015), ‘Research on Optimization of Plunge Centerless Grinding Process using Genetic Algorithm and Response Surface Method’, International Journal of Scientific Engineering and Technology (ISSN : 2277-1581),Volume No.4 Issue No.3, pp : 207-211. [3] Ohmori.H, Li.W, Makinouchi.A &Bandyopadhyay.B.P(2000), Efficient and precision grinding of small hard and brittle cylindrical parts by the centerless grinding process combined with electro-discharge truing and electrolytic in-process dressing’, Journal of Material Processing Technology, pp. 322-327. [4] Yongbo Wu, Jun Wan , Yufeng Fan&Masana Kato(2005), ‘Determination of waviness decrease rate by measuring the frequency characteristics of the grinding force in centerless grinding’, Journal of Material Processing Technology, pp. 563-569. [5] Arshad Noor Siddiquee & Zahid A. Khan & Zulquernain Mallick(2010),’Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding’, International Journal of Advanced Manufacturing Technology, pp. 983–992. [6] Dhavlikar.M.N, Kulkarni.M.S&Mariappan.V (2003), ‘Combined Taguchi and dual response method for optimization of a centerless grinding operation’, Journal of Material Processing Technology, pp. 90-94. [7] Fukuo Hashimoto, Ivan Gallego (2), Joao F.G. Oliveira , David Barrenetxea,Mitsuaki Takahashi, Kenji Sakakibara , Hans-Olof Stalfelt, Gerd Staadt, Koji Ogawa (2014), ‘Advances in centerless grinding technology’CIRP Annals - Manufacturing Technology 61,pp. 747–770. [8] Xu.W, Wu.Y (2011),‘A new in-feed centerless grinding technique using a surface grinder’, Journal of Materials Processing Technology 211, pp. 141–149. [9] Hashimoto.F, Lahoti.G,D(2004), ‘Optimization of Set-up Conditions for Stability of The Centerless Grinding Process’, CIRP Annals - Manufacturing Technology – 53, pp. 271-274. [10] Xu.W, Wu.Y, Sato.T, Lin.W(2010), ‘Effects of process parameters on workpiece roundness in tangential-feed centerless grinding using a surface grinder’, Journal of Materials Processing Technology 210, pp.759–766. [11] Jorge Alvarez, David Barrenetxea, Jose Ignacio Marquinez, Iñigo Bediaga & Ivan Gallego (2014),’ Continuous variable feed rate: a novel method for improving infeed grinding processes’, International Journal of Advanced Manufacturing Technology, pp. 53–61. [12] Krajnik.P,Kopac.J,Sluga.A (2005),’Design of grinding factors based on response surface methodology’,Journal of Materials Processing Technology, pp. 629–636. [13] Garitaonandia, J. Albizuri, J.M. Hernandez-Vazquez, M.H. Fernandes, I. Olabarrieta & D. Barrenetxea. (2013),’Redesign of an active system of vibration control in a centerless grinding machine: Numerical simulation and practical implementation’, Precision Engineering, pp 562– 571. [14] Alessandro Rascalha , Lincoln Cardoso Brandão & Sergio Luiz Moni Ribeiro Filho (2013), ‘Optimization of the dressing operation using load cells and the Taguchi method in the centerless grinding process’, International Journal of Advanced Manufacturing Technology, pp. 1103–1112. [15] Kim.H.Y, Kim.S.R, Ahn.J.H & Kim.S.H (2001),’Process monitoring of centerless grinding using acoustic emission’, Journal of material processing technology 111, pp. 273-278. [16] Yokeswaran. R, Karuppusamy. S & Arul. S (2018), “Experimental Investigation and Optimization of Welding Parameters of Metal Inert Gas Welding Process in joining dissimilar metals”, International Journal of Mechanical and Production Engineering Research and Development (IJMPERD), Vol. 8, Issue 1, pp.no 415-422. [17] Venkatesh.R, Vijayan.V (2016), ‘Performance Evaluation of Multipurpose Solar Heating system’,Mechanics and Mechanical Engineering Vol. 20, Issue 4 , pp.no 359–370.

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