MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN SURFACE GRINDING OFSiC ADVANCED CERAMIC MATERIAL

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1 MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN SURFACE GRINDING OFSiC ADVANCED CERAMIC MATERIAL Binu Thomas 1,Eby David 2,Manu R 3*, 1 M.Tech Scholar,MED,NIT,Calicut,673601,binukthomas@rediffmail.com 2 Phd Scholar, MED,NIT,Calicut,673601,ebydavid@gmail.com 3* Associate Professor,MED,NIT,Calicut,673601,manu@nitc.ac.in Abstract Advanced ceramics are increasingly utilized in various engineering applications such as aerospace, marine, automobile etc. But hard and abrasive particles in advanced ceramic materials create unusual machining problems. Machining of advanced ceramics, require tool materials of very high wear resistance because of the presence of hard abrasive particles. Grinding is commonly used for producing parts of high precision and high surface quality from advanced ceramics. But only a few investigationswere carried out on grinding of advanced ceramics till date. The objective of this work is to model and optimize surface of Silicon carbide (SiC) advanced ceramic material subjected to surface grinding process. In the present work,an analytical model for surface (Ra) in surface grinding of SiC advanced ceramic material is proposed. Effectiveness of this model is evaluated by comparison with the experimental results. Finally optimization of surface is done by considering machining parameters like table feedrate, depth of cut and wheel speed using Response Surface Methodology in Design Expert software. Keywords: Advanced Ceramics, Surface Roughness, Response Surface Methodology, Design expert 1Introduction Advanced ceramic materials are the new and emerging family of ceramics, possessing superior properties and are widely used in aircrafts, automotive, defense and aerospace industries as well as other advanced industries.grinding is widely used machining process in advanced ceramic industry for surface finishing. Grinding of advanced ceramic materials is a rather complex task owing to the hard and abrasive nature of the material. Agarwal and Rao(2010)developed an analytical model for surface prediction of ground ceramics, based on the analysis of the grooves left by the grains that interact with the workpiece, which is characterized by the undeformed chip.jiang and Hong(2013)established a new numerical model which described the micro-interacting situations between grains and workpiece material in grinding contact zone.gopal and Rao(2003)carried out experimental studies to obtain optimum conditions for silicon carbide grinding using genetic algorithms. The effect of wheel grit size and grinding parameters such as wheel depth of cut and work feed rate on the surface and damage are investigated.agarwal and Rao(2005) established a new analytical surface model on the basis of stochastic nature of the grinding process, governed mainly by the random distribution geometry and the random distribution of cutting edges and by assuming the profile of groove generated by an individual grain to be an arc of circle.snoeys et al.(1974)and Kedrov(1980)proposed the empirical model of surface using grinding experiments and showed that surface was related through empirical constantsfor a given dressing condition.xhou and Xi (2002) proposed a new model for surface prediction in ceramic grinding by taking into consideration of random distribution of the grain protrusion heights using Gaussian distribution model.alao and Konneh(2011)used Tanguchi method and Box-Behnken designs forminimizing surface in precision grinding of silicon using resin bonded diamond wheels. Taguchi method was used to study the effect and optimization of grinding parameters while Box- Behnken method was utilised to develop a mathematical model relating the average surface to the grinding parameters.lian etal.(2014)developed annew empirical model of surface in grinding of engineering ceramics and experimentally validated the model for a given dressing condition. Surface is one of the most important factors in assessing the surface quality of a ground component in advanced ceramics. However, no 311-1

2 MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN SURFACE GRINDING OFSiC ADVANCED CERAMIC MATERIAL comprehensive model has been reported till date that can predict surface over a wide range of operating conditions.the models reported till date are not fully feasible and the experimental investigations are very exhaustive but with limited applicability. Therefore, in this work, an attempt is made to develop an analytical model for the prediction of surface over a wide range of operating conditions, achieved in the grinding of silicon carbide ceramic material with diamond abrasive wheel.the developed model is experimentally validated and finally surface is optimized by considering machining parameters like table feed rate, depth of cut and grinding wheel speed. reach A.The cutting trajectory F B C A is a cycloid curve formed by the grinding wheel velocity and the workpiece velocity. The displacement AA is the distance moved on two neighbouring cutting edges, and it can be given as (2.1) Where s is the moving distance of the grinding wheel on two neighbouring cutting edges in mm, v s is the grinding wheel tangential speed in m/s,v w is the workpiece velocity in mm/min, and L is the space of two neighbouring working cutting edges in mm. 2Analytical model for surface - (2.2) Consider a grinding wheel of diameter D, rotating with speed N rpm, having C grits per unit area of the wheel surface. The interaction between an active grit on the wheel and the work piece along with the shape of a typical chip formed is as shown in Figure 2.1(a)&(b). (2.3) Becauseγ+ѳ=π/2, cosѳ=1-2a p /d s,when simplified gives, becausea p /d s << 1 Figure 2.1 (a) Grinding wheel interacting with workpiece, (b) A typical chip which when put into equation (1) gives (2.4) Whereh m is the uncut chip thickness in mm, a p is the equivalent cutting depth in mm and d s is the wheel diameter in mm. Figure 2.2 The interface trajectory of grinding wheel-workpiece A sketch ofthe interface trajectory of grinding wheel and workpiece is shown in Figure 2.2 The cutting edge comes into contact with the workpiece at F to The grinding wheel topology was too irregular to be characterized by random cutting edge s spacing and protrusion height in the grindingg process. Snoeys et al. [4] and Kedrov [5] proposed the empirical model of surface using grinding experiments and it is given as (2.5) 311-2

3 Where,R 1 was determined by a series of experimental data. Snoeys et al. showed that surface was related through empirical constants R 1 and x to (v w a p /v s ) for a given dressing condition. The coefficients R 1 and x of the Snoeys model were determined by least square method from a series of grinding experiments.however, the model had certain limitations in predicting surface because the constants depend on the particular wheel, workpiece, grinding fluid, and dressing conditions, as well as on the accumulated stock removal.the Snoeys model not only correlated fairly well with the surface and wheel wear but also with other performance characteristics, including the grinding forces and energy. Therefore, the modified model was proposed based on the Snoeys model in this work by taking equivalent cutting depth as uncut chip thickness.the determination of coefficientsr 1 and x of theproposed analytical model is based on the Snoeysmodel where, the least squares method is used to determine the parameters R 1 and x byconducting a series of suitablegrinding experiments. Now that is, (2.6) (2.7) From equation (2.7), it is clear that the surface in the grinding of SiC advanced ceramics is a function of different parameters like a p (depth of cut),v w (feed rate), v s (wheel speed),and d s (diameter of wheel). 3 Optimization of surface 3.1 Experimental methodology In this work, Response Surface Methodologyfull factorial design is applied on the most effective process parameters i.e. table feed, wheel speed and depth of cut while grinding SiC ceramic work piece with metallic bonded diamond grinding wheel. 27 experiments are carried out on 320 grit size SiC ceramic workpiece on a modified tool and cutter grinder (HMT GTC-28) and the corresponding values were measured by Surftestprofilometer (Mitutoyo SJ-301) The machining parameters and levels are given in Table 3.1The experimental results of surface finish of SiCadvanced ceramic material on grinding with diamond grinding wheel are shown in Table 3.2 Symbols Run A B C Table 3.1 Grinding variables used for optimization of surface Machining parameters Table feed (m/min) Depth of cut (mm) Wheel speed (rpm) Table 3.2 Experimental data A:Feed (m/min) B:doc (mm) Levels C:Speed (rpm) Ra

4 MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN SURFACE GRINDING OFSiC ADVANCED CERAMIC MATERIAL 3.2 Response surface methodology (RSM) Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques whichare useful for developing, improving and optimizing processes.the application of RSM to design optimization is aimed at reducing the cost of expensive analysis methods and their associated numerical noise. It also has important applications in the design, development and formulation of new products, as well as in the improvement of existing product designs. In this work, RSM is applied to determine the optimum machining parameters leading to minimum surface.experimental results were used for modeling using RSM. The experimental data was used to build first order and second order mathematical models by using regression analysis method. These developed mathematical models were optimized by using the DOE optimization procedure for the output responses by imposing lower and upper limit for the input machining parameters table feed, depth of cut and wheel speed. 4 Results and discussion 4.1 Introduction Experiments are conducted on modified tool and cutter grinder and the surface values are measured using the Surftestprofilometer.Separate experiments are conducted for optimization and experimental validation of the model.the analysis and optimization of surface are done using Design Expert software. 4.2 Experimental validation of the model To study the variation of surface with various grinding parameters like work piece velocity, equivalent cutting depth and wheel surface speed, some of the variables in the equation are assumed suitable values. SiC ceramic work piece with 320grit size is used. For an 80/100 diamond grinding wheel, the average distance between two random continuous cutting grain, L is taken as mm and wheel diameter,d s is150mm.the coefficients R 1 and x of the empiricalmodel were determined by conductinga series of suitable grinding experiments as 0.25 and respectively For the validation of the proposed model the values of the parameters considered are v w =7.5 m/min, N= 2000 rpm (v s = m/s) and d= 0.10 mm for 320 grit size SiC workpiece. A total of 18 experiments are performed by varying each parameter for 6 levels, keeping the other two parameters constant, and the corresponding surface values are recorded using a Surftestprofilometer.For the same values of the process parameters the theoretical surface values are calculated from the model equation (3.8). The experimental and theoretical surface values are compared in Figures 4.1, 4.2 and Effect of process parameters on surface Design expert 7.0 software has been used to compute the values as shown in Table 3.2 Based on ANOVA Table 4.1 summarizes the effects of process variables and the interactions for second order quadraticmodel for Ra. This model was developed for 95% confidence level. Table 4.1 ANOVA for Response surface quadratic model Ra Source Sum of Mean F P value dof squares square value Prob>F Model < A:Feed < B:doc < C:Sped B 2 4.0E E- 003 Table 4.2 R-squared values for Ra R- squared Adj R-squared Pred R-squared Adeq Precision The model F value of 57.25implies that the model is significant, with a negligible influence of noise. By checking F values andp values, it is seen that the factor A (Table feed) has the mostsignificant effect on R a. The valueof Prob>F less than 0.05 indicates that the model terms A, B, C and B 2 are significant. Values greater than indicate the model termsare not significant. The Pred.R-squared value of is in reasonable agreement with the Adj.Rsquared value of The developed statistical model for R a in coded form: Surface Roughness= feed doc e-005 speed doc

5 Surface,Ra Variation of surface with table feed Table feed (m/min) Experimental Ra Theoretical Ra Figure 4.1 Variation of surface with table feed at B=0.10 mm and C=2000 rpm Figure 4.4 (a) Variation of feed Vs surface Surface,Ra Variation of surface with depth of cut Depth of cut (mm) Experimental Ra Theoretical Ra Figure 4.4(b) Variation of doc Vs surface Figure 4.2 Variation of surface with doc at A=7.5 m/min and C=2000 rpm Surface roughnes,ra Variation of surface with wheel speed Wheel speed (rpm) Experimental Ra Theoretical Ra Figure 4.3 Variation of surface with wheel speed at A=7.5m/min and B=0.10mm Figure 4.4(c) Variation of speed Vs surface The significance of the grinding variables on surface is evaluated by conducting experiments and the results are represented in Figures 4.4(a), 4.4(b) and 4.4(c). It is observed from the Figures4.4(a) and 4.4(b) that the surface increases with an increase in feed and depth of cut. When the feed and depth of cut are increased, the increase in material removal rate and the increase in 311-5

6 MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN SURFACE GRINDING OFSiC ADVANCED CERAMIC MATERIAL chip thickness account for the increase of surface. It is also observed from the results shown in Figure 4.4(c)that surface decreases with an increase in wheel speed. As the grinding wheel speed increases, the heat generated in the deformation zone increases and thereby softening the area thus reducing surface. 4.4 Optimization of surface Table 4.3 shows the optimized results for surface in grinding for 320 grit size of SiC ceramic work pieces and the results of the confirmation experiments conducted with the optimum conditions. Table 4.3 Design-expert optimization results A (m/min) B (mm) C (rpm) Exp. Ra Mod. Ra % error The optimal parametric combination becomes low depth of cut, high wheel speed and low table feed for minimum surface values. A confirmatory experiment has been carried out to verify the optimal setting. The confirmatory experiment shows a References Agarwal, S., and Rao, P.V., (2004),A new chip thickness model for performance assessment of silicon carbide grinding,internationaljournal of Advanced.ManufuringTechnology,Vol. 24,pp Agarwal, S., and Rao, P.V.(2005),A probabilistic approach to predict surface in ceramic grinding, International Journal of Machine Tools and Manufacture,Vol. 45, pp Agarwal, S., and Rao, P.V.,(2010),Modeling and prediction of surface in ceramic grinding, International Journal of Machine Tools and Manufacture,Vol.50, pp Agarwal, S., and Rao, P.V.(2012),Predictive modeling of undeformed chip thickness in ceramic grinding.,international Journal of Machine Tools and Manufacture,Vol.56,pp Alao,A.R.,andKonneh,M.(2011),Application of Tanguchi and Box-Behnken designs for surface in precision grinding of silicon, International Journal of Precision Technology,Vol.2, pp Gopal,A.V. andrao,p.v.,(2003),the optimization of the grinding of silicon carbide with diamond wheels variation of 3.17% between experimental and ANOVA model value. 5 Conclusions An analytical model has been developed to predict the surface during the surface grinding of SiC advanced ceramic materials using a diamond grinding wheel.the investigations of the study conducted indicates that the grinding variables: table feed, depth of cut and wheel speed are the primary influencing factors which affect the surface developed during the process. Based on the experimental results and discussions, the following conclusions were drawn: The proposed analyticalmodel predicts the surface with an average percentage error of 6.88 %. The ANOVA equation predicts the surface with an average percentage error of 1.28% Smaller values of table feed (about 3.12 m/min) and depth of cut (about 0.08 mm) and larger value of wheel speed (about 2735 rpm) are found to be giving minimum surface values (about µm) during the process. using genetic algorithms,international Journal of Advanced.ManufuringTechnology,Vol.22: pp Jiang,J.,Ge,P.,and Hong,J.(2013),Study on microinteracting mechanism modeling in grinding process and ground surface prediction,international Journal of Advanced.ManufuringTechnology, Vol.67:pp Kedrov,S.M., (1980),Investigation of surface finish in cylindrical grinding operation, International Journal of Machine Tools and Manufacture,Vol. 51(1), pp Lian,M.,Gong,Y.,andYang,X.(2014),Surface model in experiment of grinding engineering ceramics, Proceedings of Institution of Mechanical Engineers and Journal of Engineering Manufacture.pp1-7 Simariya, B., George, P.M.,andChauhan,V.D., (2013),Effect of grinding variables on surface finish of Ceramics, International Journal of Engineering Research and Technology,Vol.26,pp Snoeys, R., Peters, J., and Decneut, A.,(1974),The significance of chip thickness in grinding,cirp 311-6

7 Annals Manufuring Technology,Vol.23(2), pp Zhou, X., and Xi, F.,(2002),Modeling and prediction of surface in ceramic grinding,international Journal of Machine Tools and Manufacture,Vol.42, pp