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- International Journal of Advanced Research in and Technology (IJARET) International Journal of Advanced Research in Engineering Engineeringand Technology (IJARET), ISSN 0976 – 6480(Print) IJARET ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEISSN 0976 – 6499(Online) Volume 2Number 1, Jan - Feb (2011), pp. 12-28 © IAEME© IAEME, http://www.iaeme.com/ijaret.html OPTIMIZATION OF SURFACE FINISH DURING MILLING OF HARDENED AISI4340 STEEL WITH MINIMAL PULSED JET OF FLUID APPLICATION USING RESPONSE SURFACE METHODOLOGY K. Leo Dev Wins School of Mechanical Sciences Karunya University, Coimbatore Tamilnadu, E-Mail: leodevwins@gmail.com A. S. Varadarajan School of Mechanical Sciences Karunya University, Coimbatore TamilnaduABSTRACT Machining with minimal fluid application is involves the use of extremely smallquantities of cutting fluid so that for all practical purposes it resembles dry machining.This technique is free from problems associated with procurement, storage and disposalof cutting fluid and helps in promoting an eco friendly atmosphere on the shop floor.Apart from machining parameters, the fluid application parameters such as pressure ofthe fluid injector, frequency of pulsing and the rate of fluid application also influence thecutting performance during minimal fluid application. Good surface finish is a functionalrequirement for many engineering components and in the present investigation an attemptis made to optimize surface finish during milling of hardened AISI4340 steel withminimal fluid application using response surface methodology. The surface finishpredicted by the model matched well with the experimental results.Key words: Central composite; Environment friendly; Mathematical models; Minimalcutting fluid application; Pulsed jet; Rotatable design.1. INTRODUCTION Conventional surface milling of hardened steel involves application of largequantities of cutting fluid. Procurement, storage and disposal of cutting fluid incurexpenses and large scale use of cutting fluid causes serious environmental and health 12
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEhazards on the shop floor. It also leads to problems in disposal of cutting fluid which hasto comply with environmental legislation as well. According to the Occupational Safetyand Health Administration (OSHA) regulations, the permissible exposure Level for mistwithin the plant (PEL) is 5 mg/m³and is likely to be reduced to 0.5 mg/m³ [1]. In thiscontext, pure dry milling is a logical alternative which is totally free from the problemsassociated with storage and disposal of cutting fluid. But it is difficult to implement onthe existing shop floor as it requires ultra hard cutting tools and extremely rigid machinetools [2]. Ultra hard cutting tools may be introduced but the existing machine tools maynot be rigid enough to accept them. In this context the best alternative is to introducepseudo dry milling or milling with minimal fluid application [3 - 6]. By introducing thecutting fluid precisely at the cutting zone, better cutting performance can be achievedwhich will result in better surface finish, reduction of tool wear and cutting force [7–9].In minimal fluid application, extremely small quantities of cutting fluid is introduced ashigh velocity (70 m/s) tiny droplets at critical zones so that for all practical purposes itresembles dry milling [10]. It is reported that minimal cutting fluid application can bring forth better cuttingperformance during turning and in the case of minimal application, heat produced duringmachining is transferred predominantly in the evaporative mode, which is more efficientthan the convective heat transfer prevalent in conventional wet turning [3, 10]. Very lesswork is reported in the area of fluid minimization during milling [11, 12]. Research workcarried out in our laboratory indicated that good cutting performance could be achievedin terms of surface finish, tool wear and cutting force when a specially formulated cuttingfluid was applied on critical locations in the form of high velocity narrow pulsed jetduring surface milling of AISI4340 steel with a hardness of 45 HRC by a fluidapplication system that can deliver cutting fluid through fluid application nozzles andoffer better rake face lubrication. The scheme is environment friendly and can be easilyimplemented on the shop floor. Surface roughness (Ra) is widely used as an index to determine the surface finishin the machining process. Surface roughness has become one of the important outputparameters for many years and one of the important design features in many situationssuch as parts subject to fatigue loads, precision fits, fastener holes and aesthetic 13
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMErequirements. In addition to the tolerances, surface roughness imposes one of the mostcritical constraints for selection of machines and cutting parameters in process planning[13]. For achieving the desired surface finish, it is necessary to understand themechanisms of the material removal and the kinetics of machining processes affecting theperformance of the cutting tool [14]. Earlier work in this research showed the fluidapplication parameters such as pressure at the fluid injector, frequency of pulsing and therate of fluid application affects the surface roughness to a larger extent [11]. Thetraditional ‘one-factor at a time’ technique used for optimizing a multivariable system isnot only time consuming but often misses easily the alternative effects between thecomponents. Also, this method requires carrying out a number of experiments todetermine the optimum levels, which are false at most of the times. These drawbacks ofsingle factor optimization process can be eliminated by optimizing all the affectingparameters collectively by central composite design (CCD) using Response SurfaceMethodology (RSM). For prediction, the response surface Method is practical,economical and relatively easy to use when compared to other types of optimizationtechniques [15]. In the present work, a mathematical model has been developed topredict the surface roughness of machined work piece using response surface method.Analysis of variance (ANOVA) is used to check the validity of the model developed.1.1 Selection of work material Through hardenable AISI4340 steel was selected as work material. It washardened to 45 HRC by heat treatment. It is a general purpose steel having wide rangeof applications in automobile and allied industries by virtue of its good hardenability.Plates of 125 mm length, 75 mm breadth and 20 mm thickness were used for the presentinvestigation. The composition of the work material is shown in Table 1. Table 1 Chemical composition of work material Element % C 0.38 – 0.43 Cr 0.7 – 0.9 Mn 0.6 – 0.8 Mo 0.2 – 0.3 Ni 1.65 – 2.0 P 0.035 max Si 0.15 – 0.3 S 0.04 max Fe Balance 14
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME1.2 Selection of cutting tool Carbide inserts with the specification AXMT 0903 PER-EML TT8020 ofTaeguTec was used in the investigation along with a tool holder with the specificationTE90AX 220-09-L.1.3 Formulation of cutting fluid Since the quantity of cutting fluid used is extremely small, a specially formulatedcutting fluid was employed in this investigation. The base was a commercially availablemineral oil and the formulation contained other ingredients [16]. It acted as an oil inwater emulsion.1.4 Fluid application system Figure 1 Schematic view of the minimal fluid applicator A special test rig was developed for this purpose [3]. It consists of a P-4 fuelpump (Bosch make) coupled to an infinitely variable electric drive. An injector nozzle ofsingle hole type with a specification DN0SD151 with a spray angle of 0º was used in theinvestigation. The test rig facilitated independent variation of pressure at fluid injector(P), frequency of pulsing (F) and the rate of fluid application (Q). The system can delivercutting fluid through four outlets simultaneously so that cutting fluid could be applied tomore than one location or more than one machine tool at the same time. By selectingproper settings the rate of fluid application could be made as small as 0.25ml/min. Thefrequency of pulsing is determined by the speed of rotation of the DC variable speedmotor that drives the fluid pump. The fluid applicator delivers cutting fluid at a rate of one pulse per revolution.This facility enables application of less amount of cutting fluid per pulse. For example, ifQ is the rate of fluid application in ml/min and F is the frequency of pulsing inpulses/min, fluid applied per pulse is given by Q/F. Pulsing jet aids in fluid minimization 15
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEwithout compromising the velocity of individual particles as the pressure at the fluidinjector remains constant. By increasing the frequency, the rate of fluid delivered perpulse can be controlled. For example if Q is 1 ml/min and F is 1000 pulses/min and thepressure at the fluid nozzle is set at 100 bar, then fluid delivered per pulse is equal to1/1000 = 0.001 ml while the velocity of the individual fluid particles will beapproximately equal to about 70 m/sec [10]. A schematic view of the fluid applicator isshown in Figure 1. Special fixtures were designed as in Figure 2 so that the injector nozzle could belocated in any desired position without interfering the tool or work during actual cutting. Figure 2 Fixtures for locating the fluid injector2. SCHEME OF INVESTIGATION The experiments were designed based on five-level factorial central compositerotatable design with full replications. The design matrix is shown in Table 3.Experiments were carried out on an HMT (model: FN1U) milling machine. Surfacefinish was measured using a stylus type Perthometer (Mahr make). The cutting speed,feed and depth of cut were set in the semi finish milling range for the tool-workcombinations. The cutting parameters such as cutting speed, feed rate and depth of cutwere kept constant at 45 m/min, 0.14 mm/tooth and 0.4 mm respectively [17]. In order to achieve the desired objective, the investigations were planned in thefollowing sequence: 1. Identifying the predominant factors which are having influence on surface roughness and finding the upper and lower limits of the chosen factors. 2. Developing the experimental design matrix. 3. Conducting the experiments as per the design matrix and recording the responses. 4. Developing the mathematical model, calculating the coefficients of the model and testing the significance of the coefficients. 16
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME 5. Checking the adequacy of the developed model by ANOVA method and 6. Validating the mathematical model by experimentation.2.1 Identifying the predominant factors which are having influence on surface roughness and finding the upper and lower limits of the chosen factors Surface roughness of the work piece is an important attribute of quality in anymachining operation. During machining many factors affects the surface finish. Basedon the previous research work [11], it was found that in addition to the machiningparameters such as cutting speed, feed rate and depth of cut the fluid applicationparameters also influence the quality of the surface generated. Apart from machiningparameters, the independently controllable predominant fluid application parameters thatinfluence the surface finish of the work piece were identified as: 1. pressure at the fluid injector (P) 2. Frequency of pulsing (F) 3. Quantity of application of cutting fluid. Preliminary experiments were carried out to fix the lower and upper limits ofthese factors. Accordingly, pressure at the fluid injector was fixed between 50 and 100bar. In line with this factor, the frequency of pulsing was fixed between 250 and 750pulses /min and the rate of application of cutting fluid was fixed between 2 and 10ml/min. The upper limit of the factor was coded as +1.682 and the lower limit as -1.682.The coded values for intermediate values were calculated from the followingrelationship: Where is the required coded value of a variable X; and X is any value of thevariable from to . The selected process parameters with their limits, units andnotations are given in Table 2. Table 2 Process control parameters and their limits LimitsProcess parameters Units Notations -1.682 -1 0 1 1.682Pressure at fluid injector bar P 50 60 75 90 100Frequency of pulsing Pulses/min F 250 350 500 650 750Quantity of cutting fluid ml/min Q 2 3.5 6 8.5 10application 17
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME2.2 Developing the experimental design matrix A five level, three-factors, central composite rotatable factorial design [18],consisting of 20 sets of coded conditions is shown in Table. 3. The design matrixcomprises a full factorial design 2³ [=8] plus six star points and six center points. Allfluid application parameters at the intermediate level (0) constitute center points andcombinations at either its lowest (-1.682) or highest (+1.682) level with the other twoparameters at the intermediate level constituting the star points. Thus the 20experimental runs allowed the estimation of the linear, quadratic and two-way interactiveeffects of the process parameters on the surface roughness. Table 3 Design matrix and observed values of surface roughness Design Matrix Ra in microns S. No P F Q 1 -1 -1 -1 0.675 2 1 -1 -1 0.591 3 -1 1 -1 0.880 4 1 1 -1 0.627 5 -1 -1 1 0.817 6 1 -1 1 0.514 7 -1 1 1 0.870 8 1 1 1 0.564 9 -1.682 0 0 0.840 10 1.682 0 0 0.400 11 0 -1.682 0 0.615 12 0 1.682 0 0.711 13 0 0 -1.682 0.851 14 0 0 1.682 0.820 15 0 0 0 0.527 16 0 0 0 0.545 17 0 0 0 0.516 18 0 0 0 0.521 19 0 0 0 0.532 20 0 0 0 0.5362.3 Conducting the experiments as per the design matrix and recording the responses The experiments were conducted as per the design matrix at random, to avoid thepossibility of systematic errors. The average roughness (Ra) is mostly used in industries,is taken as the response for this study. The surface roughness was measured using a stylustype Perthometer (Mahr make). Table 3 presents a record of the surface finish for eachexperiment. In this table, for experimental runs 15 to 20, even though the experimentalsetup and all machining conditions remain the same, the responses varied slightly. This is 18
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEdue to the effect of unknown and unpredictable variables called noise factors, which creptinto the experiments. To account for the impact of these unknown factors of the response,replicated runs were included in the design matrix.2.4 Developing the mathematical model, Calculating the coefficients and testing the coefficients The response function representing surface roughness can be expressed as Ra = f(P, F, Q) and the relationship selected being a second-order response surface. Thefunction is as follows [19] Ra = b₀ + b₁ P + b₂ F + b₃ Q + b₁₁ P² + b₂₂ F² + b₃₃ Q² + b₁₂ PF + b₁₃ PQ + b₂₃ FQWhere coefficients b1, b2 and b3 are linear terms, coefficients b₁₁, b₂₂ and b₃₃ are second-order terms, and coefficients b₁₂, b₁₃ and b₂₃ are interaction terms. MINITAB software(version 13.1) software package was used to calculate these coefficients and the resultsobtained are shown in Table 4. Table 4 Estimated values of regression coefficients S. Term Regression Standard p- No coefficient error value 1 Constant 0.530 0.011 0.000 2 P -0.123 0.007 0.000 3 F 0.037 0.007 0.001 4 Q -0.004 0.007 0.046 5 P*P 0.027 0.007 0.004 6 F*F 0.042 0.007 0.000 7 Q*Q 0.103 0.007 0.000 8 F*P -0.022 0.010 0.049 9 Q*P -0.034 0.010 0.005 10 Q*F -0.017 0.010 0.103 The regression model developed for predicting surface finish (Ra) is shown by thefollowing equation (1).Surface finish Ra = 0.530 - 0.123 P + 0.037 F - 0.004 Q + 0.027 P² + 0.042F² + 0.103 Q²- 0.022 PF - 0.034 PQ - 0.017 FQ. ……………….(1)2.5 Checking the adequacy of the developed model by ANOVA technique. The model was examined for lack of fit, adequacy and efficiency. Table 5presents the ANOVA summary of the model developed. The model is highly significantas indicated by the p-value (p<0.001). The goodness of the fit of the model was checkedby the coefficient of determination (R²). The value of R² is always between 0 and 1. The 19
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEcloser the R² value to 1, the stronger will be the model and better will be its predictions[19, 20]. In this case, the value of the coefficient of determination (R² = 0.982) indicatesthat 98.2% of the variability in the response could be explained by the model. In additionto this, the value of the adjusted coefficient of determination (Adj. R² = 0.967) is alsovery high to advocate for a higher significance of the model. Table 5 ANOVA summary for the model Source of Sum of Degrees of Mean F-ratio p- R² variation squares freedom squares value Regression 0.413 9 0.046 62.198 0.000 0.982 Residual 0.007 10 0.001 A p-value less than 0.05 indicated the significant model terms. The regressionanalysis of the experimental design presented in Table. 5 demonstrated that the linearmodel terms (P, F and Q), quadratic model terms (P², F², and Q²) and interactive modelterms (F*P, Q*P) are significant (p<0.05) and the interactive model term Q*F isinsignificant (p>0.05). After dropping out the non-significant terms from Table. 4, themodel can be expresses by the equation (2):Surface roughness Ra = 0.530 - 0.123 P + 0.037 F - 0.004 Q + 0.027 P² + 0.042F² + 0.103Q² - 0.022 PF - 0.034 PQ. …………. (2) Studentized residuals were calculated to check the adequacy of the model.Residual represents the difference between the observed value of a response and thevalue that is fitted under the hypothesized model. Any observation with a studentizedresidual value greater than 3 was considered as outlier. It is found that exceptexperimental run no. 1 with a studentized residual of -6.819, other values were wellwithin the acceptable range.2.6 Validating the mathematical model Validity of the developed models was tested by drawing scatter diagrams thatshows the observed and predicted values of surface roughness. Fig. 3 shows therepresentative scatter diagram. Test runs were conducted to determine the accuracy of themodel conformity. A comparison was made between predicted and actual values. Theresults obtained show that the model is sufficiently accurate as indicated by the R² valuewhich is as high as 0.976. 20
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME Figure 3 Scatter diagram for surface roughness (Ra)3. RESULTS AND DISCUSSIONS The mathematical model as in equation (2) can be used to predict the surfaceroughness (Ra) by substituting the values of the respective process parameters. Thesurface roughness calculated from the final model for each set of coded values of fluidapplication parameters are represented graphically in Figure 4, Figure 5, Figure 6 andFigure 7. These plots show the convincing trends between cause and effect. The directand interaction effects are discussed below.3.1 Direct Effect of pressure at the fluid injector on Surface roughness Figure 4 represents the direct effect of pressure at the fluid injector (P) on surfaceroughness (Ra). From the Figure, it is clear that surface finish increases with increase inpressure at the fluid injector. Figure 4 Direct effect of pressure at the fluid injector on surface roughness The pressure at the fluid injector should be kept at high level (100 bar)corresponding to an exit velocity of 50 m/s to achieve better surface finish. The exitvelocity of the fluid particles from the nozzle is directly proportional to the pressure atthe fluid injector whereas the size of fluid particle is inversely proportional to the 21
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEpressure [21]. The cutting force is directly related to the chip friction on the rake face.Any attempt to reduce friction on the rake face can bring forth lower cutting force, lowerenergy consumption and better surface finish. Hence when the pressure at the injector ishigh, the fluid particles will have higher velocity and smaller size which help them topenetrate into the tool-chip interface [3] leading to better lubrication at the contactsurfaces and hence better surface finish.3.2 Direct Effect of frequency of pulsing on Surface roughness Figure 5 represents the direct effect of frequency of pulsing (F) on surfaceroughness (Ra). From the Figure 5, it is clear that surface roughness first decreases withincrease in frequency of pulsing and then it increases. Figure 5 Direct effect of frequency of pulsing on surface roughness It is observed that frequency of fluid application in the range of 400 to 500pulses/min favored better surface finish. It is reported that the frictional forces betweentwo sliding surfaces can be reduced considerably by rapidly fluctuating the width of thelubricant filled gap separating them [22]. When a pulsing jet is used, the width of the lubricant filled gap between the toolrake face and the chip fluctuates with a frequency equal to the frequency of pulsing of thefluid jet. The width will be maximum when the fluid slug falls at the gap and will beminimum when no particles fall on the gap during the pulsing cycle. This processcontinues as the fluid particles fall in the gap between the chip and the tool intermittently.When the frequency of pulsing is 750 pulses/min, the quantity of fluid delivered per pulsewill be very less when compared to 500 pulses/min for any fixed rate of fluid application.Hence the fluctuation in the width of the liquid film between the tool and the chip is lessappreciable. A minimum quantity of cutting fluid should be delivered per pulse to get 22
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEappreciable fluctuation in the width. This leads to presence of fresh fluid droplets in tothe tool chip interface unlike in the case where a stagnant layer of cutting fluid present ifa continuous jet was employed [23]. The presence of fresh fluid droplets facilitates betterfilling of the gap on the tool chip interface thereby providing better lubrication andenhanced cooling as the droplets evaporate. When the frequency of pulsing is very high, the individual particles will be smalland may lack in kinetic energy to penetrate in to the tool chip interface. This leads to lessfluid particles reaching the rake face and hence less efficient rake face lubrication. It isalso to be noted that the pulsing nature of the fluid delivery vanishes when the frequencyof pulsing is very high and the fluid delivery tends to resemble a continuous jet, devoid ofall the aforesaid advantages claimed for a pulsing jet.3.3 Direct Effect of quantity of cutting fluid Figure 6 represents the direct effect of Quantity of cutting fluid (Q) on surfaceroughness (Ra). From the Figure, it is clear that surface roughness decreases withincrease in the quantity of cutting fluid and then increases. Figure 5 Direct effect of frequency of pulsing on surface roughness It is observed that the quantity of cutting fluid about 6 ml/min favored bettersurface finish. According to the empirical relationship developed by Hiroyasu andKadota [21], the mean diameter D for a droplet size cutting fluid injection is given by D = K(∆P)-0.135 ρ0.121V0.131 Where ∆P is the mean pressure drop, ρ is the density of the medium in whichinjection of fluid takes place, V is the quantity of fluid delivered per pulse and K is a 23
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEconstant. With lower delivery rates, droplet size decreases. When the size of the dropletsis small, they can easily enter into the tool-chip interface and provide better rake facelubrication but when the size is too small, the kinetic energy of the fluid particles will bevery less and the particles need a minimum kinetic energy to reach the tool-chipinterface. It appears that a rate of fluid application of 6 ml/min favors the bestpenetration from the point of view of the individual size of the fluid droplets and thekinetic energy. If the rate of fluid application is greater than 6 ml/min, the fluid particleswill have higher kinetic energy but their sizes may not be favorable for their easypenetration into the tool-chip interface. When the rate of fluid application is much lessthan 6 ml/min, the size of individual particles may favor their passage into the tool-chipinterface but they may not have sufficient kinetic energy owing to their smaller size.When the rate of flow is about 6 ml/min, it appears that both the size and the kineticenergy favors easy penetration of fluid particles into the tool-chip interface therebyproviding better rake face lubrication and hence better surface finish. It is also to benoted that the pulsing nature of the fluid delivery is affected when the quantity of cuttingfluid increases.3.4 Interaction Effect of pressure at the fluid injector and Frequency ofpulsing on Surface roughness Figure 6 Interaction effect of pressure at the fluid injector and frequency of pulsing on surface roughness The response surface plot shown in Figure 6 shows the interaction effect ofpressure at the fluid injector and frequency of pulsing on surface roughness while thequantity of cutting fluid was maintained at 6 ml/min. From the contour of the surface, itis noted that surface roughness (Ra) is maximum (1.0565 µm) when pressure at the fluid 24
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEinjector was at lower (-1.682) level and frequency of pulsing at higher level (+1.682), andthe surface roughness (Ra) was minimum (0.4 µm) when pressure at the fluid injectorwas at higher (+1.682) level and frequency of pulsing at intermediate level (0).3.5 Interaction Effect of pressure at the fluid injector and quantity of cuttingfluid on Surface roughness The response surface plot shown in Figure 7 shows the interaction effect ofpressure at the fluid injector and quantity of cutting fluid on Surface roughness while thefrequency of pulsing was maintained at 500 pulses/min. Figure 7 Interaction effect of pressure at the fluid injector and quantity of cutting fluid on surface roughness From the contour surface, it is noted that surface roughness (Ra) is maximum(1.194 µm) when pressure at the fluid injector was at lower (-1.682) level and quantity ofcutting fluid at higher level (+1.682), and the surface roughness (Ra) was minimum(0.394 µm) when pressure at the fluid injector was at higher (+1.682) level and frequencyof pulsing at intermediate level (0) for a constant value of frequency of pulsing at 500pulses/min. The optimal conditions obtained form the analysis in coded form are P = 1.682, F= 0.0003 and Q = 0.297. The real values are pressure at the fluid injector at 100 bar,Frequency of pulsing at 500 pulses/min and the quantity of cutting fluid at 5.2936ml/min. The minimum surface roughness (Ra) that can be achieved when the pressure atthe injector is kept at 100 bar, frequency of pulsing at 500 pulses/min and the rate of fluidapplication at 6.706 ml/min is 0.3904 µm. Cutting experiments were conducted tovalidate the prediction and from Table 6 it is evident that the value of surface finish aspredicted by the model matched well with the experimental result. Table 6 presents the 25
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEMEcomparison of the optimum surface finish as predicted by the model with theexperimental results. Table 6 Comparison of the predicted surface finish with experimental value Frequency of Quantity of Ra in micronsSl Pressure at the % pulsing cutting fluid Observed Predicted errorNo. injector (bar) (Pulses/min) (ml/min)1. 100 500 6.706 0.4100 0.3904 4.74 CONCLUSIONS Mathematical model for surface roughness has been developed to correlate theimportant fluid application parameters in machining of hardened AISI4340 steel. Theexperimental plan used is of rotatable central composite design. The three important inputvariables considered for the present research study is pressure at the fluid injector,frequency of pulsing and quantity of cutting fluid application. The influences of the fluidapplication parameters on surface roughness have been analyzed based on themathematical model developed. The study leads to the following conclusions. 1. The surface roughness decreases with the increase of pressure at the fluid injector. 2. The surface roughness decreases with increase in frequency of pulsing up to certain level (about 500 pulses/min) and then increases with the increase of frequency of pulsing. 3. The surface roughness decreases with increase in the quantity of cutting fluid up to certain level (about 6.7 ml/min) and then increases with the increase in quantity of cutting fluid. 4. It was found that the predictions of the RSM matched well with the experimental results.Acknowledgement The authors thank the authorities of the Centre for Research in Design andManufacturing of the Karunya University for facilitating this project and M/s TaugetecIndia (P) Ltd. for supplying cutting tools at concessional rates.References1. R.S. Marano, J.M. Smolinnski, C.W.M. Esingulari, Polymer additives as mist suppressants in metal cutting fluids, J. Soc. Tribol. Lubr. Eng. (1997) 25–32.2. F. Klocke, G. Eisenblatter, Dry cutting, Annals of the CIRP, 46 (2) 1997, 519 – 526. 26
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME3. A. S Varadarajan, P.K. Philip, B. Ramamoorthy, Investigations on hard turning with minimal cutting fluid application(HTMF) and its comparison with dry and wet turning, International Journal of Machine tool and Manufacture (2001) 193 – 200.4. A.S Varadarajan, P.K. Philip, B. Ramamoorthy, Neural Network Assisted Performance Prediction in Hard Turning with Minimal Quantities of Cooling Lubricants, Proceedings of the 14th International Conference, CAD/CAM, Robotics and Factories of the Future PSG College of Technology, Coimbatore, India, pp. (1998) 654-658.5. Varadarajan, A.S., P.K. Philip, B. Ramamoorthy, Investigations on Hard Turning with Minimal Pulsed jet of Cutting Fluid, Proceedings of the International seminar on Manufacturing Technology beyond 2000, Bangalore, India (1999) pp173-179.6. A. Attansio, M. Gelfi, C. Giardimi, C. Remino, Minimal Quantity Lubrication in Turning: Effect on tool wear, Int. Journal of wear, 260(2006) 333-338.7. R.Wertheim, A. Ber, Rotberg, Influence of high pressure flushing through the rake face of the cutting tool, Ann. CIRP 41 (1992) 101–106.8. M.A. Chepe, P.K. Philip, Cutting fluid injection at tool chip interface to improve machining performance, J. Inst. Eng. (India) 75 (1994) 25–30.9. M. Mazurkiowicz, Z. Kubala, J. Chow, Metal machining with high pressure water jet cooling assistance new possibility, J. Eng. Ind. 111 (1989), 7–12.10. P.K. Philip, A. S Varadarajan, B. Ramamoorthy, Influence of cutting fluid composition and delivery variables on performance in hard turning using minimal fluid in pulsed jet form, Journal of the Institution of Engineers (India), vol 82, (2001)12–19.11. Anil Raj, K. Leo Dev Wins, Robinson Gnanadurai, A. S Varadarajan, Investigations on hard milling with minimal fluid application, International conference on frontiers in Design and Manufacturing, Karunya University, Coimbatore (2008) pp183-187.12. T. Thepsonthi, M. Hamdi, K. Mitsui, Investigation into minimal-cutting fluid application in high-speed milling of hardened steel using carbide mills, International Journal of Machine Tools and Manufacture, 49 (2009) 156 – 162. 27
- International Journal of Advanced Research in Engineering and Technology (IJARET)ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME13. Jack Feng CX, Wang X, Development of empirical models for surface roughness prediction in finish turning, International Journal of advanced Manufacturing technology, 2002;20:348-356.14. Sreejith PS, Krishnamoorthy R, Malhotra SK, Narayanasamy K, Evaluation of PCD tool performance during machining of carbon / phenolic ablative composites, Journal of Material Processing Technology, 2000; 104:53 – 58.15. Sahin Yusuf, Riza Motorcu A, Surface roughness prediction model in machining of carbon steel by PVD coated cutting tools, American Journal of Applied Science, 2004; 1(1): 12-17.16. Varadarajan, A.S, B. Ramamoorthy, P.K. Philip, Formulation of a Cutting fluid for Hard Turning with Minimal Fluid Application, 20 th AIMTDR conference at Birla institute of Technology Ranchi, India, (2002) pp89-95.17. K. Leo Dev Wins, A.S Varadarajan, Optimization of operating parameters during surface milling of hardened AISI4340 steel with minimal pulsed jet fluid application, Journal of Manufacturing Technology Today, Vol.8, Issue 12, Dec 2009, p3-10.18. Murugan, N., and Parmar, R. S., Effects of MIG process parameters on the surfacing of stainless steel, Journal of Materials Processing Technology, 1994, 41: 381–398.19. Cochran, W. G., and Cox, G. M, Experimental Designs, 2nd Edition, John Wiley & Sons, Inc, New York, 1957, 346 – 354.20. Box G E P, Hunter W G & Hunter J S, Statistics for experiments, John Wiley & Sons, Inc, New York, 1978.21. Hiroyasu, T Kadota, Fuel drop size distribution in diesel combustion chamber, SAE paper 740715, SAE Transactions, vol. 83 (1974) 715 – 721.22. Uzi Landman, "FRUSTRATED" lubricant molecules offer new strategy for reducing friction in mechanical devices, Georgia Tech – Research news, http://gtresearchnews.gatech.edu/newsrelease/FRICTION.html, July 1998.23. Alexender, R, A.S. Varadarajan , P.K. Philip, Hard Turning with Minimum Cutting Fluid –A Viable green Alternative on the shop floor, Proceedings of the 18th All India Manufacturing Technology Design and Research Conference, vol.1, Kharagpur, (1998) pp 183-187. 28

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