Barriers to Continuous Improvement

A Study On Machining Parameters Optimization Of Spheroidal Graphite Iron On A Vertical Machining Center

NIRAJAN PUDASAINI2VX12MPD15A Project on:PROF. R R MALAGIPROJECT GUIDEContentsAbstractVertical millingAbout SG ironInput parametersResponse variablesObjectiveLiterature reviewProblem statementAbout DOERSMBox- Behnken design

Design matrixSequence of operationManufacture of workpieceMachiningMeasurement of SRObservation tableResultsConclusionFuture works

AbstractThis report presents an approach to predicting the surface roughness and material removal rate in milling of spheroidal graphite iron using tungsten carbide insert tool and its optimization by coupling the prediction model with response surface methodology. In this work, experiments are carried out as per the Box-Behnken design and an L13 orthogonal array is used to study the influence of various combinations of process parameters on SR and MRR. ANOVA test is conducted to determine the significance of each process parameter. Two sets of L13 OA are used each for tool orientation of 45 and 90 degrees.This work may be useful in selecting optimum values of various process parameters that would maximize the MRR and minimize the SR in machining. Vertical Milling

Machining Centers classified as:Vertical Machining centersHorizontal Machining centersUniversal Machining centers

VMC has spindle on vertical axis relative to work table

Used for flat works that require tool access from topFor e.g.: Mould and die cavities, large aircraft components

Figure: A Vertical Milling MachineSpheroidal Graphite IronAlso called ductile ironCharacterized by graphite occurred in microscopic spheroidsVarious grades, differed due to matrix (microstructure of metal around the graphite)IndiaIS 1865SG370/17SG400/12SG500/7SG600/3SG700/2SG800/2ISOISO 1083400-15450-10500-7600-3700-2800-2900-2Cutting parametersResponse variablesSurface roughnessIt is a measure of the level of unevenness of the part's surface.Measurement procedureSurface inspection by comparison methodDirect instrument methodParametersRa = arithmetic mean of departures of profile from mean lineRq, Ry, Rz, Sm are other parameters

2. Material removal rateIt is the volume of material removed divided by the machining time.MRR can be expressed as the ratio of the difference between the weight of the work piece before and after machining to the machining time.MRR= (Wb-Wa)/tWhere Wb = Weight of work piece before machining. Wa = Weight of work piece after machining. t = Machining time

ObjectiveThe objective of this study is to find out the optimum levels for the process parameters so that the surface roughness value will be minimum and rate of material removal will be maximum in a vertical machining center and to check the optimality by developing empirical models. Literature reviewPratyusha J et al. made a study for finding out optimum parameters for milling process using Taguchi methods. L9 array was used, parameters studied were speed, feed and depth of cut. They found that Taguchi method provides a systematic and efficient methodology for searching optimal milling parameters.

R. Suresh et al. made an attempt to analyse the influence of cutting speed, feed rate, DOC and machining time on machinability characteristics like SR and tool wear using RSM. The found that combination of low feed rate, low depth of cut and low machining time with high cutting speed is beneficial for minimizing the machining force and surface roughness

Balinder singh et al. carried out experiments for optimization of input parameters in the CNC milling on EN 24 steel.Taguchi technique usedSR and MRR were response variables, speed, feed and DOC were control parametersL27 array was used generated from MINITAB V15Confirmation runs was used to verify the experimentOther research on VMC using Taguchi technique were done by Piyush Pandey et al., Avinash A. Thakre, Reddy Sreenivalsu and so on.Milon D. Selvam et al., R. Jalili Saffar et al. used GA method.

Ahmad Hamdan et al. carried out experiments for high speed machining of stainless steel using L9 array and Taguchi method. Results showed a reduction of 25.5% in cutting forces and 41.3% in SR improvement.

Norfadzlan Yusup et al. made a comparison of five year researches from 2007 to 2007 that used evolutionary techniques to optimize machining process parameters. They found that SR is mostly studied with GA.

A Kacal and M Gulesin studied optimal cutting condition in finish turning of of ductile iron using Taguchi method. ANOVA was used to identify significant factors affecting SR. They found that feed rate is most significant.

Problem statementIn machining operation, the quality of surface finish and the rate of material removal are important requirements.

The choice of optimized cutting parameters is very important for controlling the required surface quality and obtaining the maximum MRR.

In this study, the optimum machining parameters, for vertical milling of SG iron, are to be determined to increase MRR and reduce the SR.

DOE techniqueStatistical design of experiments refers to the process of planning the experiments so that appropriate data that can be analysed by statistical methods will be collected, resulting in valid and objective conclusions.

Guidelines for designing an experiment1. Recognition of and statement of the problem2. Choice of factors, levels and ranges*3. Selection of response variable*4. Choice of experimental design5. Performing the experiment6. Statistical Analysis of the data7. Conclusions and recommendations*In practice, step 2 and 3 are often done simultaneously or in reverse orderResponse surface methodologyIt is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response.In Statistics, RSM explores relationship between several explanatory variables and one or more response variables.Idea is to use a sequence of designed experiments to obtain an optimal solution.Estimate first-degree polynomial by factorial experimentsExplains which explanatory variables have an impact on response variable of interest.By Box-behnken method, 2nd degree polynomial model is estimated.

This second degree polynomial can be used to optimize.Box-Behnken designA useful method for developing second-order response surface modelsBased on the construction of balanced incomplete block designs and requires at least three levels for each factor. Requires only three levels to run an experiment. It is a special 3-level design because it does not contain any points at the vertices of the experiment region.

Geometric representation

Number of trails and corresponding levelDesign matrixInput parameters with levelsFactors/LevelsLevel -1 (low)Level 0 (medium)Level 1 (high)Speed (rpm)8009001000Feed (mm/rev)300400500Depth of Cut (mm)0.51.01.5Design matrix for 45 degree tool orientationStd OrderRunOrderPtTypeBlocksFeed (mm/rev)Speed (rpm)Depth of cut (mm)81215009001.51221300800143215001000124215008001115214008001.51360140090013721300100011282140010001.599214008000.5510213009000.510112140010000.5612215009000.5713213009001.5Design matrix for 90 degree tool orientationStd OrderRunOrderPtTypeBlocksFeed (mm/rev)Speed (rpm)Depth of cut (mm)81215009001.51221300800143215001000124215008001115214008001.51360140090013721300100011282140010001.599214008000.5510213009000.510112140010000.5612215009000.5713213009001.5Sequence of operation1. Manufacture of workpieces (26 pieces) from casting2. Measurement of initial weight3. Machining in the Vertical Machining Center (MCV-1000)4. Measurement of Surface Roughness5. Measurement of final weight6. Calculation of MRR and Surface Roughness for each trials7. Analysis using MINITAB V168. Optimization of the responsesManufacture of workpiecesUsing a sand casting method.26 sets of workpieces were manufacturedMade up of SG Iron

MachiningPerformed at a vertical machining center, Shradha enterprises, Udyambag, BelgaumThe machine used is TAKUMI MCV-1000 model

Takumi MCV- 1000Machining procedure

Clamping of workpieceMilling cutterMachining with coolantMachining outputsRun Order (45 degree)Initial weight (kg)Final weight (kg)Time (min)Run Order (90 degree)Initial weight (kg)Final weight (kg)Time (min)10.9180.8480.3510.90020.8320.3520.87050.8480.4520.92490.9080.430.9550.8920.3530.94810.8820.3540.95810.8740.4540.92160.8580.450.89510.8620.450.86050.8220.3560.8690.8380.3560.92260.8880.470.90000.860.470.86690.8260.4580.92730.8580.3580.93950.8660.3590.93770.8580.4590.92850.8520.45100.85220.8320.45100.8560.8380.45110.8580.8420.4110.87660.8600.45120.9340.870.4120.9140.8520.4130.9120.8920.4130.90280.8440.35Measurement of Surface roughnessSurface roughness measurement is done using the Surtronic 3+ device available at metrology laboratory of Gogte Institute of Technolgy, Belgaum.

Observation tableObservation table for 45 degree tool orientationStd. OrderRun OrderPt. TypeBlocksFeed (mm/rev)Speed (rpm)DOC (mm)MRR (kg/min)SR (microns)81215009001.50.24.46122130080010.056.854321500100010.184.87242150080010.1875.28115214008001.50.08286.561360140090010.095.73721300100010.14.891282140010001.50.1984.5799214008000.50.177147.2510213009000.50.0455.2910112140010000.50.045.22612215009000.50.165.2713213009001.50.055.67Observation table for 90 degree tool orientationStd.OrderRunOrderPt.TypeBlocksFeed (mm/rev)Speed (rpm)DOC (mm)MRR (kg/min)SR (microns)81215009001.50.1954.4122130080010.04236.874321500100010.1894.66242150080010.1595.43115214008001.50.116.461360140090010.08655.513721300100010.0914.591282140010001.50.214.8899214008000.50.176.99510213009000.50.045.2910112140010000.50.0374.22612215009000.50.1554.55713213009001.50.1685.77Results and DiscussionsTo find out which factors among the speed, feed and DOC is significant in increasing MRR and reducing the SR and at what levelResponse surface analysis using MINITAB v16ANOVA to check adequacy of modelConfidence interval = 85 % Only terms whose p < 0.15 is used to develop empirical modelAnalysis done using coded units, so, empirical equation generated are expressed in coded unitsAnalysis of response for 45 degree tool orientationRegression analysis for MRRTermsCoeff.SE Coeff.T testP valueConstant0.090.0169185.3200.013Feed0.0602500.00598110.0730.002*Speed0.0026330.0059810.4400.690Depth of cut0.0135830.0059812.2710.108*Feed * Feed0.0142570.0111901.2740.292Speed *Speed0.0249930.0111902.2330.112*DOC * DOC0.0094930.0111900.8480.459Feed * speed-0.014250.008459-1.6850.191Feed * depth of cut0.0087500.0084591.0340.377Speed * DOC0.0630850.0084597.4580.005*Analysis of Variance for MRRSource DFSeq SSAdj SSAdj MSFPFeed10.0290410.0290410.029041101.460.002*Speed10.0000550.0000550.0000550.190.69Depth of cut10.0014760.0014760.0014765.160.108*Feed*Feed10.0000470.0004650.0004651.620.292Speed*Speed10.0012260.0014280.0014284.990.112*DOC*DOC10.0002060.0002060.0002060.720.459Feed*Speed10.0008120.0008120.0008122.840.191Feed*DOC10.0003060.0003060.0003061.070.377Speed*DOC10.0159190.0159190.01591955.620.005*Residual error30.0008590.0008590.000286Total120.49947Empirical model for MRR

MRR = 0.090 + 0.060250* feed +0.013583* DOC+ 0.024993* speed* speed + 0.063085* speed * DOC Main effect plot for MRR at 45 degree insert orientation

Interaction effect

Optimum machining parametersFeed (mm/rev)Speed (rpm)Depth of cut (mm)500(level 1)1000(level 1)1.5(Level 1)Regression analysis for surface roughness for insert at 45 degreeTermsCoeff.SE Coeff.T testP valueConstant5.70.382414.9070.001Feed-0.361250.1352-2.6720.076*Speed-0.79250.1352-5.8620.010*Depth of cut-0.206250.1352-1.5260.225Feed * Feed-0.480.2529-1.8980.154Speed *Speed0.25250.25290.9980.392DOC * DOC-0.0650.2519-0.2570.814Feed * speed0.38750.19122.0270.136*Feed * depth of cut-0.280.1912-1.4650.239Speed * DOC-0.00250.1912-0.0130.99Analysis of Variance for SRSource DFSeq SSAdj SSAdj MSFPFeed11.044011.044011.044017.140.076Speed15.024555.024555.0245534.370.01Depth of cut10.340310.340310.340312.330.225Feed*Feed10.884810.884810.884813.60.154Speed*Speed10.220080.220080.2200810.392DOC*DOC10.009660.009660.009660.070.814Feed*Speed10.600620.600620.600624.110.136Feed*DOC10.31360.31360.31362.140.239Speed*DOC10.000020.000020.0000200.99Residual error30.438620.438620.14621Total128.87620Empirical model for SR

SR = 5.7 0.36125 * feed 0.7925 * speed +0.38750 *feed* speedMain effect plot for SR at 45 degree insert orientation

Interaction effect

Optimum machining parametersFeed (mm/rev)Speed (rpm)Depth of cut (mm)500(level 1)1000(level 1)1.5(Level 1)Analysis of response for 90 degree tool orientationRegression analysis for MRRTermsCoeff.SE Coeff.T testP valueConstant0.08650.029432.9390.061*Feed0.0445880.010414.8250.023*Speed0.0057120.010410.5490.621Depth of cut0.0351250.010413.3760.043*Feed * Feed0.0207880.019471.0680.364Speed *Speed0.0130370.019470.670.551DOC * DOC0.0322120.019471.6550.197Feed * speed-0.004680.01472-0.3180.772Feed * depth of cut-0.0220.01472-1.4950.232Speed * DOC0.058250.014723.9590.029*Analysis of Variance for MRRSourceDFSeq SSAdj SSAdj MSFPFeed10.0159040.0159040.01590418.360.023Speed10.0002610.0002610.0002610.30.621Depth of cut10.009870.009870.0098711.40.043Feed*Feed10.000160.0009880.0009881.140.364Speed*Speed10.0000020.0003890.0003890.450.551DOC*DOC10.0023720.0023720.0023722.740.197Feed*Speed10.0000870.0000870.0000870.10.772Feed*DOC10.0019360.0019360.0019362.240.232Speed*DOC10.015720.135720.1357215.670.029Residual error30.0025980.0025980.000866Total120.046763Empirical model for MRR

MRR = 0.0865 + 0.044588 * feed + 0.03512 * DOC +0.05825 *speed * DOCMain effect plot for MRR at 90 degree insert orientation

Interaction effect

Optimum machining parametersFeed (mm/rev)Speed (rpm)Depth of cut (mm)500(level 1)1000(level 1)1.5(Level 1)Regression analysis for surface roughnessTermsCoeff.SE Coeff.T testP valueConstant5.510.308117.8860Feed-0.4350.1089-3.9940.028*Speed-0.9250.1089-8.4930.003*Depth of cut0.05750.10890.5280.634Feed * Feed-0.378750.2038-1.8590.16Speed *Speed0.256250.20381.2580.298DOC * DOC-0.128750.2038-0.6320.572Feed * speed0.37750.1542.4510.092*Feed * depth of cut-0.15750.154-1.0230.382Speed * DOC0.29750.1541.9310.149*Analysis of Variance for SRSource DFSeq SSAdj SSAdj MSFPFeed11.51381.51381.513815.950.028Speed16.8456.8456.84572.130.003Depth of cut10.02640.02640.026450.280.634Feed*Feed10.5350.32790.327893.460.16Speed*Speed10.27160.15010.150091.580.298DOC*DOC10.03790.03790.037890.40.572Feed*Speed10.570.570.570036.010.092Feed*DOC10.09920.09920.099221.050.382Speed*DOC10.3540.3540.354033.730.149Residual error30.28470.28470.0949Total1210.5377Empirical model for SR

SR = 5.51 0.435 * feed 0.925 *speed + 0.37750* feed* speed + 0.29750* Speed * DOC Main effect plot for SR at 90 degree insert orientation

Interaction effect

Optimum machining parametersFeed (mm/rev)Speed (rpm)Depth of cut (mm)500(level 1)1000(level 1)0.5(Level -1)ConclusionANOVA result showed that for both condition of tool orientation, feed and depth of cut have significant impact on material removal rate. Interaction of speed and DOC also have significant impact on it. For surface roughness, feed and speed has main effect and interaction of feed and speed also has significant impact for 45 degree tool orientation and for 90 degree orientation along with above factors a combination of speed and DOC is also significant.

Future worksResults obtained can be used as a model for the selection machining parameters while machining in a VMC in order to obtain optimum MRR and SR.Also possible to study the effect of various other parameters like number of passes, tool diameter, austempering temperature of spheroidal graphite iron and so on.Another possibility is the use of collected data and results in order to find the signal to noise ratio by using the Taguchi method.

THANK YOU