Modelling and prediction of surface roughness

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Modelling and prediction of surface roughness

  1. 1. INTERNATIONALMechanical Engineering 3, Issue 3, Sep- Dec (2012)ENGINEERING International Journal of JOURNAL OF and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume MECHANICAL © IAEME AND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online)Volume 3, Issue 3, September - December (2012), pp. 599-613 IJMET© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2012): 3.8071 (Calculated by GISI) ©IAEMEwww.jifactor.com MODELLING AND PREDICTION OF SURFACE ROUGHNESS, CUTTING FORCE AND TEMPERATURE WHILE MACHINING NIMONIC-75 AND NICROFER C-263 SUPER ALLOYS USING ARTIFICIAL NEURAL NETWORK (ANN) P. SUBHASH CHANDRA BOSE1, C S P RAO2 1 (Department of Mechanical Engineering, National Institute of Technology- Warangal, India, subhashnitw@gmail.com) 2 (Department of Mechanical Engineering, National Institute of Technology- Warangal, India, csp_rao63@yahoo.com)ABSTRACT In the present investigation the influence of process parameters like speed, feed, depth ofcut in dry machining, are studied as surface roughness, cutting force and temperature as theoutput. An artificial neural network (ANN) model was developed for the analysis and predictionof the relationship between input and output parameters during high-speed turning of nickel-based alloys like Nimonic-75. The input parameters of the ANN model are the cuttingparameters: speed, feed rate and depth of cut. The output parameters of the model are three,measured during the machining trials namely surface roughness (Ra), cutting force (Fz) andtemperature (T). The model consists of a two layered feed forward back propagation neuralnetwork. The network is trained with pairs of inputs/outputs datasets generated when machiningNimonic-75 with TN6025 coated carbide tool. A highly efficient neural network, in agreementwith the experimental data, was achieved. The model can be used for the analysis and predictionof the complex relationship between cutting conditions and the output parameters in metal-cutting operations and for the optimization of the cutting process for efficient and economicproduction.Keywords: Artificial Neural Network; Nimonic-75; Surface Roughness; Cutting Force;Temperature 599
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME1. INTRODUCTION Machining super alloys has become an important manufacturing process, particularly in theautomotive, aerospace and gas turbine manufacturing industries. Metal cutting is one of the important and widely used manufacturing processes in engineeringindustries. The study of metal cutting focuses among others on the features of tools, input work materials,and machine parameter settings influencing process efficiency and output quality characteristics (orresponses). The quality of a machined surface is always an important parameter in the componentperformance and reliability. While machining any component, it is necessary to satisfy the surfacetechnological requirements in terms of high product accuracy, good surface finish and minimum ofdrawbacks that may arise as a result of possible surface alterations by the machining process. Finish hard turning is an emerging machining process which enables manufacturers to machinehardened materials having hardness greater than 45 HRC using a single point cutting tool without any aidof cutting fluid on a rigid lathe or turning center. This process has been developed as an alternative to thegrinding process in a bid to reduce the number of setup changes, product cost and lead time withoutcompromising on surface quality to maintain competitiveness. For successful implementation of hardturning, selection of suitable cutting parameters for a given cutting tool work piece material and machinetool are important and need to be developed through experimentation as the suitable cutting parameterare not available. Machining of super-alloys is a challenging task. Nickel based super-alloys like Nimonic, Nicroferand Niobium have got wide application in missile technology and other defense applications due to itsstrength at high temperatures. Hence evaluation of machining parameters is the need of the hour formanufacturing of super-alloy components. Hence an attempt is made by the author to evaluate surface finish, cutting force and temperature inmachining of Nimonic-75 and Nicrofer C-263 alloys. Several experiments were conducted using DOEand ANOVA analysis was done on the output parameters. The process is simulated by modeling withANN for prediction of surface finish, cutting force and temperature at different values of speed, feed anddepth of cut. An artificial neural network (ANN), usually called neural network (NN), is a mathematical modelor computational model that is inspired by the structure and/or functional aspects of biological neuralnetworks. A neural network consists of an interconnected group of artificial neurons and it processesinformation using a connectionist approach to computation. In most cases an ANN is an adaptive systemthat changes its structure based on external or internal information that flows through the network duringthe learning phase. Modern neural networks are non-linear statistical data modeling tools. They areusually used to model complex relationships between inputs and outputs or to find patterns in data. Fig 1 Dimensions of the Carbide Tool 600
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME2. MODEL DESCRIPTION There has been in an increase in the research interest in the applications of ANN’s modeling the relationship between the cutting conditions and the process parameters during the machining process. The input parameters of the neural networks being the speed, feed and depth of cut and the output parameters are three of the most important process parameters namely, surface roughness (Ra), Cutting forces (Fz) and Cutting Temperature (T). The five basic steps used in general application of the neural networks are adopted in the development of the model: assembly or collection of data; analysis and pre-processing of the data; design of the network object; training and testing of the network; and performing simulation with trained network and post –processing of results. 2.1 Experimentation/collection of input/output data set Rigid, high power, 12kW VDF retrofitted CNC lathe with a speed of 56-3550 rpm. 38 mmdiameter and 250 mm long cast solution treated, annealed and hardened, nickel chromium alloy withcontrolled additions of titanium and carbon, Nimonic 75, alloy bars were used. The chemicalcomposition and physical properties of the work pieces are given in Table 1 and 2, respectively. Beforeconducting the machining trails, up to 2 mm thickness of the top surface of each bar was cleaned in orderto eliminate any skin defect that can adversely affect the machining results. Multi-layered PVD coated cemented tungsten carbide inserts were used for the turning tests. Thecoated carbide grade was TN6025; the dimensioning of the tool is given in fig 1, which is designed forlight and medium turning operations of high temperature alloys. TN6025 inserts are Nano-multilayeredTiAlN coated insert, having very high wear resistance and also good toughness. Table 1 Chemical composition of Nimonic 75 (wt %) Element C Cr Cu Fe Mn Si Ti Ni Wt (%) 0.08 18 0.5 5 1.0 1.0 0.2 Balance* Table 2 Physical Properties of Nimonic 75 Hardness Density Melting Point Thermal conductivity Electrical Resistivity (HRC) (mg/m3) (0C) (W/mK) (µ .m) 28 8.37 1340 11.7 1.09 The measurements of average surface roughness (Ra) were made on HANDYSURF E 35 B. Threemeasurements of surface roughness were taken at different locations and the average value is used in theanalysis. It directly gives the value in digital format. Infrared thermometer Kiray 300 was used for temperature measurement, while conducting theexperiments. This is a thermometer used to diagnose, inspect and check any temperature. Thanks to itselaborated optical system with a dual laser sighting, it allows easy and accurate measurements of littledistant targets. The KIRAY 300 instrument has an internal memory which can save up to 100measurements. Compatible with thermocouple K probe. Four-component dynamometer was used to measure the cutting force components. Thisdynamometer can be used for measuring a torque Mz and the three orthogonal components of a force.The dynamometer has a great rigidity and consequently a high natural frequency. Its highresolution enables the smallest dynamic changes in large forces and torques to be measured.Compact and robust multi-component force measuring instrument 601
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME 2.2 Preprocessing of input/output datasets The working ranges of the parameters, for the subsequent design of experiment, were selectedbased on tool manufacturer recommendations and machine tool capabilities. In the present experimentalstudy, three parameters such as speed, feed and depth of cut have been considered as process variableswith 5, 4, 3 levels respectively. The levels have been so selected based on the intuition of the affect ofthese parameters on the output process parameters. The values are listed in Table 3 below. TABLE 3 Cutting Level Controllable factors Level 1 Level 2 Level 3 Level 4 Level 5 Speed 30 36.25 42.5 48.75 55 Feed 0.08 0.12 0.16 0.20 - Depth of cut 0.5 1.0 1.5 - - Experiments have been carried out using full factorial experimental design which consists of 60combinations of speed, feed and depth of cut. Out of which 50 have been used for training the neuralnetworks and 10 have been used for testing the networks. The design of experiment has been shown inTable 4. TABLE 4 Designs of Experiments S. No Speed Feed Doc 31 42.5 0.08 1 1 42.5 0.12 1.5 32 55 0.08 0.5 2 42.5 0.12 0.5 33 36.25 0.16 1.5 3 55 0.12 1 34 30 0.16 1 4 30 0.12 1 35 55 0.08 1.5 5 42.5 0.16 1 36 30 0.2 1.5 6 55 0.08 1 37 55 0.2 1 7 48.75 0.2 0.5 38 55 0.16 1.5 8 36.25 0.16 1 39 42.5 0.08 0.5 9 48.75 0.12 1 40 48.75 0.2 1 10 36.25 0.08 1 41 36.25 0.2 1 11 36.25 0.2 1.5 42 48.75 0.12 0.5 12 36.25 0.12 1.5 43 36.25 0.08 1.5 13 30 0.2 0.5 44 48.75 0.16 1.5 14 48.75 0.16 1 45 36.25 0.12 0.5 15 36.25 0.12 1 46 30 0.12 0.5 16 30 0.08 1.5 47 30 0.16 0.5 17 36.25 0.2 0.5 48 42.5 0.16 1.5 18 48.75 0.08 1.5 49 55 0.2 0.5 19 55 0.16 1 50 55 0.12 1.5 20 30 0.08 1 51 55 0.16 0.5 21 55 0.12 0.5 52 42.5 0.2 1 22 48.75 0.08 1 53 30 0.12 1.5 23 30 0.08 0.5 54 55 0.2 1.5 24 48.75 0.08 0.5 55 36.25 0.16 0.5 25 42.5 0.2 0.5 56 48.75 0.12 1.5 26 48.75 0.2 1.5 57 30 0.2 1 27 30 0.16 1.5 58 42.5 0.12 1 28 42.5 0.2 1.5 59 48.75 0.16 0.5 29 36.25 0.08 0.5 60 42.5 0.16 0.5 30 42.5 0.08 1.5 602
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME 2.3 Neural network design and training The network architecture or feature such as number of neurons and layers are very importantfactors that determine the functionality and generalization capability of the network. For this model,standard multilayered feed-forward back propagation hierarchical neural networks were designed withMATLAB 7.7 Neural network toolbox. The network consists of 4 layers, one input, two hidden layersand one output layer. In general, the networks have three neurons in the input, corresponding to each ofthe three cutting parameters and one neuron in the output, corresponding to each of the processparameter. For all networks, linear transfer function ‘purelin’ has been used for input and output layersand tangent transfer sigmoid transfer function ‘tansig’ were used in the hidden layers. Three differentneural network models are used, one for the prediction of surface roughness, second for the prediction ofcutting force and third for the prediction of temperature. The networks were trained with Levenberg-Marquardt algorithm. This training algorithm waschosen due to its high accuracy in similar function approximation [3, 15]. In order to improve thegeneralization of the network, a ‘regularization’ scheme was used in conjunction with the Levenberg-Marquardt algorithm. The automatic Bayesian regularization was used. For training with Levenberg-Marquardt combined with Bayesian regularization, the input/outputdataset was divided randomly into two categories: training dataset, consisting of 50 of the input/outputdataset and the remaining as the test dataset. Fig 2 Picture Depicting the Layers in a Neural Network 2.4 Testing and performance of the network In order to determine the optimum number of neurons in the hidden layers, the testing was donetaking 5, 10, 15, 20, 25 neurons in each hidden layer and for each iteration the best performance errorwas calculated and then compared. Table 5 shows the combinations of neurons in the first and secondhidden layers and the best performance error calculated for all the iterations for the surface roughnessprocess parameter. The values of the error with 20 neurons in the second hidden layer are lower than theother error values for all the other combinations. Table 6 gives us the values of best performance errorfor surface roughness parameter by iterating the neurons in the 1st hidden layer with fixed neurons in thesecond hidden layer. Table 7 and 8 show us the iterations through which the final set of neurons havebeen selected. Thus, the network having two layers of 20 neurons and 19 neurons, respectively, trainedwith Levenberg-Marquardt algorithm and Bayesian regularization has been chosen as the optimumnetwork and used for development of this model. The performance of the model for prediction of thesurface roughness with an error of 0.00262 has been developed. The combination which gave the least 603
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEerror was selected for the testing of the neural networks. The predicted values from the neural networkare then compared to the experimentally obtained values. Three inputs are taken for all the threenetworks, which are speed, feed and depth of cut. In this, linear function was used for input layer/ outputlayer and sigmoid function is used for hidden layers. This demonstrated that the models have highaccuracy for predicting the process parameters. Acceptable results have were also obtained for all theprocess parameters. The analysis of variance (ANOVA) was performed to statistically analyze theresults. An ANOVA summary table is commonly used to summarize the test of the regressionmodel, test of the significance factors and their interaction and lack-of-fit test. If the value of‘Probability > F’ in ANOVA table is less than 0.05 then the model, the factors, interaction offactors and curvature are said to be significant. Finally, % contribution column is added in ANOVAsummary table and it often serves as a rough but an effective indicator of the relative importance of eachmodel term. TABLE 5 Best Performance Error for Surface Roughness parameter by iterating the neurons in the second hidden layerNo of Neurons No of Neurons 15 10 0.0642 Best Performance in 1st Hidden in 1st Hidden 15 15 0.0541 Error Layer Layer 15 20 0.0124 5 5 0.0242 15 25 0.0329 5 10 0.379 20 5 0.0142 5 15 0.0386 20 10 0.0263 5 20 0.0142 20 15 0.0281 5 25 0.0268 20 20 0.00834 10 5 0.0468 20 25 0.0142 10 10 0.0486 25 5 0.0163 10 15 0.0562 25 10 0.024 10 20 0.013 25 15 0.029 10 25 0.142 25 20 0.0381 15 5 0.0565 25 25 0.024TABLE 6 Best Performance Error for Surface Roughness parameter by iterating the neurons in the first hidden layer with fixed neurons in the second hidden layer No of Neurons in 1st No of Neurons in 1st Hidden Best Performance Error Hidden Layer Layer 5 20 0.0142 10 20 0.013 15 20 0.0124 20 20 0.00834 25 20 0.0381 TABLE 7 Best Performance Error for Surface Roughness parameter, to fix the neurons in the first hidden layer No of Neurons in 1st No of Neurons in 1st Hidden Best Performance Error Hidden Layer Layer 15 20 0.0124 17 20 0.0139 19 20 0.0432 20 20 0.00834 21 20 0.0328 23 20 0.056 25 20 0.0381 604
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME TABLE 8 Best Performance Error for Surface Roughness parameter, to fix the neurons in the second hidden layer st No of Neurons in 1 No of Neurons in 1st Best Performance Hidden Layer Hidden Layer Error 20 15 0.0281 20 17 0.0174 20 18 0.0382 20 19 0.0262 20 20 0.0834 20 21 0.0943 20 23 0.0262 20 25 0.01423. Results and Discussions 3.1 Results The predicted values and experimental values of surface roughness, cutting force and temperature both training and testing are shown in the fig 3, 4 and 5, respectively. From the above said figures it is evident that network responded well for the testing data as well. Fig. 3 Experimental and predicted Ra values 605
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Fig. 4 Experimental and predicted Cutting Force values Fig. 5 Experimental and predicted Temperature values 606
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 9 Ra values Exp no Measured Predicted Percentage values values of error 1. 1.31 1.25 4.58 2. 1.71 1.65 3.50 3. 0.82 0.75 8.53 4. 0.97 0.90 7.21 5. 0.72 0.62 13.88 6. 0.8433 0.733 13.07 7. 1.15 1.02 11.30 8. 2.18 2.08 4.58 9. 1.36 1.25 8.08 10. 0.97 0.80 17.52 Table 10 Cutting Force values Exp Measured Predicted Percentage no values values of error 1. 407.3 423.715 4.1301 2. 555.9 549.0548 1.231 3. 119.3 107.41 9.96 4. 88.72 79.72 10.14 5. 164.2 150.24 8.501 6. 617 610.55 1.134 7. 477.7 487.7 2.093 8. 275.3 276.0625 0.276 9. 375.2 377.8248 0.699 10. 184.5 175.15 5.067 607
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 11 Temperature values Exp Measured Predicted Percentage no values values of error 1. 154.5 152.1884 1.49 2. 148.5 145.9225 1.73 3. 70.4 74.708 6.11 4. 73.1 70.2374 3.91 5. 168.3 160.45 4.66 6. 170.2 165.02 4.01 7. 96.6 89.6 7.24 8. 92.2 80.6 12.58 9. 137.7 125.922 8.55 10. 82.8 75.7979 8.453.2 Evaluation of performance of the network From different runs of the program, the best architectures are found to be that training 10neurons in the hidden layer and regression R=0.8661 and no of epochs=69for the prediction ofRa. 20 in the hidden layer and the R=0.9065 and no of epochs=93 for the prediction oftemperature, 44 neurons in the hidden layer and the R=0.90581 and no of epochs=89for theprediction of cutting forces. Mean relative error is used for evaluation of performance of the networks. The calculatedmean relative errors of the network used for predicting surface roughness, temperature andcutting forces found to be 0.92% ,5.873% and4.32% respectively. These values show that theaccuracy of neural network is good.3.3 Response tables for surface roughness, cutting force and temperature without interactions. Table 12 Response table for Ra Level Speed Feed Doc 1 1.3283 0.973 1.0905 2 1.0192 1.1693 1.3640 3 1.1342 1.2707 1.2202 4 1.1775 1.4860 ----- 5 1.4653 ----- ----- Max-Min 0.4461 0.513 0.2735 Rank 2 1 3 608
  11. 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 13 Response table for Cutting Force Level Speed Feed Doc 1 106.10 114.13 87.89 2 114.45 115.51 116.86 3 111.15 108.67 127.73 4 103.68 105.00 ----- 5 118.76 ----- ----- Max-Min 15.08 10.51 39.84 Rank 2 1 3 Table 14 Response table for Temperature Level Speed Feed Doc 1 285.6 235.6 155.0 2 327.9 285.8 305.6 3 318.9 346.8 467.1 4 309.6 368.7 ----- 5 304.1 ----- ----- Max-Min 42.3 133.1 312.1 Rank 2 1 33.4 Analysis of variance tables for surface roughness, cutting force and temperature Table 15 ANOVA table for Ra S. No Source DOF Sum of squares Mean squares F Value % contribution 1 Speed 4 1.4554 0.3639 1.51 8.91 2 Feed 3 12.0480 0.6827 2.88 73.82 3 Doc 2 0.7487 0.3743 0.59 4.58 4 Error 50 1.6652 0.2413 ----- ----- 5 total 59 16.3192 ----- ----- ----- Table 16 ANOVA table for Cutting ForceS. No Source DOF Sum Of Squares Mean Squares F Value % Contribution 1 Speed 4 12319 3080 0.55 8.6 2 Feed 3 163632 54544 9.72 11.4 3 Doc 2 974660 487330 86.87 68.10 4 Error 50 280507 5610 ----- ----- 5 Total 59 1431118 ----- ----- ----- 609
  12. 12. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 17 ANOVA table for Temperature S.No Source DOF Sum Of Squares Mean Squares F Value % Contribution 1 Speed 4 16966.5 448.9 0.96 39.16 2 Feed 3 1070.7 356.9 0.76 2.47 3 Doc 2 23483.9 8483.2 18.06 54.21 4 Error 50 1795.6 469.7 ----- ----- 5 Total 59 43316.7 ----- ----- ----- From the analysis of table 15, we can observe that the cutting factors speed (p= 8.91%), feed(p= 73.82%), doc (4.58%) have statistical significance on the surface roughness obtained,especially the feed. From the analysis of the table 16, we can observe that the cutting factors speed(P=8.6%), feed (P=11.9%) and Doc (P=68.01%) have statistical significance on the cutting forceobtained, especially the Doc. From the analysis of the table 17, we can observe that the cutting factorsspeed (P=.21%), feed (P=2.47%) and Doc (P=39.1654%) have statistical significance on thetemperature obtained, especially the Doc.3.5 Analysis of variance tables for surface roughness, cutting force and temperature with interactions. Table 18 ANOVA table for Ra with interactions S.No Source DOF Sum of squares Mean square F Value % contribution 1 Speed 4 1.4554 0.3639 1.63 8.91 2 Feed 3 5.3677 0.6827 3.05 32.10 3 Doc 2 0.7487 0.3743 1.67 4.5 4 Speed*feed 12 2.8356 0.2363 1.06 17.37 5 Speed*doc 8 1.2415 0.1552 0.69 7.60 6 Feed*doc 6 2.6223 0.4370 1.95 16.06 7 Error 24 2.0480 0.2337 ----- ----- 8 Total 59 16.3192 ------ ----- ----- Table 19 ANOVA table for Cutting Force with interactions S.No Source Dof Sum of squares Mean square F Value % contribution 1 Speed 4 12319 3080 0.68 0.8 2 Feed 3 163632 54544 12.06 11.43 3 Doc 2 974660 487330 107.73 68.10 4 Speed*feed 12 55041 4587 1.01 3.84 5 Speed*doc 8 74288 9286 2.05 5.19 6 Feed*doc 6 42613 7102 1.57 2.97 7 Error 24 108565 4524 ----- ----- 8 total 59 1431118 ----- ----- ----- 610
  13. 13. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 20 ANOVA table for Temperature with interactions S.No Source Dof Sum of squares Mean square F Value % contribution 1 Speed 4 14323.5 448.9 0.75 33.06 2 Feed 3 1070.7 356.9 0.60 2.47 3 Doc 2 16966.5 8483.2 14.21 39.16 4 Speed*feed 12 3256.6 271.4 0.45 7.518 5 Speed*doc 8 3990.1 498.0 0.84 4.416 6 Feed*doc 6 1913.1 319.0 0.53 4.419 7 Error 24 1797.6 596.0 ----- ----- 8 total 59 43316.7 ----- ----- ----- From the analysis of table 18, we can observe that the cutting factors speed (P=8.91%),feed (P=32.10%), doc (P=4.5%), speed*feed (P=17.37%), speed*doc (P=7.6%), feed*doc(P=16.06%) have statistical significance on the surface roughness obtained, especially the feed.From the analysis of table 19, we can observe that the cutting factors speed (P=8.6%), feed(P=11.43%), doc (P=68.10%), speed*feed (P=3.84%), speed*doc (P=5.19%), feed*doc(P=2.97%) have statistical significance on the cutting force Fz obtained, especially the doc. Fromthe analysis of table 20, we can observe that the cutting factors speed (P=39.16%), feed(P=2.47%), doc (P=33.06%), speed*feed (P=7.518%), speed*doc (P=4.416%), feed*doc(P=4.419%) have statistical significance on the temperature obtained, especially the speed anddoc. As we increase the speed, the input parameter, from figure 6, the Surface roughnessinitially decreases and later on increases more or less reaching to the same initial value of surfaceroughness within the range of input parameters taken. With increase in feed, from figure 7, thesurface roughness does on increasing. Whereas with increase in depth of cut, the surfaceroughness has no significant effect, figure 8. For the cutting forces as the output parameter, from figure 9, as we increase the speed, thecutting forces initially increase forming a peak and later on decrease. With increase in the feed,from figure 10, the cutting forces increase rapidly. Similarly with increase in depth of cut thecutting forces are increasing, from figure 11. With increase in speed, the Temperature have seen a initial increase in temperatures with alocal minimum and then a increasing trend as we increase the speed, from figure 12. From figure13, with increase in feed the temperature first increased and later on decreased. With increase indepth of cut, the temperature goes on increasing, from figure 14. 611
  14. 14. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Fig. 6 Speed Vs ‘Ra’ mean Fig.7 Feed Vs ‘Ra’ mean Fig. 8 D.O.C Vs ‘Ra’ mean Fig.9 Speed Vs ‘Fz’ mean Fig. 10 Feed Vs ‘Fz’ mean Fig.11 D.O.C Vs ‘Fz’ mean Fig. 12 Speed Vs Temperature mean Fig.13 Feed Vs Temperature mean Fig. 14 D.O.C vs. Temperature mean 612
  15. 15. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME4. CONCLUSIONS1. The plot of experimental and predicted values from neural network shows that the network has been trained well and has good generalization ability.2. The network having two layers of 20 neurons and 19 neurons, respectively, trained with Levenberg-Marquardt algorithm and Bayesian regularization has been chosen as the optimum network and used for development of this model.3. The performance of the model for prediction of the surface roughness with an error of 0.00262 has been developed.4. The optimum cutting speeds at which the minimum process parameters were obtained were is 35, 55 and 48.75 for surface roughness, cutting forces and temperature respectively.5. With increase in feed, the output parameters surface roughness and cutting forces have increased consistently, while temperatures have decreased.6. Depth of cut has had the same effect, more or less, on all the parameters. With increase in the depth of cut all the output parameters have increased.REFERENCES1. E.O. Ezugwu, D.A. Fadare, J. Bonney, R.B. Da Silva, W.F. Sales - Modeling the correlation between cutting and process parameters in high speed machining of Inconel 718 alloy using an artificial neural network.2. E.O. Ezugwu, Advances in the machining of nickel and titanium base superalloys, Keynote paper presented at the Japan Society for Precision Engineering Conference 2004, pp. 1–40.3. S. Malinov, W. Sha, J.J. McKeown, Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network, Comput. Mater. Sci. 21 (2001) 375–394.4. E.O. Ezugwu, K.A. Olajire, J. Bonney, Modelling of tool wear based on component forces, Tribol. Lett. 11 (1) (2001).5. E.O. Ezugwu, J. Bonney, Effect of high-pressure coolant supply when machining nickel-base, Inconel 718, alloy with coated carbide tools, Proceedings of AMPT 2003, 8–11 July 2003, Dublin, Ireland, pp.787–790.6. E.O. Ezugwu, J. Bonney, Effect of high-pressure coolant supply when machining nickel-base, Inconel 718, alloy with coated carbide tools, J.Mater. Process. Technol. 153–154 (2004) 1045–1050.7. J. Bonney, High-speed machining of nickel-base, Inconel 718, alloy with ceramic and carbide cutting tools using conventional and high-pressure coolants, PhD Thesis, London South Bank University, 2004.8. E.O. Ezugwu, A.R. Machado, I.R. Pashby, J. Wallbank, The effect of high-pressure coolant supply when machining a heat-resistant nickel-based superalloy, Lubr. Eng. 47 (9) (1991) 751–757.9. E.O. Ezugwu, S.J. Arthur, E.L. Hines, Tool-wear prediction using artificial neural networks, J. Mater. Process. Technol. 49 (1995)255–264.10. D.E. Dimla Sr., Application of perceptron neural networks to tool-state classification in a metal- turning operation, Eng. Appl. Artif.Intell. 12 (1999) 471–477.11. H. Demuth, M. Beale, Neural Network Toolbox User’s Guide, Version 4 (Release 12), The Mathworks, Inc., 2000.12. R.S. Pawade a, Suhas S. Joshi a, P.K. Brahmankar b, M. Rahman “An investigation of cutting forces and surface damage in high-speed turning of Inconel 718” (Journal of Materials Processing Technology 192-193 (2007) 139-146). 613

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