prediction of surface roughness in cnc milling … · 2015-04-09 · response surface methodology...

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Int. J. Mech. Eng. & Rob. Res. 2014 Ravikumar D Patel et al., 2014

PREDICTION OF SURFACE ROUGHNESS IN CNCMILLING MACHINE BY CONTROLLINGMACHINING PARAMETERS USING ANN

Ravikumar D Patel1*, Nigam V Oza1 and Sanket N Bhavsar2

*Corresponding Author: Ravikumar D Patel, [email protected]

By optimization of various parameters of CNC milling process like spindle speed, feed rate anddepth of cut, Improvement can be achieved in surface finishing. Various methods are used forpredict surface roughness in CNC milling machine. Here Artificial Neural Network has beenimplemented for better and nearest result. By using this paper, mathematical model can bedeveloped easily for milling process. Number of experiments have been done by using Hy-techCNC milling machine. Conclusion from Taguchi method, Surface roughness is most influencedby Feed rate followed by spindle speed and lastly depends on depth of cut. Predicted surfaceroughness has been obtained, average percentage error is calculated by ANN method. Themathematical model is developed by using Artificial Neural Network (ANN) technique shows thehigher accuracy is achieved which is feasible and more efficient in prediction of surface roughnessin CNC milling. The result from this paper is useful to be implemented in manufacturing industryto reduce time and cost in surface roughness prediction.

Keywords: CNC milling, ANN, Surface roughness

INTRODUCTIONHigher quality is mainly goal of modernmachining industries. CNC milling is a verycommonly used machining process now inindustry. High production rate with goodsurface finish and low machining time is themain criteria for industries today. Toachievement for higher surface finishing,machining parameters like spindle speed,

ISSN 2278 – 0149 www.ijmerr.comVol. 3, No. 4, October 2014

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Int. J. Mech. Eng. & Rob. Res. 2014

1 Government Polytechnic College, Nr. Shankhalpur Talav, Vadnagar, Mehsana, Gujarat, India.2 Mechatronics Department, G H Patel College of Engineering & Technology, Vallabh Vidyanagar 388120, Anand, Gujarat, India.

feed rate and depth of cut should be properlycontrolled. The ability to control the processfor better surface finishing of the final productis most importance. The mechanism for thegeneration of surface roughness in CNC millingprocess is very complicated, and processparameters dependent. In CNC millingmachining process, some of the parameterscan be controlled like Spindle speed, Feed,

Research Paper

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Depth of Cut, by choosing particular drill cuttingtool with required angle and flute, number ofpass etc. Sometimes, machine operators areusing ‘trial and error’ method to set-up millingmachine for cutting workpiece. This methodis not effective and efficient. It is a repetitiveand very time consuming method. Thus, amathematical model using statistical methodprovides a better solution.

Multiple regression analysis is suitable tofind the best combination of independentvariables (Like spindle speed, feed rate andthe depth of cut) in order to achieve desiredsurface roughness. Unfortunately, multipleregression model is obtained from a statisticalanalysis which require to collect large numberof sample data.

Artificial Neural Network (ANN) is state ofthe art. Artificial intelligent method is the bestmethod to prediction of surface roughness.This paper will present the application of ANNto predict surface roughness for CNC millingprocess.

LITERATURE REVIEWGhani et al. (2004) outlined the Taguchioptimization methodology, which was applied

to optimize cutting parameters in end millingwhen machining hardened steel AISI H13 withTiN coated P10 carbide insert tool under semi-finishing and finishing conditions of high speedcutting. The milling parameters evaluated wascutting speed, feed rate and depth of cut. Anorthogonal array, Signal-to-Noise (S/N) ratioand Pareto analysis of variance (ANOVA)were employed to analyze the effect of thesemilling parameters. Wattanutchariya andPintasee (2006) attempted to optimize themetallic milling parameters for surfacefinishing. The two controlled parameters werespindle speed and feed rate. Three materials:aluminum, brass and cast iron were tested.The research methodology concerned theResponse Surface Methodology (RSM) byCentral Composite Design (CCD). Then, theAl 2072, brass with 10% zinc and cast iron(A287) were tested in order to investigate therelationship between the controlledparameters. Gopalsamy (2009) appliedTaguchi method to find optimal processparameters for end milling while hardmachining of hardened steel. An orthogonalarray, signal-to-noise ratio and ANOVA wereapplied to study performance characteristicsof machining parameters (cutting speed, feed,depth of cut and width of cut) with considerationof surface finish and tool life. Chipping andadhesion were observed to be the maincauses of wear. Multiple regression equationswere formulated for estimating predictedvalues of surface roughness and tool wear.Ginta et al. (2009) focused on developing aneffective methodology to determine theperformance of uncoated WC-Co inserts inpredicting minimum surface roughness in endmilling of titanium alloys Ti-6Al-4V under dryconditions. Central composite design of

Figure 1: Artificial Neural Network (ANN)

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response surface methodology was employedto create an efficient analytical model forsurface roughness in terms of cuttingparameters: cutting speed, axial depth of cut,and feed per tooth. Ab. Rashid et al. (2009)presented the development of mathematicalmodel for surface roughness prediction beforemilling process in order to evaluate the fitnessof machining parameters; spindle speed, feedrate and depth of cut. Multiple regressionmethod was used to determine the correlationbetween a criterion variable and acombination of predictor variables. It wasestablished that the surface roughness wasmost influenced by the feed rate. Alwi (2010)studied the optimum of surface roughness byusing response surface method. Theexperiments were carried out using CNCmilling machine. All the data was analyzed byusing Response Surface Method (RSM) andNeural Network (NN). The result showed thatthe feed gave the more affect on the bothprediction value of Ra compare to the cuttingspeed and depth of cut. Routara et al. (2010)highlighted a multi-objective optimizationproblem by applying utility concept coupledwith Taguchi method through a case study inCNC end milling of UNS C34000 mediumleaded brass. Rashid and Abdul Lani (2010)Derive the nearest result for predict surfaceroughness by Comparision between MultipleRegression Analysis and Artificial NeuralNetwork for predict surface roughness.

Moaz Ali and Basim (2013) developedFinite Element Modeling (FEM) to predict theeffect of feed rate on surface roughness withcutting force during face milling of titanium alloy.In this paper, focus is on the effect of feed rate(f) on surface roughness (Ra) and cutting force

components (Fc, Ft) during the face-millingoperation of the titanium alloy (Ti-6Al-4V) andseveral tests are performed at several feedrates (f) while the axial depth of the cut andcutting speed remain constant in dry cuttingconditions. Results showed that one couldpredict the surface roughness by measuringthe feed cutting force instead of directlymeasuring the surface roughnessexperimentally through using the finite elementmethod to build the model and to predict thesurface roughness from the values of the feedcutting force. The accuracy of both values ofthe cutting force for the experimental andpredicted model was about 97%.

METHODOLOGYArtificial Neural Networks (ANNs) are one ofthe most powerful techniques, currently beingused in various fields of engineering forcomplex relationships which are difficult todescribe with physical models. The input andoutput data set of the ANN model is generatedto predict surface roughness. The inputparameters of the artificial neural network areSpindle Speed, Feed rate and Depth of Cut.The output of this ANN model is SurfaceRoughness.

For this study, the network is given a set ofinputs and corresponding desired outputs, andthe network tries to learn the input-outputrelationship by adapting its free parameters.The activation function f(x) used here is thesigmoid function which is given by:

xxf

exp1

1

Between the input and hidden layer:

m

ijijiux

1

j = 1 to n

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and between hidden layer and output layer:

m

jkjkjux

1

k = 1 to i

where

m = Number of input nodes

n = Number of hidden nodes

i = Number of output nodes

u = Input node values

v = Hidden node values

= Synaptic weight

= Threshold

In back-propagation neural network, thelearning algorithm has two phases. One, atraining input pattern is indicated to the networkinput layer. The network then propagates theinput pattern from layer to layer until the outputpattern is generated by the output layer. If thispattern is different from the desired output, anerror is calculated and then propagatedbackwards through the network from the outputlayer to the input layer.

And Second, a back-propagation one isdetermined by the connections between theneuron (Network’s architecture), the activationfunction used by the neurons, and the learningalgorithm (or learning law) that specifies theprocedures for adjusting weights.

Typically, a back-propagation network ismultilayer network that has three or four layers.The layers are fully connected, that is, everyneuron in each layer is connected to every otherneuron in the adjacent forward layer. The neuralnetwork computational model coding is builtusing MATLAB software.

EXPERIMENTAL SETUPAluminium 80 x 80 x 20 mm piece is machinedby HSS CNC milling cutter is used. Here threetypes of spindle speeds, three types of feedand three types of depth of cut are taken andachieve surface roughness for each case.

Spindle Speed (rpm) 950 1300 1600

Feed (mm/min) 150 400 600

Depth of Cut (mm) 0.25 0.75 1.25

Table 1: Input Parameters for CNC MillingMachining

Low Medium HighVariables

Levels

RESULTSBy controlling three input data (independentvariables) in this paper are spindle speed, feed

Figure 2: CNC Milling Machine

MACHINE TECHNICALSPECIFICATIONAxes: Longitudinal Traverse 250 mm

Cross Traverse 150 mm

Vertical Traverse 200 mm

Spindle Speed: 200 to 2500 rpm

Minimum increment: 0.005 mm

Tool Offset: 12 sets

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rate and depth of cut while actual surfaceroughness acted as output. 27 numbers ofexperimental readings are taken which areshown below.

Artif icial neural network has beenimplemented in this research work. Thepredicted surface roughness has beencalculated using Artificial Neural Network

(ANN) in MATLAB. Table shows the predictedsurface roughness using this method. Thenetwork propagates the input pattern fromlayer to layer until the output is generated. Thenthe result output will be compared with theactual surface roughness in this study. Nearestresults can be achieved by implementation of

950 150 0.25 1.961

950 150 0.75 1.95

950 150 1.25 1.942

950 400 0.25 3.145

950 400 0.75 3.141

950 400 1.25 3.09

950 600 0.25 4.02

950 600 0.75 4.01

950 600 1.25 4.016

1300 150 0.25 1.492

1300 150 0.75 1.491

1300 150 1.25 1.485

1300 400 0.25 2.675

1300 400 0.75 2.671

1300 400 1.25 2.658

1300 600 0.25 3.617

1300 600 0.75 3.611

1300 600 1.25 3.605

1600 150 0.25 1.112

1600 150 0.75 1.107

1600 150 1.25 1.092

1600 400 0.25 2.289

1600 400 0.75 2.284

1600 400 1.25 2.278

1600 600 0.25 3.23

1600 600 0.75 3.222

1600 600 1.25 3.216

Table 2: Experimental Result

SpindleSpeed Feed Depth of

CutSurface

Roughness

1.961 1.809 92.25

1.950 1.791 91.85

1.942 1.782 91.76

3.145 2.891 91.92

3.141 2.889 91.98

3.090 2.845 92.07

4.020 3.692 91.84

4.010 3.684 91.87

4.016 3.691 91.91

1.492 1.372 91.96

1.491 1.369 91.82

1.485 1.364 91.85

2.675 2.460 91.96

2.671 2.454 91.88

2.658 2.445 91.99

3.617 3.325 91.93

3.611 3.325 92.08

3.605 3.315 91.96

1.112 1.025 92.18

1.107 1.015 91.69

1.092 1.005 92.03

2.289 2.110 92.18

2.284 2.100 91.94

2.278 2.100 92.19

3.230 2.950 91.33

3.222 2.965 92.02

3.216 2.955 91.88

Table 3: Comparision BetweenExperimental Result and Theoretical

Result by ANN

Measured SurfaceRoughness

TheoreticalResult by ANN % Achieved

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ANN. surface roughness is calculated andpropagated back through network. Then, theweight will be changed and the same processrepeated until the smallest error is achieved.The plot of predicted surface roughness(output) against the actual surface roughness(target) is indicated in figure.

CONCLUSIONThe main purpose of this paper is to providean nearest result for predict surface roughnessin CNC end milling. The model developed isreliable to predict surface roughness withrespect to all previous research. Artificial neuralnetwork is a feasible technique in engineeringfield. This technique provides a brand newperspective in engineering field and in surfaceroughness prediction to be precise.

Back-propagation neural network had beenimplemented to prepare a predict model ofsurface roughness. From result, variousreading indicates in table which is to minimizethe error during surface roughness prediction.

The result of the prediction is favorable with8.06% average percentage of error, means91.94% accurate. So conclusion, ArtificialNeural Network (ANN) provided betteraccuracy to predict surface roughness in CNCmilling process.

REFERENCES1. Ab. Rashid M F F and Abdul Lani M R

(2010), “Surface Roughness Predictionfor CNC Milling Process Using ArtificialNeural Network”, Proceedings of theWorld Congress on Engineering, Vol. III,WCE, June 30-July 2, London, UK.

2. Ab. Rashid M F F, Gan S Y andMuhammad N Y (2009), “MathematicalModeling to Predict Surface Roughnessin CNC Milling”, World Academy ofScience, Engineering and Technology,Vol. 53, pp. 393-396.

3. Alwi Mohd A B M (2010), “Optimization ofSurface Roughness in Milling by UsingResponse Surface Method (RSM)”,

Figure 3: Comparision of Actual Surface Roughness and Theoretical Result by ANN

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Thesis of Bachelor of Mechanical withManufacturing Engineering, Faculty ofMechanical Engineering, UniversityMalaysia Pahang.

4. Ghani J A, Choudhury I A and Hassan H H(2004), “Application of Taguchi Method inthe Optimization of End MillingParameters”, Journal of MaterialsProcessing Technology, Vol. 145, No. 1,pp. 84-92.

5. Ginta T, Amin A K M Nurul, Radzi H C D Mand Lajis M A (2009), “Development ofSurface Roughness Models in End MillingTitanium Alloy Ti-6Al-4V Using UncoatedTungsten Carbide Inserts”, EuropeanJournal of Scientific Research, Vol. 28,No. 4, pp. 542-551.

6. Gopalsamy B M, Mondal B and Ghosh S(2009), “Taguchi Method and ANOVA: AnApproach for Process ParametersOptimization of Hard Machining WhileMachining Hardened Steel”, Journal of

Scientific and Industrial Research ,Vol. 68, pp. 686-695.

7. Moaz H Ali, Basim A Khidhir, Ansari M NM and Bashir Mohamed (2013), “FEM toPredict the Effect of Feed Rate onSurface Roughness with Cutting ForceDuring Face Milling of Titanium Alloy”,HBRC Journal.

8. Routara B C, Mohanty S D, Datta S,Bandyopadhyay A and Mahapatra S S(2010), “Optimization in CNC End Millingof UNS C34000 Medium Leaded Brasswith Multiple Surface RoughnessCharacteristics”, Sadhana, IndianAcademy of Sciences, Vol. 35, No. 5,pp. 619-629.

9. Wattanutchariya W and Pintasee B(2006), “Optimization of Metallic MillingParameters for Surface Finishing”,Proceedings of the 7 th Asia PacificIndustrial Engineering and ManagementSystems Conference, December 17-20,Bangkok, Thailand.

prediction of surface roughness in cnc milling … · 2015-04-09 · response surface methodology...

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