 Open Access
 Total Downloads : 924
 Authors : Anuj Kumar Sehgal, Meenu
 Paper ID : IJERTV2IS3745
 Volume & Issue : Volume 02, Issue 03 (March 2013)
 Published (First Online): 03042013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Optimized Prediction and Modeling Under End Milling Machining By ANOVA And Artificial Neural Network
Anuj Kumar Sehgal, Meenu
Department of Mechanical Engineering, National Institute of Technology.
Kurukshetra, Haryana, India
Abstract
. In recent times, the need for characterization of surface roughness in an end milling machining process is essential. Accordingly a better approach can provide accurate machining for post casting of rolled Aluminium which often requires better dimensional tolerances. In present work the study for development of surface roughness model for rolled Aluminium is described. Experimental data is collected as per L18 orthogonal array with three levels defined for each of the factor by design of experiment approach. Analysis of variance (ANOVA) is used to test the adequacy of the developed mathematical model. Further, ANN analysis with multilayer feed forward perceptron structure using graphical user interface (GUI) under MATLAB is adopted with the experimental values as inputoutput pairs. Back Propagation algorithm using the input cutting conditions viz. Spindle Speed, Feed rate and Depth of Cut is being constructed and the surface roughness of the machined component is been taken as output response. The effect of feed rate is found to be the highest influence on surface roughness followed by the cutting speed and depth of cut. The result revealed the close correlation between the measured values and the model output in much lesser time and with a high accuracy.
Keywords: Dimensional tolerances, Machining, Surface Roughness, ANOVA, Artificial Neural Network

Introduction
To meet the need of precise dimensional tolerances through process planning, thus to achieve maximum productivity, an optimal metal removal process must be designed. The basic approach identified earlier, in order to obtain better surface finish, is the selection of proper cutting parameters before the actual machining takes place. The design specification for considering the material properties like fatigue strength, wear resistance, reliability of performance for any material undoubtedly, is surface quality and thus an important factor in evaluation of performance index for a machine tool. With the application of industrial development computer aided manufacturing (CAM) associated with CNC (Computer Numeric Control) has widely been accepted to result more accurate machining data and provide the most optimum production .The increase in utilization of machinability data systems for the selection of optimum cutting parameters has attracted the machinists for the machining processes such as milling. The most common machining operations in industries are milling operations, which is generally used for finishing, material removal processes. Many researchers have studied the effects of optimal selection of machining parameters of end milling which requires the simultaneous considerations of multiple objectives,
including maximum tool life, desired surface roughness of the machined surface, metal removal rate etc. The investigations by El Bardie discussed the development of surface roughness prediction models for turning EN 24T steel and adopted the most conventional approach namely D.O.E in metal cutting which includes the experiment observations conclusion strategy.
Yang J, attempted the two purposes in the research. The first was to demonstrate a systematic procedure of using Taguchi parameter design in process control of individual milling machines. The second was to demonstrate a use of the Taguchi parameter design in order to identify the optimum surface roughness performance with a particular combination of cutting parameters in an end milling operation. Indrajit M successfully applied the RSM, a tool under design of experiment for prediction and optimization of cutting parameters. Yang Y K, applied the Taguchi method and the analysis of variance (ANOVA) approach to optimize parameters i.e. groove difference and the roughness average at the bottom plane of the inside groove in end milling for highpurity graphite under dry machining. It is mapped to achieve the objective of maximization or minimization of the output properties. The theoretical surface roughness in end milling operations is dependent on the various parameters viz. cutting conditions, workpiece material, cutting tools etc. The D.O.E indicates the series of steps for the experiment planning for better understanding of product or process performance. In order to develop the techniques to predict the surface roughness of a product before the actual machining the evaluation of robustness of machining parameters such as feed rate, spindle speed, depth of cut etc. must be evaluated. Thus the best suited prediction technique must be employed for keeping the desired surface roughness and high product quality.
Hence, by this study an attempt has been made to develop a model to predict surface roughness in end milling of rolled aluminum, which is commercially available and known for the multiple uses in domestic as well as high precise industrial use. The research may be summarized by its purpose, as illustrated below:

To study the effect of machining parameters on the surface roughness of the machined product.

To develop the surface prediction model by multiple regression technique.

To prepare the surface roughness model further by employing Artificial neural network and compare with the actual surface roughness achieved earlier,

To provide a solution of optimization problem for the selection of the best control setting to reduce the surface roughness.


Experimental set up & Procedure

Experimental Design
The factors whose effects were studied are depth of cut (inch), cutting speed/spindle speed (rpm), feed rate (inch/min.) on the quality characteristic surface roughness (Ra) in an end milling operation. With the involvement of 2 or more factors the response affect may be interacted; so for considering the effect the possible factor level combinations has been considered. The procedure adopted for preparing a model of the process is as illustrated:

Selection of factors & their levels in the process

Conducting the experiments at possible factor level combinations randomly

Analyze the data by ANOVA

Building of Ist order multiple regression model

Validating of the model
For the investigation to be carried out and in order to reduce the number of experiments to a practical level,
partial factorial design with L18 orthogonal array is proposed.


Experimental Procedure
The set up employed a VMC (Vertical Milling Centre), two 4flute and 80 mm diameter SGS48554 cemented carbide end mill cutters. The machining performed in dry environment. (Table 4).
The cutting process parameters and their levels selected are as shown in table 1.
Process Parameters (units)
Levels
1
2
3
Depth of cut (inch)
0.02
0.038
0.046
Cutting Speed (rpm)
2500
3800
5000
Feed Rate (inch/mm)
2.0
3.5
5.0
Process Parameters (units)
Levels
1
2
3
Depth of cut (inch)
0.02
0.038
0.046
Cutting Speed (rpm)
2500
3800
5000
Feed Rate (inch/mm)
2.0
3.5
5.0
Table 1: Cutting Process Parameters and their Levels
The machining specimen is made at 18 different factor level combinations as representing the conduction of 18 experiments in table 3. The dimension of work material is taken as 80 mm x 75 mm x 15 mm. The specification of the work material is as follows (Table 2).
Particulars
Value
Material
Rolled Aluminium
Elastic limit
48.1 MN/m2
Modulus of elasticity
66.7 MN/m2
Modulus or resilience
0.280 MJ/mm2
Izod No. (Impact Strength)
7.9
Hardness (BHN)
250
Weight
230 gm.
Particulars
Value
Material
Rolled Aluminium
Elastic limit
48.1 MN/m2
Modulus of elasticity
66.7 MN/m2
Modulus or resilience
0.280 MJ/mm2
Izod No. (Impact Strength)
7.9
Hardness (BHN)
250
Weight
230 gm.
Table 2: Specification of Work Material
Table 3: Process Parameters & their Levels
Exp. No.
Column No.
1
Depth of cut (inch)
2
Cutting speed (rpm)
3
Feed Rate (inch/mm)
4
Avg. Surface roughness, Ra (Âµinch)
1
0.02
2500
2.0
80.3
2
0.02
5000
3.5
111.7
3
0.038
3800
5.0
76.0
4
0.046
2500
2.0
39.3
5
0.046
5000
3.5
61.0
6
0.02
3800
3.5
65.3
7
0.038
2500
5.0
69.3
8
0.038
5000
3.5
93.67
9
0.046
3800
5.0
22.3
10
0.02
2500
2.0
137.67
11
0.02
5000
5.0
42.0
12
0.038
3800
2.0
58.0
13
0.046
2500
3.5
62.7
14
0.046
5000
3.5
72.0
15
0.02
3800
5.0
31.3
16
0.038
2500
2.0
88.3
17
0.038
5000
5.0
40.0
18
0.046
3800
3.5
48.3
The experimental conditions are given in table 4 as follows.
Table 4: Experimental Conditions
Particulars
Experimental conditions
Machine
Vertical milling center, AGNI BMV
45. MODEL No. 4000SPE
Tool
4 flute, 8.0 diameter, SGS 48554, Cemented carbide end mill cutter
Collet
Size 89 mm
Cutting parameters
Depth of cut, Cutting speed, Feed rate
Profilometer, Federal
Pocketsurf3
Surface roughness tester
Piezoelectric contact type stylus, Stylus travel: 0.1 inch/2.54 mm, Maximum stylus force: 15 MN,
Measuring capacity: Ra or Rmax/Ry
or Rz
Coolant
Dry machining
The surface roughness was measured at three different spaced locations along the length of the specimen and the average was taken as response values. The mean response values for experimental data are shown in table 5 as given below.
Levels
Depth of cut
(inch)
Cutting speed (rpm)
Feed rate
(inch/mm)
1
84.28
2.0
80.3
2
55.27
3.5
111.7
3
60.32
5.0
76.0
Maxmin ()
29.01
2.0
39.3
Rank
3
2
1
Levels
Depth of cut
(inch)
Cutting speed (rpm)
Feed rate
(inch/mm)
1
84.28
2.0
80.3
2
55.27
3.5
111.7
3
60.32
5.0
76.0
Maxmin ()
29.01
2.0
39.3
Rank
3
2
1
Table 5: Mean response Table for Experimental Data
The result of the analysis of variance of surface roughness is shown in table no. 6 as given below.
Table 6: ANOVA Table for Surface Roughness
Factor
Cutting parameter
D O F
Sum of squares
Mean square
Ftest
A
Depth of cut (inch)
2
36.669
18.335
12.223
B
Cutting speed (rpm)
2
56.018
28.009
18.673
C
Feed rate (inch/mm)
2
160.485
80.243
53.495
Error
–
47
70.52
1.50
–
Total
–
53
323.694
–
–

Analysis of Variance
The purpose of the analysis of variance (ANOVA) is to determine which cutting parameter significantly affects the quality characteristic (Ra). Being an objective decision making tool it can provide a measure of average performance of group of items under test. This does not analyze the data directly, but rather determine the value ability (variance) of the data. The Ftest (Frequency test) a statistical measure to analyzethe significant effect of the parameters expressing the quality characteristics table 7 shows the result of ANOVA analysis of raw data for surface roughness for a level of significance for 95 % level of confidence. From the table 7, it is apparent that the Fratio values of factor A (depth of cut), Factor B (cutting speed) and factor C (feed rate) are all greater than F0.05,2,45 = 3.2. Thus have statistical physical significance as the response variable (surface roughness).
Table 7: Mean Response Table for S/N ratio
Levels
Depth of cut
(inch)
Cutting speed (rpm)
Feed rate (inch/mm)
1
37.94
37.492
32.26
2
34.55
37.12
36.477
3
35.505
33.38
39.54
Maxmin ()
3.39
4.11
7.28
Rank
3
2
1
The mean response and the mean signal to noise ratio for the depth of cut, cutting speed & feed rate are represented graphically & tabulated in Fig. 2 & table 5 and table 6. The optimal cutting parameters for surface roughness are A2B3C1. The predicted surface roughness is obtained by the following equation:
Âµ = Âµ + (ÂµA – Âµ) + (ÂµB – Âµ) + (ÂµC – Âµ) (1)
where, Âµ = Mean Surface Roughness
ÂµA = Main effect due to A
ÂµB = Main effect due to B
ÂµC = Main effect due to C
Figure 1: Experimental and data aquisition setup
Table 8: Final Confirmation Experiment
A2B3C1D2
Ra (Âµin.)
S/N Ratio
Exp. No.
R1
R2
R3
N1
1*
17
21
22
20.0
25.92
2
20
22
25
22.3
27.016
N2
1*
17
21
22
20.0
25.92
2
15
17
24
18.67
25.60


Artificial Neural Network (ANN)
Among the most appropriate tools in artificial intelligence and widely accepted in all fields of engineering, modeling and optimization is ANN. ANN is used to predict the linear and nonlinear problems in machining field. Davim J P [12], developed and investigated the predicted models for Surface roughness using artificial neural network (EBPTA) during turning of free machining steel, 9SMnPb28k (DIN). Zain A.M [13,15] discussed the concept, application, abilities and limitations of Artificial Neural Network in the machining process modeling. The comparison been attempted between the conventional approaches such as statistical regression technique, explicit models with non conventional approaches or Artificial Intelligence techniques such as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm based modeling. Muthukrishnan N, J. Paulo Davim [14], studied the surface roughness of AlSiC (20 p) composite bars in turning operation using coarse grade polycrystalline diamond (PCD) insert under different cutting conditions. Experimental data collected, tested with ANOVA and artificial neural network techniques. Multilayer perceptron model has been constructed with backpropagation algorithm using the input parameters of depth of cut, cutting speed and feed. Output
parameter is surface finish of the machined component. As development of mathematical models are required for the prediction of decision variable and responses in decision making processes for industries[16], in this research work the attempt is made to model the machining parameters using artificial neural network. The developed model for the milling process derives the relationship between the input parameter and the output parameter in terms of mathematical equation. It is a multi layer feed forward network using gradient
descent based delta learning rule, commonly known as back propagation rule. Being a gradient descent method, it minimizes the total squared error of the output computed by the net. The network is trained by supervised learning method. The network achieves a balance between the ability to respond correctly to the input patterns that are used for training and the ability to provide good responses to the input.
2
2
Artificial neural networks are mathematical models of biological neural systems. It learns from examples
function updates the weight and bias values according to Levenberg Marquardt algorithm. It is one of the best ways to improve generalization performance of network for function approximation problems. This is because it does not require that a validation data set be separated out of the training data set. It uses all of the data. This advantage is especially noticeable when the size of data set is small. It minimizes a linear combination of squared errors and weights and then determines the correct combination to produce network that generalizes well. The number of neurons in hidden layer [11], was determined by trial and error. The multilayer feed forward ANN consisted of neurons divided into input layer, output layer and hidden layers. The neurons between the layers are connected by the links having synaptic weights. The error backpropagation training algorithm is based on weight updates so as to minimize the sum of squared error for Knumber of output neurons, given as
through iteration, without requiring a priori knowledge of relationships between variables under investigation. Each example includes both inputs (information used to
E 1
2
K
K
(d
k 1
k . p okp )
(2)
make a decision) and patterns (prediction or responses). ANN tries each example in turn using the inputs to
Where, dk,p = desired output for the pth pattern. The weights of the links are updated as
calculate answers which it compares to provide pattern. If it is wrong, ANN corrects the network by making
wji(n1) w
ji(n)

pjopi wji(n)
(3)
changes to internal connections (weights). The trial and error process continues until the network outputs are in good agreement with patterns to a certain specified level of accuracy.
In ANN modeling, MATLAB software was used for
where n is the learning step, is the learning rate, is the momentum constant and pj is error for output or input layer as,
pk= (dkpokp)(1okp), k=1,2,.K (4)
training and validating of neural network models.
= o
(1o )
w , j=1,2J (5)
Multiple input and multiple output (MIMO) network
pj pj
pj
pk kj
models were developed for objective parameters and for sensory attributes. Standard Bayesian regularization back propagation training algorithm, trainbr function, was used for training the network. This training
where, j is the number of neurons in the hidden layer. The training process is initialized by assigning small random weight values to all the links. The inputoutput patterns are presented one by one and updating the
weights each time. The mean square error (MSE) at the end of each epoch due to all patterns is computed as
1 P K 2
NP
NP
MSE (dkp okp )
(6)
p1 k 1
Where, NP = number of training patterns. The training process will be terminated when the specified goal of MSE or maximum number of epochs is achieved.

ANN Training
The training of ANN for the 18 inputs and output patterns has been used under MATLAB software. The multilayered feed forward ANN with 3 inputs (depth of cut, spindle speed, feed rate) and 1 output (Ra) is employed. The training simulations with following details are as under table 9. For each input pattern, the predicted value of Ra is compared with the respective experimental measured value and the %ge absolute error is measured as equation 7.
Figure 2: The variation of MSE with number of epochs

Model Verification
The training of the artificial neural network with different nodes in the hidden layer found to be 20.The performance of ANN after taking account of all the training and testing pattern is 11.38.Thus proven to be a significant A.I tool in order to predict the surface roughness in end milling machining process. The Fig. 5 shows the graph plotted using
yi.exp yi. pred
(7)
predicted and tested values for surface roughness.
%ge AbsoluteError
yi.exp
100
The correlation between the outputs and targets measures the variation in the outputs that may be
Particulars
Value
Number of nodes in input layer
3
Number of nodes in hidden layer
20
Number of nodes in output layer
1
Learning rate chosen
0.05
Momentum
0.9
Goal (MSE)
1e05
Number of epochs
10000
Particulars
Value
Number of nodes in input layer
3
Number of nodes in hidden layer
20
Number of nodes in output layer
1
Learning rate chosen
0.05
Momentum
0.9
Goal (MSE)
1e05
Number of epochs
10000
Table 9: Factors assumed to train the Network
related with targets. The correlation coefficient (RC value) for the output (Ra) is found to be 0.98, nearly equal to 1, which indicates the very good correlation. Further the validation of ANN model for the output (surface roughness, Ra) is performed with the data set of actual experimental values and the predicted surface roughness values in Fig. 4. The trend reveals that the predicted values following the actual values observed in experimental trials.
Figure 3: Correlation of the Training Pattern for Ra
Figure 4: Validation of ANN Model for Surface Roughness


Conclusions
The 18 experiments were conducted on a VMC using cemented carbide end mill cutter; the surface roughness values were obtained with various factorlevel combinations. The two modeling techniques, viz. ANOVA and ANN were explored for the prediction of surface roughness in an end milling machining. The outcome of the calculations and analysis by the ANOVA has depicted that the effect of feed rate represents the highest influence on surface roughness followed by the cutting speed and depth of cut. From the (Table 3) Average (Mean) Effect Response Table for the raw data, the factor (C) Feed rate represents the largest influence on surface roughness followed by factor (B) Cutting Speed, factor (A) Depth of Cut and finally factor (D) Type of tool which is further verified
with the (table 4) Average Effect Response Table for S/N Ratio. Further, ANN with configuration of 18 patterns was trained. The result revealed the close correlation between the measured values and the model output in much lesser time and with a high accuracy. Finally it is concluded that ANN is a powerful tool for the modeling of linear and nonlinear relationship between the process response (surface roughness) and input parameters problems. Thus modeling using ANN is proven to be an effective method for the prediction of experimental patterns.

References

Benardos, P.G., Vosniakos, G.C., (2002), Prediction of surface roughness in CNC face milling using neural networks and Taguchis design of experiments, Robotics and Computer Integrated Manufacturing 18, pp 343354.

Benardos, P.G., Vosniakos, G.C., (2003), Predicting surface roughness in machining: a review, Int. J. Mach. Tools Manuf. 43, pp 833844.

Alauddin,M, M.A. ElBaradie, M.S.J. Hashmi, (1995), Computeraided analysis of a surface roughness model for end milling, Journal of Materials Processing Technology 55, pp 123127.

Mansour.A., H. Abdalla, (2002) Surface roughness model for end milling: a semifree cutting carbon casehardening steel (EN32) in dry condition, Journal of Materials Processing Technology, Vol.124, pp. 183191.

Hayajneh,M.T, Montasser,S.T., Joachim,B., (2007), A Study of the Effects of Machining Parameters on the Surface Roughness in the EndMilling Process, Journal of Mechanical and Industrial Engineering 1,No. 1, pp 15.

Sanjit Moshat, Saurav Datta, Asish Bandyopadhyay and Pradip Kumar Pal, (20100, Optimization of CNC end milling process parameters using PCAbased Taguchi method, International Journal of Engineering, Science and Technology, Vol. 2, No. 1, pp 92102.

I.A. Choudhury, M.A. ElBaradie, (1997), Surface roughness in the turning of highstrength steel by factorial design of experiments, Journal of Materials Processing Technology 67, pp 5556.

Yang J. and Chen J., (2001), A Systematic Approach for identifying Optimum Surface Roughness Performance in End Milling Operations, Journal of Industrial Technology, Vol. 17, No.2.

Indrajit M, Pradip K R, (2006), A review of optimization techniques in metal cutting processes, Computers & Industrial Engineering 50, pp 1534.

Yang, Y K, Chuang, M,T and Lin, S.S., (2009), Optimization of dry machining parameters for highpurity graphite in end milling process via design of experiments methods, Journal of Materials Processing Technology, Vol. 209, pp. 43954400.

Datta S., Nandi G., Bandyopadhyay A. and Pal P.K., (2010), Optimization of CNC end milling process parameters using PCAbased Taguchi method, Int. J. of Engineering, Science and Technology 2, No. 1, pp 92102.

Davim J. P., V.N. Gaitondeb, S.R. Karnik, (2008), Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models, Journal of materials processing technology, Vol. 205, pp. 1623.

Zain,A M, Habibollah H, Safian S, (2009) "Application of Regression and ANN Techniques for Modeling of the Surface Roughness in End Milling Machining Process," Asia International Conference on Modelling & Simulation, pp. 188193, Third Asia International Conference on Modelling & Simulation.

N. Muthukrishnan, J. P Davim, (2009). Optimization of machining parameters of Al/SiCMMC with ANOVA and ANN analysis, journal of materials processing technology, Vol. 209, pp. 225 232.

Zain,A M, Habibollah H, Safian S, (2010), Prediction of surface roughness in the end milling machining using Artificial Neural Network, Journal Expert Systems with
Applications: An International Journal archive , Volume 37 Issue 2.

ALZuberi, S., Zaharah. A.G, Che. H, C, H, (2011), Application of ANN in Milling process: A review, Modelling & Simulation in Engineering, Vol. 2011, ID: 696275, pp 17.

Amir Mahyar Khorasani, Mohammad Reza Soleymani Yazdi, Mir Saeed Safizadeh, (2011), Tool Life Prediction in Face Milling Machining of 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experimet (DOE), IACSIT International Journal of Engineering and Technology, Vol.3, No.1.

T. B. Asafa, D. A. Fadare, (2012), Artificial neural network predictive modeling of uncoated carbide tool wear when turning NST 37.2 steel, ARPN Journal of Engineering and Applied Sciences, Vol. 7, No. 4.

SouÃ¢d Makhfi , RaphaÃ«l Velasco , Malek Habak , Kamel Haddouche, Pascal Vantomme,(2013), An Optimized ANN Approach for Cutting Forces Prediction in AISI 52100 Bearing Steel Hard Turning, Science and Technology pISSN: 21632669, eISSN: 21632677, 3(1): 2432.