Int J Adv Manuf Technol (2016) 82:549–557 DOI 10.1007/s00170-015-7349-y
ORIGINAL ARTICLE
Investigation of material removal rate and surface roughness in wire electrical discharge machining process for cementation alloy steel using artificial neural network Saeid Shakeri 1 & Aazam Ghassemi 1 & Mohsen Hassani 1 & Alireza Hajian 2
Received: 6 February 2013 / Accepted: 19 May 2015 / Published online: 14 June 2015 # Springer-Verlag London 2015
Abstract Investigating the effect of process parameters on material removal rate and surface roughness is very important for process planing in wire electro-discharge machining. In this study, wire electro-discharge machining of cementation alloy steel 1.7131 is experimentally studied, then linear regression model and feedforward backpropagation neural network were established to predict surface roughness and material removal rate for effective machining. The full factorial experiment was chosen for experiments. Experiments were performed under different cutting conditions of pulse current, frequency of pulse, wire speed, and servo speed. The optimized neural network with the best performance for prediction had eight neurons in the hidden layer, capability with 0.773 % overall mean prediction error, while 2.547 % errors was revealed by regression model. Totally, the comparison of the results showed that the neural network is more robust with better accuracy.
Keywords Wire electro-discharge machining . Cementation alloy steel . Feedforward backpropagation neural networks . Linear regression
Nomenclature ANN Artificial neural network BPNN Backpropagation neural network F Frequency of pulse FFBPNN Feedforward backpropagation neural network MRR Material removal rate P Pulse current SR Surface roughness SS Servo speed WEDM Wire electro-discharge machining WS Wire speed
1 Introduction * Saeid Shakeri
[email protected] Aazam Ghassemi
[email protected] Mohsen Hassani
[email protected] Alireza Hajian
[email protected] 1
Department of mechanical engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
2
Department of Physics, Najafabad Branch, Islamic Azad University, Isfahan, Iran
Electro-discharge machining wire cutting (WEDM) is an essential operation in several manufacturing processes in industries such as aerospace, automotive, and precise molding to machine precise, complex, and irregular shapes in various difficult-to-machine electrically conductive materials [1, 2]. Several researchers have attempted to improve the performance characteristics, but solving the full potential utilization of the process is too difficult because of its complex process and large number of variable parameters. Surface roughness (SR) and material removal rate (MRR) are the most important parameters in manufacturing which can be optimized with
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choosing the exact controllable input parameters. Selecting the optimized parameters and decreasing the time of machining are the most important purposes of the researchers in this way, because the initial cost for purchase of WEDM machine is high. Sarkar et al. developed a feedforward backpropagation neural network (FFBPNN) to model WEDM process. A feedforward neural network algorithm by 6-15-3 network topology was adopted to model the process. The model was capable of predicting the response parameters as a function of six different control parameters, i.e., pulse on time, pulse of time, peak current, wire tension, dielectric flow rate, and performance parameters—cutting speed, surface roughness, and wire offset were considered. It was observed that the surface quality decrease as the cutting speed increased [3]. Kurikose and Shunmugam used a multi-regression model to represent relationship between input and output variables and multiobjective optimization method base on a non-dominated sorting genetic algorithm is used to optimize wire-EDM process [4]. Ramakrishnan and Karunamoorthy described the multi-objective optimization of the WEDM process using parametric design of Taguchi methodology. It was identified that the pulse on time and ignition current intensity has influence more than the other parameters [5]. Mahapatra and Patnaik developed relationships between various process parameters and responses like material removal rate, surface roughness and kerf by means of non-linear regression analysis and then employed genetic algorithm to optimize the WEDM process with multiple objectives [6]. Saha et al. developed a feedforward backpropagation neural network (FFBPNN) by 4-11-2 network topology to correlate the input process parameters, such as pulse on-time, pulse off time, peak current, and capacitance with the performance measures, namely cutting speed and surface roughness in wire electro-discharge machining (WEDM) of tungsten carbide-cobalt composite material [7]. Vamsi Krishna et al. employed Taguchi methodology involving an orthogonal array L27 to find out the main
Int J Adv Manuf Technol (2016) 82:549–557 Table 1
Range of input parameters used in this research
Input parameters
Level 1
Level 2
Level 3
Pulse current (A)
8
12
16
Frequency (KHZ)
40
50
60
Wire speed (m/min) Servo speed (mm/min)
8 4
10 8
12 –
parameters that affect the surface roughness value [8]. Roa and Ramji investigated the effect of various cutting parameters of WEDM on surface roughness using Taguchi methods base on L18 mixed orthogonal array. The best amount of surface roughness was 1.03 μm in experiments [9]. Somashkhar et al. reported the use of simulated annealing to optimize the performance characteristics of WEDM machining. The simulated annealing approach helps to optimize the wire-EDM process with multi-process responses. As a result of this research, the process response such as SR and MRR in WEDM process can be greatly improved [10]. In WEDM process, wire speed (WS) and servo speed (SS) parameters are more effective on surface roughness (SR) and for material removal rate (MRR), frequency (F), and pulse current (P) parameters are more important. Simultaneous study of these parameters (WS, SS, F, P) on SR and MRR for cementation alloy steel has been studied in this research for the first time. It is considerable that WS, SS, F, and P have nonlinear effect on SR and MRR, so investigation of artificial neural network model is very applicable. For this aim, full factorial experiment has been designed for machining of cementation alloy steel as a work piece by WEDM process. Linear regression has been established and also neural network using a backpropagation learning algorithm for the estimation of the performance characteristics, such as SR and MRR, has been developed with various machining parameters. 1.1 Experimental setup In this study, the cementation alloy steel was selected as a work piece. The experiments were performed by ROBOFIL 200 WEDM machine which is a CNC five-axis wire electrical Table 2
Fix parameters used in this research
Voltage Wire tension Upper dielectric pressure Bottom dielectric pressure Wire Dielectric Fig. 1 ROBOFIL 200 WEDM machine
120 V 1 kg 1.5 bar 1.8 bar Zinc Deionized water
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6 5.5 5 4.5 4 3.5 3 2.5
SRmicrometer)
SR(micrometer)
Int J Adv Manuf Technol (2016) 82:549–557
6
8
10
12
14
16
18
Pulse Current(A)
3.5 3.4 3.3 3.2 3.1 3 2.9 2.8 2.7 2.6 2.5
Fig. 2 Effect of pulse current on SR
35
40
45
50
55
60
65
Frequency(KHZ)
Fig. 4 Effect of frequency on SR
discharge machine (Fig. 1). The samples had 62 HRC hardness. Four important input machining parameters, P, F, WS, and SS have been selected (Table 1). The outputs parameters are SR and MRR. The experimental setup is as follows: zinc-coated brass wire 0.25-mm diameter as electrode and deionized water as fluid, wire tension, gap voltage, upper dielectric pressure, bottom dielectric pressure, and length of cut have been kept 1 kg, 210 V, 1.5 bar, 1.8 bar, and 40 mm, respectively (Table 2). Work piece dimensions were kept as 24.6-mm diameter and length of 40 mm for all experiments. Mean value of three measurements is used as a response value of surface roughness for any sample. For all experiments, the exact machining time of each work piece was measured, then the cutting speed was evaluated by dividing the length of cut with the exact time of cutting for each experiments. Material removable rate (MRR) was noted from the follow equation [11]: MRR ¼ : b: h
learning and 7 of them were used for the validation of the model.
2 Prediction of the model In the present work, a linear regression model was selected to predict the relationship between performance characteristics such as SR and MRR and input parameters, like pulse current, frequency of pulse, wire speed, and servo speed. According to this model, Eqs. (2) and (3) have been achieved. Mean−SR ¼ 0:126 þ 0:244*P þ 0:0330*F− 0:0572*WS− 0:0139*SS ð2Þ MRR ¼ − 5:24 þ 0:591*P þ 0:103*F þ 0:115*WS þ 0:0134*SS
ð1Þ
where VC =cutting speed in (mm/min), b=width of cut in (mm), and h=height of the work piece in (mm). In total, 54 experiments were applied that, 47 of them were considered as the training set for the designed neural network
The regression constants were computed with linear regression model with Minitab software. According to regression constants, pulse current and frequency of pulse have the most effect on surface roughness and material removal rate.
13 MRR(mm3/min)
MRR(mm3/min)
12 11 10 9 8 7 6
6
8
10
12
14
Pulse Current(A)
Fig. 3 Effect of pulse current on MRR
16
18
ð3Þ
6.9 6.7 6.5 6.3 6.1 5.9 5.7 5.5 5.3 5.1 4.9 4.7 4.5
35
40
45
50
55
Frequency(KHZ)
Fig. 5 Effect of frequency on MRR
60
65
Int J Adv Manuf Technol (2016) 82:549–557
3.55
3.3
3.5
3.29 SR(micrometer)
(micrometer)
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3.45 3.4 3.35 3.3 3.25
3.28 3.27 3.26 3.25 3.24
7
8
9
10
11
12
13
3.23
3
4
5
Fig. 6 Effect of wire speed on SR
Fig. 8 Effect of servo speed on SR
3 Effect of input parameters on the performance characteristics
4 Artificial neural network
MRR(mm3/min)
As a result of experiments, Figs. 2 and 3 show that the increase of pulse current will increase discharge energy in each pulse resulting in higher melting of cementation alloy steel. This increases the depth and diameter of discharge crater, raising the MRR, but decreasing the SR. Figures 4 and 5 show the increase of frequency of pulse will raise the MRR and will decrease the surface finish. According to the experiments, pulse current and frequency of pulse were the most significant machining parameters for SR and MRR. Increase of SR and MRR clearly followed the trend indication with increasing wire speed as a result of a rapid crossing of wire on the work piece and amount of energy available to melt and evaporate the material is low. Figures 6 and 7 show the effect of wire speed on SR and MRR. It was found that surface finish is good at high value of servo speed. Higher value of servo speed provides rapid removal of the eroded particle from the work piece surface which results in better surface finish (Fig. 8). Wire speed and servo speed are relative to each other, i.e., increase in amount of wire speed requires higher value of servo speed, to prevent to go to pieces of wire during the machining process. 6.82 6.8 6.78 6.76 6.74 6.72 6.7 6.68 6.66 6.64 6.62
7
8
9
10
11
6
7
8
12
In principle, artificial neural network (ANN) can compute any computable function, i.e., they can do everything a normal digital computer can do. Almost any mapping between vector spaces can be approximated to arbitrary precision by feedforward ANN. In practice, ANN is especially useful for classification and function approximation problems usually when rules such as those that might be used in an expert system cannot easily be applied. Neural computing requires a number of neurons, to be connected together into a neural network. Neurons are arranged in layers. Each neuron within the network is usually a simple processing unit which takes one or more inputs and produces an output. At each neuron, every input has an associated weight which modifies the strength of each input. The neuron simply adds together all the inputs and calculates an output to be passed on [12]. The structure of a neuron of an artificial neural network has been drawn in Fig. 9. ƒðp1 *w1 þ p2 *w2 þ p3 *w3 þ bÞ ¼ ƒ ∑pi *wi þ b ð4Þ
13
Wire speed(m/min)
Fig. 7 Effect of wire speed on MRR
9
Servo Speed(m/min)
Wire speed(m/min)
Fig. 9 Structure of a neuron of an artificial neural network
Int J Adv Manuf Technol (2016) 82:549–557 Table 3 Datasets selected for training of the neural networks
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experiment
P (A)
F (KHZ)
Ws (m/min)
Ss (mm/min)
Mean-SR (μm)
MRR (mm3/min)
1
8
40
8
4
2.87
4.74
2 3
8 8
40 40
8 10
8 4
2.81 2.77
4.69 4.87
4
8
40
10
8
2.73
4.9
5
8
40
12
8
2.64
5.19
6 7
8 8
50 50
8 8
4 8
3.21 3.16
5.65 5.57
8
8
50
10
8
3.03
5.88
9 10
8 8
50 50
12 12
4 8
2.96 2.92
5.94 6.09
11 12
8 8
60 60
8 8
4 8
3.53 3.46
6.65 6.54
13
8
60
10
8
3.35
6.76
14 15
8 8
60 60
12 12
4 8
3.29 3.24
6.8 6.86
16 17 18
12 12 12
40 40 40
8 8 10
4 8 4
3.89 3.83 3.78
6.94 6.89 6.06
19 20 21 22 23
12 12 12 12 12
40 40 50 50 50
12 12 8 8 10
4 8 4 8 4
3.66 3.6 4.21 4.16 4.11
7.21 7.29 8.48 8.41 8.6
24 25 26 27
12 12 12 12
50 50 50 60
10 12 12 8
8 4 8 8
4.06 4.02 3.96 4.46
8.73 8.83 8.97 9.11
28 29 30 31 32
12 12 12 12 16
60 60 60 60 40
10 10 12 12 8
4 8 4 8 4
4.41 4.35 4.3 4.24 4.78
9.27 9.35 9.39 9.44 9.48
33 34 35 36 37 38 39 40 41
16 16 16 16 16 16 16 16 16
40 40 40 40 40 50 50 50 50
8 10 10 12 12 8 10 10 12
8 4 8 4 8 4 4 8 4
4.75 4.71 4.66 4.61 4.57 5.16 5.02 4.95 4.87
9.46 9.57 9.66 9.72 9.79 10.12 10.34 10.39 10.47
42 43 44 45 46 47
16 16 16 16 16 16
50 60 60 60 60 60
12 8 8 10 12 12
8 4 8 4 4 8
4.82 5.57 5.49 5.41 5.28 5.22
10.56 11.39 10.27 11.83 12.18 12.27
Int J Adv Manuf Technol (2016) 82:549–557
where pi is input, wi is weight, b is bias, and ƒ is transfer function.
5 Feedforward backpropagation algorithm A set of examples for training the network is assembled. Each case consists of a problem statement (which represents the input into the network) and the corresponding solution (which represents the desired output from the network). The input data is entered into the network via the input layer. Each neuron in the network processes the input data with the resultant values steadily “percolating” through the network, layer by layer, until a result is generated by the output layer. The actual output of the network is compared to expected output for that particular input. These result in an error value. The connection weights in the network are gradually adjusted, working backwards from the output layer, through the hidden layer, and to the input layer, until the correct output is produced. Fine tuning the weights in this way has the effect of teaching the network how to produce the correct output for a particular input, i.e., the network learns. In this study, a feedforward backpropagation algorithm by 4-8-2 network topology for the estimation of SR and MRR has been presented. Four neurons in input layer, eight neurons in hidden layer, and two neurons in output layer have been used in this model. Table 3 shows the data set that have been used for training of network. Data set have been normalized between 0 and 1 for training the networks. The best architecture has been selected by varying the number of neurons in the hidden layer. Figure 10 shows the variation of overall mean prediction error with the number of neurons in hidden layer. The typical neural network architecture has been shown in Fig. 11. Here, tan-sigmoid and Levenberg-Marquardt algorithm are used as transfer function and training algorithm, respectively. Tan-sigmoid equation is as follows:
Overall mean pridiction erorr(%)
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11 10 9 8 7 6 5 4 3 2 1 0
2 3 4 5 6 7 8 9 10 11 12 13 Number of neurons in the hidden layer
Fig. 10 Variation of overall mean prediction error with number of neurons in the hidden layer
where m=number of test pattern, Ti =experimental result of ith testing pattern, and Yi =predicted result of ith testing pattern. Experimental, regression, and backpropagation neural network results are compared which are illustrated in Figs. 12, 13, 14, 15, 16, 17, 18, and 19 (red = feedforward backpropagation, black=experimental data, green =linear regression). According Figs. 12, 13, 14, and 15, input parameters influence order on MRR is as follows: pulse current>frequency> wire speed>servo speed. So pulse current has the maximum effect on MRR, and servo speed has the minimum effect. Figures 3, 5, and 7 show that by increasing pulse current, frequency, wire speed, and servo speed up to 50 % results MRR increasing up to 40, 35, 2.25, and 0.8 %, respectively. Also Figs. 16, 17, 18, and 19 show that input parameters influence the order on SR is as follows: pulse current>frequency>wire speed>servo speed. According Figs. 2 and 4, decreasing the pulse current and frequency up to 50 %
P
ƒðzÞ¼
2 ð1þe−2z Þ−1
ð5Þ
In Table 4, seven experiments (data set) are predicted with linear regression and feedforward backpropagation (BPNN). 4-8-2 neural network architecture provides the best prediction capability with 0.773 % overall mean prediction error, while 2.547 % error was revealed by regression model. The mean prediction error was calculated using the following expression [13]:
F
SR
Ws
MRR
Ss
Input Layer
1 X m jTi −Yi j Mean prediction error%¼ 100 i¼1 Ti m
ð6Þ
Hidden Layer
Output Layer
Fig. 11 Backpropagation neural network with eight neurons in the hidden layer, used for SR and MRR prediction
Int J Adv Manuf Technol (2016) 82:549–557
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Table 4 Comparison of experimental result with the ANN and regression model, P (ampere), F (KHZ), Ws (m/min), SS (mm/min), SR (μs), and MRR (mm3/min) N
P
F
Ws
SS
SR
MRR
SR (ANN)
MRR (ANN)
SR (REG)
MRR (REG)
SR errors (ANN)
1
8
40
12
4
2.69
5.07
2.72
4.92
2.656
5.0416
1.115
2.958
1.264
0.560
2 3
8 8
50 60
10 10
4 4
3.09 3.41
5.72 6.74
3.08 3.40
5.73 6.70
3.1004 3.4304
5.8416 6.8716
0.323 0.293
0.174 0.593
0.337 0.598
2.126 1.953
4 5
12 12
40 60
10 8
8 4
3.71 4.51
6.12 9.16
3.64 4.58
6.11 9.18
3.6908 4.5208
7.2292 9.0056
1.886 1.552
0.163 0.218
0.518 0.24
18.124 1.685
6
16
50
8
8
5.09
9.85
5.1
9.81
5.1112
10.393
0.196
0.406
0.417
5.515
7 16 60 10 8 Mean prediction error%
5.34
11.9
5.35
11.99
5.3268
11.6532
0.187 0.793
0.756 0.753
0.274 0.521
2.074 4.573
Over all mean prediction error%
0.773
SR errors (REG)
6 Conclusion In this study, the effect of process parameters such as pulse current, frequency of pulse, wire speed, and servo speed have been studied on material removal rate and surface roughness. For this, 54 experiments have been done. According to the experimental results, increase of pulse current and frequency raises the MRR and roughness of the surface. The increase of wire speed results increasing the MRR and decreasing the SR parameter. It was found that surface finish is good at high value of servo speed and higher value of servo speed provides rapid removal of the eroded particle from the work piece. In order to prediction of process parameters, artificial neural network and linear regression have been used. The neural network topology, 4-8-2, and tan-sigmoid transfer function provide the best prediction capability with 0.773 % overall mean prediction error, while 2.547 % errors was revealed by regression model. Though the proposed regression model is adequate and accepted, BPNN yields better prediction. It is
MRR (
/
) MRR (
Pulse current (A) Fig. 12 Effect of pulse current on MRR
MRR errors (REG)
2.547
)
decreases roughness of surface up to 30 and 22 %, respectively. Also, Figs. 6 and 8 show that by increasing wire speed and servo speed up to 50 %, roughness of surface increases up to 7 and 1.5 % respectively. So it can be seen that increasing pulse current and frequency increases the amount of MRR and decreasing them results to smoother surface. Therefore, an optimum range can be found for frequency and pulse current parameters to improve roughness of surface and MRR simultaneously. On the other hand, increasing wire speed and servo speed (until machine limitation) causes MRR increasing and SR improvement. Comparison of experimental tests with regression and artificil neural network (ANN) models is illustrated in Figs. 12, 13, 14, 15, 16, 17, 18 and 19. It can be seen that ANN model is more coincident with experimental test. It is notable that ANN is a non-model base method that has high ability in simulation of nonlinear processes. So using ANN for prediction the effect of input parameters on outputs, in WEDM process, is acceptable.
MRR errors (ANN)
Servo Speed (mm/min) Fig. 13 Effect of servo speed on MRR
Int J Adv Manuf Technol (2016) 82:549–557
MRR (
SR (
/
)
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Frequuency (KHZ)
Fig. 17 Effect of frequency on SR
MRR (
SR (
/
)
Fig. 14 Effect of frequency on MRR
Frequency (KKHZ)
Wire Speed (m/min)
Fig. 15 Effect of wire speed on MRR
Fig. 18 Effect of wire speed on SR
SR (
SR (
Wire Speed (m/min)
Pulse current (A) Fig. 16 Effect of pulse current on SR
Serrvo Speed (mmm/min) Fig. 19 Effect of servo speed on SR
Int J Adv Manuf Technol (2016) 82:549–557
observed that the regression model is quite comparable to BPNN for surface roughness prediction. Totally, the comparison of the results showed that the neural network is more robust with better accuracy. Optimization of process can be done using genetic algorithm for future work.
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7.
8.
References 1.
2. 3.
4.
5.
Rajarshi M, Shankar C, Suman S (2012) Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms. Appl Soft Comput 12(8):2506–2516 Jain V (2005) Advanced machining processes. Allied Publishers Pvt. Limited, New Delhi Sarkar S, Mitra S, Bhattacharyya B (2005) Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminized alloy. J Mater Proc Tech 159:286–294 Shajan K, Shunmugam M (2005) Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. J Mater Proc Tech 170:133–141 Ramakrishnan R, Karunamoorthy L (2006) Multi response optimization of wire EDM operations using robust design of experiments. Int J Adv Manuf Technol 29:105–112
9.
10.
11.
12.
13.
Mahapatra S, Amar P (2007) Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. Int J Adv Manuf Technol 34:911–925 Probir S, Abhijit S, Surjya K (2008) Soft computing models based prediction of cutting speed and surface roughness in wire electrodischarge machining of tungsten carbide cobalt composite. Int J Adv Manuf Technol 39:74–84 Vamsi K (2010) Optimizing surface finish in WEDM using the Taguchi parameter design method. J Braz Soc Mech Sci Eng. doi: 10.1590/S1678-58782010000200002 Pujari S, Koona R (2011) Effect of WEDM conditions on surface roughness: a parametric optimization using Taguchi method. Int J Adv Eng Sci Tech 6(1):041–048 Somashekhar K, Jose M, Ramachandran N (2012) A feasibility approach by simulated annealing on optimization of micro-wire electric discharge machining parameters. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4096-1 Aniza A, Bulan A, Norliana M (2012) WEDM: influence of machine feed rate in machining titanium Ti-6Al-4V using brass wire and constant current (4A). Procedia Eng 41:1812–1817 Debabrata M, Surjya K, Partha S (2007) Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. J Mater Proc Tech 186:154–162 Aykut C, Sukru O, Temel V (2012) Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks. Powder Tech 228:26–35
