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Int. J. Machining and Machinability of Materials, Vol. 18, Nos. 1/2, 2016
Influence of process parameters on material removal rate and surface roughness in WED-machining of Ti50Ni40Cu10 shape memory alloy M. Manjaiah* and S. Narendranath Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal-575025, Karnataka, India Email:
[email protected] Email:
[email protected] *Corresponding author
S. Basavarajappa Department of Studies in Mechanical Engineering, University B.D.T. College of Engineering, Davangere-577 004, Karnataka, India Email:
[email protected]
V.N. Gaitonde Department of Industrial and Production Engineering, B.V.B. College of Engineering and Technology, Hubli-580 031, Karnataka, India Email:
[email protected] Abstract: Among the shape memory alloys (SMAs), TiNi SMAs have been typically used as the functional elements in the larger part of the industries due to exceptional properties like super elasticity and shape memory effect. However, traditional machining of these alloys is fairly complex due to these properties. The non-traditional machining process like electric discharge machining (EDM) exhibits outstanding capability in machining of these alloys. The poor selection of machining parameters may cause increased roughness of workpiece and lesser material removal rate. Hence, an effort has been made in the present work to explore the effects of three process parameters, such as pulse on time, pulse off time and servo voltage in wire electric discharge machining (WEDM) of Ti50Ni40Cu10 shape memory alloy (SMA) using zinc coated brass wire electrode on material removal rate and surface roughness using response surface methodology (RSM)-based mathematical models. The experiments were planned as per central composite design (CCD). The investigations revealed that pulse on time and servo voltage have predominant effects in maximising material removal rate and minimising surface roughness. The best combination of the process parameters for multi-response optimisation was obtained through desirability function. Keywords: wire electrical discharge machining; WEDM; material removal rate; MRR; surface roughness; central composite design; CCD; response surface methodology; RSM. Copyright © 2016 Inderscience Enterprises Ltd.
Influence of process parameters on MRR and surface roughness Reference to this paper should be made as follows: Manjaiah, M., Narendranath, S., Basavarajappa, S. and Gaitonde, V.N. (2016) ‘Influence of process parameters on material removal rate and surface roughness in WED-machining of Ti50Ni40Cu10 shape memory alloy’, Int. J. Machining and Machinability of Materials, Vol. 18, Nos. 1/2, pp.36–53. Biographical notes: M. Manjaiah is a Postdoctoral Fellow in the Department of Mechanical Engineering Science at University of Johannesburg, South Africa. He obtained his MTech in Production Engineering Systems Technology from University B.D.T. College of Engineering, Davangere and PhD degree from National Institute of Technology Karnataka, Surathkal. His fields of interest include machining of shape memory alloy, surface integrity, materials characterisation, surface engineering, biomedical dental implants, process modelling and optimisation, application of artificial neural network (ANN), genetic algorithm (GA), particle swarm optimisation (PSO) and robust design in manufacturing processes. His work is published in several peer-reviewed scientific journals and conference proceedings on machining related research topics. S. Narendranath received his Doctor of Philosophy in the area of Shape Memory Alloys from Indian Institute of Technology Kharagpur (Kharagpur, India) in 2007; Master’s in Mechanical Engineering from University of B.D.T.C., Karnataka, India, in 1993, and Bachelor’s in Mechanical Engineering from Government B.D.T.C., Mysore University, Karnataka, India. He is a Professor in the Department of Mechanical Engineering at National Institute of Technology Karnataka, Surathkal, India. He has been involved in machining and corrosion behaviour of materials projects in cooperation with Department of Science and Technology, Indian Government in 2014. His main research interest is shape memory alloys, severe plastic deformation, welding, advanced manufacturing, and machining. His work is published in several peer-reviewed scientific journals and conference proceedings on machining related research topics. S. Basavarajappa is Professor and Head in the Department of Studies in Mechanical Engineering, University B.D.T. College of Engineering, Davangere, Karnataka, India. He has worked in National Aerospace Laboratories and Hindustan Aeronautic Limited Bangalore. His area of interest includes material science, composites, tribology and machining. He has more than 25 years of teaching and research experience. He is the editorial board member of few international journals, reviewer for many international journals and published more than 60 papers in refereed international journals. V.N. Gaitonde is a Professor in Industrial and Production Engineering Department at B.V.B. College of Engineering and Technology, Hubli. He obtained his ME in Production Management from Karnataka University, Dharwad and PhD degree from Kuvempu University, Shimoga. His fields of interest include process modelling and optimisation, application of artificial neural networks (ANN), genetic algorithm (GA), particle swarm optimisation (PSO) and robust design in manufacturing processes. He has more than 25 years of teaching and research experience. He is the editorial board member of four international journals, reviewer for many international journals and published more than 65 papers in refereed international journals.
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M. Manjaiah et al.
Introduction
Shape memory alloys (SMAs) have been extensively used in engineering and biomedical applications owing to exclusive properties such as shape memory effect and super-elastic property. In addition, TiNi SMAs have very good mechanical properties like high ductility, better corrosion as well as abrasion resistances (Otsuka and Ren, 2005). As a result of an excellent property outline, these alloys are used in thermo-mechanical actuators and substantial force generation in probable applications like couplings and other devices (Hsieh et al., 2009). TiNi alloy properties can be modified by substitution of ternary element. Replacing Ni with Cu narrow downs the transformation hysteresis, improves the actuation response as well as fatigue properties (Morakabati et al., 2010). Even though these materials have numerous prospective industrial applications, the traditional machining of these alloys is moderately complex due to pseudo elastic nature, lower thermal conductivity, higher tool wear and adhesive nature of TiNi alloys (Kaynak et al., 2013; Weinert et al. 2002). The electric discharge machining (EDM), a non-traditional machining process exhibits exceptional competence in machining of these alloys. Numerous investigations have been carried out to analyse the EDM performance characteristics. Chalisgaonkar and Kumar (2013) carried out multi-response optimisation in wire electric discharge machining (WEDM) of pure titanium using Taguchi method with utility concept. The optimal process parameters for cutting speed and surface roughness were determined using Taguchi modified concept. Ghodsiyeh et al. (2012) applied response surface methodology (RSM)-based approach for predicting and optimising the surface finish in WEDM of Ti-6Al-4V alloy with zinc coated brass wire. They developed surface roughness regression model to analyse the influence of process parameters such as pulse current, short pulse duration, time between two pulses, servo speed, servo reference voltage, injection pressure, wire speed and wire tension. Pasam et al. (2010) used Taguchi parameter design for optimising the surface finish in WEDM. Kumar et al. (2013) utilised desirability function for optimising multi-objectives like MRR, over cut and surface roughness during WEDM of titanium alloy. Nourbakhsh et al. (2013) explored the outcomes of affecting machining parameters on cutting speed, wire rapture and surface roughness in WEDM of titanium alloy using L18 orthogonal array experiments. Garg et al. (2012) employed non-dominated sorting genetic algorithm-II (NSGA) in WEDM of titanium alloy using cutting speed and surface roughness models. Kuriakose and Shunmugam (2004) also optimised the WEDM process parameters using NSGA. Kao et al. (2009) performed Taguchi and grey relation analysis on electrode wear, MRR and surface roughness to optimise the EDM parameters in Ti-6Al-4V alloy machining. Aspinwall et al. (2008) studied the surface roughness and surface integrity characteristics in WEDM of Ti-6Al-4V and Inconel 718 alloys. Hsieh et al. (2009) investigated the WEDM characteristics and shape recoverability of Ti-Ni-X (X = Zr, Cr) SMA. Narendranath et al. (2013) investigated the effects of pulse on time, pulse off time and peak current on surface characteristics of WEDM of Ti50Ni42.4Cu7.6 SMA using molybdenum wire electrode through Box-Behnken design (BBD). They reported that low peak current with higher pulse on time could reduce surface roughness; however, lower pulse on time with higher pulse off time is advantageous for higher MRR. Manjaiah et al.
Influence of process parameters on MRR and surface roughness
39
(2014) performed simultaneous optimisation on MRR and surface roughness in WEDM of Ti50Ni50 SMA with brass wire based on orthogonal array using Taguchi with utility concept. The pulse on time, pulse off time, servo voltage (SV), flushing pressure and wire speed were considered as the machining parameters for concurrently minimising surface roughness and maximising MRR. Their investigations indicated that the SV and wire speed do not have any noteworthy effects on optimising both MRR and surface roughness. Manjaiah et al. (2015) also conducted WEDM experiments on Ti50Ni40Cu10 and Ti50Ni30Cu20 SMAs using two different electrodes, namely, brass and zinc coated brass wires. The effects of pulse on time, pulse off time, SV, wire speed and servo feed on material removal rate (MRR), surface roughness, surface topography and metallographic changes have been studied. As can be seen from the previous works, several researchers investigated the effects of process parameters on MRR and surface roughness on WEDM of different alloys but only few have been reported on WEDM of TiNiCu SMA. In the present investigation, an effort has been made to examine the influence of pulse on time, pulse off time and SV in WEDM of Ti50Ni40Cu10 SMA with zinc coated brass wire on MRR and surface roughness using RSM-based mathematical models. The central composite design (CCD) was used to plan the experiments. The RSM-based MRR and surface roughness quadratic models were developed to analyse the interaction effects of process parameters. Further, simultaneous optimisation has been performed using desirability function
2
Experimental procedure
2.1 Work material and tool The work material used in the current study is Ti50Ni40Cu10 SMA, which is composed of 50% titanium, 40% nickel and 10% copper; prepared by using vacuum arc melting method. The hardness of as cast alloy was 420 Hv. Each specimen was cut to a curved block as shown in Figure 1. The schematic representation and photograph of workpiece used in the present investigation are given in Figure 1. The zinc coated brass wire electrode of diameter of 0.25 mm was used as a tool material.
2.2 Design of experiments and experimentation The aim of the current investigation is to analyse the influence of the controllable factors affecting the performance characteristics like MRR and surface roughness of WED-machined Ti50Ni40Cu10 SMAs. The modelling offers trustworthy equations accomplished through the planned experiments. The mathematical modelling based on RSM using design of experiments (DOE) is found to be a proficient tool (Montgomery, 2003). RSM not only decreases the cost and time; nevertheless gives the essential information about the direct and interaction effects of process parameters. Hence, in the current research, the effects of identified parameters on proposed characteristics are tested through a set of planned experiments through CCD in order to explore the quadratic response surface.
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M. Manjaiah et al.
Figure 1
The profile and photograph of the machining component used in the present investigation (see online version for colours)
The experiments were performed in an ‘Electronica 4-axis Maxicut CNC Wire cut-EDM. Three levels for each of the parameters, i.e., pulse on time (Ton), pulse off time (Toff) and SV were identified. The ranges of the identified parameters were selected based on authors’ preliminary experiments. •
Pulse on time (Ton), µs: Pulse on time is the discharge time duration for which the current is flowing in each cycle. During this, the voltage is applied between the electrode and workpiece. This is the sparking time duration.
•
Pulse off time (Toff), µs: Pulse off time is the time duration in between two simultaneous sparks. The voltage is absent during this part of cycle.
•
Duty cycle: Two WEDM process parameters complete the duty cycle (spark cycle), pulse on time (Ton) and pulse off time (Toff). The duty cycle is the period of change in voltage. Pulse on time (Ton) duration is the sparking cycle that the voltage has been built up.
•
SV, V: The spark gap set voltage is a reference voltage for the actual gap between the work piece and the wire used for cutting.
The identified parameters and their levels are illustrated in Table 1. Sixteen trials based on CCD were planned; the layout plan for the current experimental investigations is presented in Table 2. The peak current of 12 A and wire feed rate of 5 m/min was kept constant throughout the investigation. The impulse flushing of deionised water (12 Kg/cm2) was employed as a dielectric fluid. The trials were randomised to avoid the error creeping into the system. The WEDM characteristics, namely, MRR and surface roughness were selected. The tool wear is found to be very small and hence not considered in the present study. The initial weights of work were weighed using an electronic balance (0.0001 g accuracy). The work and electrode were connected to positive and negative terminals of power supply, respectively. Towards the end of each trial, the work was removed and weighed
Influence of process parameters on MRR and surface roughness
41
on a digital weighing machine. The machining time was determined using stopwatch. The MRR is computed as: MRR =
WRW ρ *Tm
(1)
where WRW is the work removal weight, ρ is density of workpiece, Tm is the machining time. ‘Talysurf’ surface roughness tester was used measure the surface roughness. The centreline average surface roughness of the work was measured with 0.8 mm cut-off value. The roughness values were measured at three different locations of the workpiece across the machined surface (transverse) and the average of five roughness values was taken as an arithmetic surface roughness (Ra). The computed values of MRR and measured values of surface roughness (Ra) are summarised in Table 2. The photograph of the experimental setup is shown in Figure 2. Table 1 Code
Selection of parameters and their levels Parameter
Level 1
Level 2
Level 3
Ton
Pulse on time (µs)
110
120
130
Toff
Pulse off time (µs)
48
54
60
SV
Servo voltage (V)
20
40
60
Table 2
Experimental layout plan as per CCD and proposed performance characteristics Ton (μs)
Toff (μs)
SV (V)
MRR (mm3/min)
Ra (μm)
1
120
60
40
6.843
1.87
2
120
54
40
7.044
1.93
3
130
60
60
4.843
1.49
4
130
48
60
2.979
1.12
5
110
54
40
4.702
1.35
6
110
60
60
3.215
1.19
7
130
54
40
5.907
1.72
8
120
54
60
3.306
1.32
9
120
54
40
5.879
1.84
10
110
48
60
2.876
1.35
11
110
60
20
6.014
2.01
12
110
48
20
6.895
2.12
13
130
60
20
10.566
2.79
14
130
48
20
9.958
2.39
15
120
54
20
7.194
2.24
16
120
48
40
6.915
1.85
Trial no.
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Figure 2
3
Experimental setup (see online version for colours)
Response surface modelling
In frequent circumstances, it is viable to represent the independent parameters in quantitative form and the performance criterion in terms of input parameters is given as (Montgomery, 2003): Y = φ ( x1 , x 2 , x 3 , ......, x k )
(2)
where Y is the performance criterion, x1, x2, x3, ……, xk are the input factors and is the response function. It can be approximated within the experimental region by a polynomial when the mathematical form of the response function is unknown. Higher the degree of polynomial better the correlation, but the experimentation costs will increase In the current investigation, second order RSM-based models for MRR and surface roughness (Ra) has been developed with pulse on time (Ton), pulse off time (Toff) and SV (V) as the process parameters. The RSM-based mathematical model is of the form (Montgomery, 2003): 2 2 Y = b0 + b1 *Ton + b 2 *Toff + b3 *SV + b11 *Ton + b 22 *Toff + b33 *SV 2
+ b12 *Ton *Toff + b13 *Ton *SV + b 23 *Toff *SV
(3)
where Y: response, i.e., MRR and Ra; b0, ……, b23: regression coefficients of the models are to be determined for each of the performance criteria. The regression coefficients of the quadratic model are determined by (Montgomery, 2003):
Influence of process parameters on MRR and surface roughness −1
B = ( XT X ) XT Y
43 (4)
where B: matrix of process parameter estimates; X: calculation matrix, which comprises linear, nonlinear and interaction terms, XT: transpose of X and Y: matrix of desired performance criterion. The constructed mathematical models to predict MRR and Ra during WEDM of Ti50Ni40Cu10 SMA using zinc coated brass wire electrode are given by: MRR = 20.7893 + 1.292294*Ton − 3.82575*Toff + 0.310782*SV 2 2 −0.00574*Ton + 0.027778*Toff − 0.00157 *SV 2 + 0.006279*Ton *Toff
(5)
−0.00368*Ton *SV + 0.002579*Toff *SV R a = −3.05679 + 0.362892*Ton − 0.68085*Toff + 0.040393*SV 2 2 −0.00184*Ton + 0.003987 *Toff + 0.000153*SV 2 + 0.002187 *Ton *Toff
(6)
−0.00061*Ton *SV − 0.000083* Toff *SV
where Ton and Toff are in µs; SV in V; MRR in mm3/min and Ra in µm. The statistical testing of the proposed RSM-based mathematical models was verified through analysis of variance (ANOVA) (Montgomery, 2003); the summary of ANOVA is presented in Table 3. It is observed that the proposed quadratic models are significant at 95% confidence interval as F-ratio of both the models is greater than 4.10 (F-table(9, 6, 0.05)). The adequacy of the constructed models is also verified through the coefficient of determination (R2) (Montgomery, 2003), which offers a measure of variability in observed values of performance criterion and can be elucidated by the independent input parameters and their interactions. R2 values illustrated in Table 3 obviously indicate the better correlation between the experimental and the predicted values of the proposed performance criteria. Hence, the proposed MRR and Ra quadratic models can be used for the prediction of MRR and surface roughness during WEDM of Ti50Ni40Cu10 SMA using zinc coated brass wire electrode. Table 3
ANOVA results and R2 values MRR and Ra mathematical models
Sum of squares Performance criterion Regression Residual
Degrees of freedom Regression Residual
Mean square
FRegression Residual ratio
R2
MRR
76.0244
2.2542
9
6
8.4472
0.3757 22.48 0.9712
Ra
3.21001
0.06154
9
6
0.35667
0.0102 34.77 0.9812
4
Results and discussion
The proposed quadratic models of MRR [equation (5)] and Ra [equation (6)] are utilised to envisage the MRR and surface roughness respectively by substituting the values of pulse on time (Ton), pulse off time (Toff) and SV within the ranges of the identified parameters. The two-factor interaction effect plots are constructed allowing for two parameters at a time, whereas the third parameter was kept at centre level and are used to analyse the machining behaviour.
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4.1 Analysis of MRR The interaction effects of pulse on time (Ton) – pulse off time (Toff), pulse on time (Ton) – SV and pulse off time (Toff) – SV on MRR are exhibited in Figures 3, 4 and 6. Figure 3
Effect of pulse on time and pulse off time on MRR (SV-40 V) (see online version for colours)
Figure 4
Effect of pulse on time and SV on MRR (pulse off time-54 µs) (see online version for colours)
Influence of process parameters on MRR and surface roughness
45
Figure 5
SEM images of the machined surfaces, (a) Ton = 110 µs and SV = 20 V (b) Ton = 130 µs and 60 V
(a)
(b)
Figure 6
Effect of pulse off time and SV on MRR (pulse on time-120 µs) (see online version for colours)
As can be seen from Figure 3, in general, for a specified value of pulse off time, the MRR nonlinearly increases with increased pulse on time. This is because, higher the pulse on time, larger will be the discharge energy and intensity of spark, which in turn eradicate larger amount of material and forming a deeper and bigger crater leading to higher MRR. It is noticed from the figure that with further increase in pulse off time (up to 52 µs), the MRR decreases. The reason is initially, lesser number of sparks discharge in a given instance at higher pulse off time, which helps to remove material at slower rate. It is worth mentioning here that MRR increases thereafter with increased pulse off time (beyond 56 µs). The increased MRR with pulse off time is mostly due to correct flushing of debris with adequate pulse off-time duration to enhance the deionisation. High pulse on time with high pulse off time is observed to be beneficial for maximising the MRR in
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M. Manjaiah et al.
our investigation. Further, it is observed that the MRR is more sensitive at higher pulse on time when compared to lower values. The variation due to pulse on time (Ton) – SV on MRR is given in Figure 4. It indicates that the behaviour of MRR with pulse on time is more or less same for any given value of SV. The MRR increases with increased pulse on time and with further increase in SV the MRR decreases. For lower SV (20 V), MRR increases with increased pulse on time because of higher discharge energy and lower discharge gap. Alternatively, for higher SV (60 V), it is revealed that the MRR increases with increased pulse on time up to 126 µs and after that it declines. The reason for this behaviour might be, initially, discharge gap gets widened due to higher voltage and with increased pulse on time the discharge energy increases along with widening of spark. Later, with increased pulse on time beyond 126 µs, the intensity of the spot is reduced probably due to widened crater. It is also observed from the figure that sensitivity of MRR is more at higher pulse on time values when compared to lower values. Figure 5 illustrates the scanning electron microscopy (SEM) images of the machined surfaces for two different combinations, namely, lower pulse on time with lower SV and higher pulse on time with higher SV. It is clearly noticed that for a combination of higher pulse on time with high SV, crater size and molten material spill out are large. This is due to increased intensity of discharge energy and inter electrode gap caused by the SV. These findings of our investigation closely agree with the results of Kung and Chiang, (2008). The plot showing the interaction effect of pulse off time (Toff) – SV on MRR is presented in Figure 6. For a specified value of SV, the MRR initially decreases nonlinearly with increased pulse off time up to 54 µs and thereafter increases. Further, with prolonged SV the MRR decreases. Higher MRR is observed at a combination of lower pulse off time with lower SV. It is also observed that MRR is insensitive to pulse off time for any value of SV. It is obvious that, lesser pulse off time causes larger number of sparks discharge in a specified circumstance, which helps to remove material more rapidly. In addition, at lower SV, increased MRR is mainly due to lower discharge gap. Hence, in the beginning due to the combination effect of these, the higher MRR is observed. In contrast, with prolonged pulse off time and higher SV, the debris could make the spark contaminated and unstable and thus leading to decreased MRR.
4.2 Analysis of surface roughness Figures 7, 8 and 10 demonstrate the interaction effects of pulse on time (Ton) – pulse off time (Toff), pulse on time (Ton) – SV and pulse off time (Toff) – SV on surface roughness (Ra). The influence of pulse on time (Ton) and pulse off time (Toff) on surface roughness is depicted in Figure 7. As seen from this figure, surface roughness nonlinearly progresses with increased pulse on time (up to 120 µs) and afterward roughness decreases for pulse off times selected in the range 48–52 µs. However, roughness nonlinearly increases with pulse on time and reached the maximum value for higher pulse off time. This is explained by the fact that, the energy content of single spark discharge is the product of pulse on time and the peak current. Increase in pulse on time produces a high discharge energy sparks that can melt and vaporise more amount of material. The surface roughness is mainly dependent on the energy per single spark and the efficiency of that spark utilised for creating a crater. The pulse energy utilisation efficiency is the ratio between the
Influence of process parameters on MRR and surface roughness
47
energy observed by the workpiece and the dielectric medium. The greater pulse on time produces higher discharge energy that forms deeper crater depth. Therefore, greater pulse on time deteriorates the surface quality. The size of the eroding surface (crater) is also dependent on the pulse energy in the discharge gap (Valentincic and Junkar, 2004). In addition, increased pulse on time also increases the flushing pressure, which helps into flush out the melted material from the machined surface, which in turn to lower surface roughness. At higher pulse on time and shorter pulse off time, the pulses produced are more with respect to the discharge time; creating a more number of overlapped craters that helps to reduce surface roughness. At a combination of higher pulse on time and pulse off time for a fixed SV, the discharge energy per pulses increases, the time allowed for deionisation available for charge carriers is low and most of the charge carriers formed during the discharge will not get deionised and thus forming deeper crater (Janardhan and Samuel, 2010). However, at higher pulse on time, it is sufficient to melt and vaporise the molten material that may adhere to the machined surface as globules and debris around the craters causing for higher surface roughness (Selvakumar et al., 2012). Figure 7
Effect of pulse on time and pulse off time on surface roughness (SV-40 V) (see online version for colours)
Figure 8 represents the estimated surface roughness in relation to pulse on time (Ton) and SV on surface roughness. The surface quality deteriorates with increased pulse on time and with further increase in SV, the surface quality improves. The surface roughness decreases with increased SV because of increased spark gap between the workpiece and electrode material that leads to the reduction in spark intensity due to energy absorbed by the dielectric medium. The less intensity sparks will in turn form smaller micro craters on the machined surface and thus causing better surface quality. Further, with increased pulse on time, due to higher pulse energy, the depth of crater is greater and hence greater MRR. Hence, an increased pulse on time with lower SV worsens the surface quality as seen in Figure 9(a). For higher SV and higher pulse on time, the intensity of spark gets
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M. Manjaiah et al.
diminishing due to widening of the gap and thus reduced surface roughness and the surface quality is shown in Figure 9(b). Figure 8
Effect of pulse on time and SV on surface roughness (pulse off time-54 µs) (see online version for colours)
Figure 9
SEM micrograph of machined surface of Ti50Ni40Cu10 alloy, (a) pulse on time-130 µs and SV-20 V (b) pulse on time-130 µs and SV-60 V
(a)
(b)
The behaviour of pulse off time (Toff) – SV on surface roughness is displayed in Figure 10, which clearly demonstrate the similar behaviour as that of MRR (Figure 6). From figure it is noticed that surface roughness is low for a combination of 54 µs pulse off time (medium value) with higher SV (60 V) and maximum roughness obtained at low pulse off time (54 µs) and low SV (20 V). At prolonged pulse off time, melting and evaporation of material primarily depend on the discharge energy. Besides, eroded substance will be swept away from the work surface by dielectric flushing and reduced
Influence of process parameters on MRR and surface roughness
49
surface roughness. With increased SV from 20–60 V, the spark intensity rate is high and thus causing aggravation of surface material and hence elevated surface roughness. Figure 10 Effect of pulse off time and SV on surface roughness (pulse on time-120 µs) (see online version for colours)
4.3 Composite desirability of MRR and surface roughness In the current investigation, the performance criteria such as MRR and surface roughness (Ra) are the responses, which are to be optimised. It is revealed from our investigation that increased MRR also increases the surface roughness. However, in machining, it is necessary to optimise the process parameters for achieving higher MRR and lower surface roughness. The best combination of process parameters, which results in minimal surface roughness, may not be suitable for maximising the MRR. Hence, trade-off is obligatory for simultaneous optimisation of performance criteria. Hence, in the present study, the optimal solution for multi-responses has been obtained by using composite desirability function. Composite desirability technique uses desirability function D(X) as an objective function (Derringer and Suich, 1980). This method transforms the response values into a 0 to 1 scale. The composite desirability means the individual desirability of the individual responses and the maximum desirability is the optimum condition. Combining an individual desirability value and then maximising the desirability have obtained the simultaneous optimisation. Table 4 illustrates the composite desirability and Figure 11 presents the optimised graph for MRR and surface roughness using MINITAB software (Minitab Inc., 2006). The composite desirability of function was considered as 0.773. Table 5 gives the optimal process parameter setting values for simultaneous maximising of MRR and minimising surface roughness.
50 Table 4 Sl. no.
M. Manjaiah et al. Process parameter settings with desirability values Ton (µs)
Toff (µs)
SV (V)
Desirability
1
120
60
40
0.727010
2
120
54
40
0.708886
3
130
60
60
0.657063
4
130
48
60
0.336805
5
110
54
40
0.636687
6
110
60
60
0.488106
7
130
54
40
0.740177
8
120
54
60
0.400869
9
120
54
40
0.708886
10
110
48
60
0.393848
11
110
60
20
0.665393
12
110
48
20
0.683372
13
130
60
20
0.345080
14
130
48
20
0.685128
15
120
54
20
0.643593
16
120
48
40
0.733132
Figure 11 Composite desirability graph (see online version for colours)
Influence of process parameters on MRR and surface roughness Table 5
Best combination values for simultaneous optimisation of MRR and surface roughness
Process parameter
Optimal value
Pulse on time (Ton)
130 µs
Pulse off time (Toff)
48 µs
Servo voltage (SV)
36.16 V
5
51
Conclusions
In the present experimental study, MRR and surface roughness (Ra) were analysed by response surface modelling in WED-machining of Ti50Ni40Cu10 SMA using zinc coated brass electrode. The pulse on time, pulse off time and SV have been identified as the process parameters and CCD was used to conduct the experiments. Based on the parametric analysis, the following conclusions are drawn within the identified ranges of the machining parameters: •
The MRR and surface roughness mainly affected by pulse on time and SV. The pulse off time has negligible influence on both the performance criteria.
•
MRR and surface roughness increase with increased pulse on time up to certain limit and decrease further for higher SV.
•
High pulse on time with high pulse off time is desirable to maximise MRR. The sensitivity of MRR is more at larger values of pulse on time when compared to lower values and MRR is insensitive to pulse off time for any value of SV.
•
The surface roughness primarily affected by the interaction of pulse on time and SV. An increased pulse on time with lower SV worsens the surface quality. On the other hand, at higher SV with higher pulse on time, the intensity of spark gets diminishing due to widening of the gap and hence reduced surface roughness
•
The pulse on time and pulse off time have greater interaction effects on the surface roughness. The surface roughness increases with increased pulse on time up to 126 µs and thereafter it reduces.
•
The optimal process parameter setting was found to be 130 µs of pulse on time, 48 µs of pulse off time and 36.16 of SV to simultaneously achieve greater MRR and lower surface roughness (Ra).
References Aspinwall, D.K., Soo, S.L., Berrisford, A.E. and Walder, G. (2008) ‘Workpiece surface roughness and integrity after WEDM of Ti-6Al-4V and Inconel 718 using minimum damage generator technology’, CIRP Annals – Manufacturing Technology, Vol. 57, No. 1, pp.187–190. Chalisgaonkar, R. and Kumar, J. (2013) ‘Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept’, Frontiers of Mechanical Engineering, Vol. 8, No. 2, pp.201–214.
52
M. Manjaiah et al.
Derringer, G. and Suich, R. (1980) ‘Simultaneous optimization of several response variables’, J. Qual. Technol., Vol. 12, No. 4, pp.214–219. Garg, M.P., Jain, A. and Bhushan, G. (2012) ‘Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm-II’, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 226, No. 12, pp.1986–2001. Ghodsiyeh, D., Lahiji, M.A. and Ghanbari, M. (2012) ‘Optimizing rough cut in WEDMing titanium alloy (Ti6Al4V) by brass wire using the Taguchi method’, Journal of Basic and Applied Scientific Research, Vol. 2, No. 8, pp.7488–7496. Hsieh, S.F., Chen, S.L., Lin, H.C., Lin, M.H. and Chiou, S.Y. (2009) ‘The machining characteristics and shape recovery ability of Ti-Ni-X (X = Zr, Cr) ternary shape memory alloys using the wire electro-discharge machining’, International Journal of Machine Tools and Manufacture, Vol. 49, No. 6, pp.509–514. Janardhan, V. and Samuel, G.L. (2010) ‘Pulse train data analysis to investigate the effect of machining parameters on the performance of wire electro discharge turning (WEDT) process’, International Journal of Machine Tools and Manufacture, Vol. 50, No. 9, pp.775–788. Kao, J.Y., Tsao, C.C., Wang, S.S. and Hsu, C.Y. (2009) ‘Optimization of the EDM parameters on machining Ti-6Al-4V with multiple quality characteristics’, The International Journal of Advanced Manufacturing Technology, Vol. 47, Nos. 1–4, pp.395–402. Kaynak, Y., Karaca, H.E., Noebe, R.D. and Jawahir, I.S. (2013) ‘Analysis of tool-wear and cutting force components in dry, preheated, and cryogenic machining of NiTi shape memory alloys’, Procedia CIRP, Vol. 8, pp.498–503. Kumar, A., Kumar, V. and Kumar, J. (2013) ‘Experimental investigation on material transfer mechanism in WEDM of pure titanium (grade-2)’, Advances in Materials Science and Engineering, Article ID 847876, 20pp, doi:10.1155/2013/847876. Kung, K-y. and Chiang, K-t. (2008) ‘Modeling and analysis of machinability evaluation in the wire electrical discharge machining (WEDM) process of aluminum oxide-based ceramic’, Mater. Manuf. Process., Vol. 23, No. 3, pp.241–250. Kuriakose, S. and Shunmugam, M.S. (2004) ‘Characteristics of wire-electro discharge machined Ti6Al4V surface’, Materials Letters, Vol. 58, Nos. 17–18, pp.2231–2237. Manjaiah, M., Narendranath, S., Basavarajappa, S. and Gaitonde, V.N. (2014) ‘Wire electric discharge machining characteristics of titanium nickel shape memory alloy’, Transactions of Nonferrous Metals Society of China, Vol. 24, No. 10, pp.3201–3209. Manjaiah, M., Narendranath, S., Basavarajappa, S. and Gaitonde, V.N. (2015) ‘Effect of electrode material in wire electro discharge machining characteristics of Ti50Ni50-xCux shape memory alloy’, Precision Engineering, Vol. 41, pp.68–77. Minitab Inc. (2006) Minitab User Manual Version 13, Quality Plaza, State College, PA, USA. Montgomery, D.C. (2003) Design and Analysis of Experiments, John Wiley, New York. Morakabati, M., Kheirandish, Sh., Aboutalebi, M., Taheri, A.K. and Abbasi, S.M. (2010) ‘The effect of Cu addition on the hot deformation behavior of NiTi shape memory alloys’, Journal of Alloys and Compounds, Vol. 499, No. 1, pp.57–62. Narendranath, S., Manjaiah, M., Basavarajappa, S. and Gaitonde, V. (2013) ‘Experimental investigations on performance characteristics in wire electro discharge machining of Ti50Ni42.4Cu7.6 shape memory alloy’, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 227, No. 8, pp.1180–1187. Nourbakhsh, F., Rajurkar, K.P., Malshe, A.P. and Cao, J. (2013) ‘Wire electro-discharge machining of titanium alloy’, Procedia CIRP, Vol. 5, pp.13–18. Otsuka, K. and Ren, X. (2005) ‘Physical metallurgy of Ti-Ni-based shape memory alloys’, Progress in Materials Science, Vol. 50, No. 5, pp.511–678. Pasam, V.K., Madar Valli, P. and Swapna, M. (2010) ‘Optimizing surface finish in WEDM using the Taguchi parameter design method’, Journal of the Brazilian society of Mechanical Science and Engineering, Vol. 32, No. 2, pp.107–113.
Influence of process parameters on MRR and surface roughness
53
Selvakumar, G., Sarkar, S. and Mitra, S. (2012) ‘Experimental investigation on die corner accuracy for wire electrical discharge machining of Monel 400 alloy’, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 226, No. 10, pp.1694–1704. Valentincic, J. and Junkar, M. (2004) ‘A model for detection of the eroding surface based on discharge parameters’, International Journal of Machine Tools and Manufacture, Vol. 44, No. 2–3, pp.175–181. Weinert, K., Petzoldt, V. and Buschka, M. (2002) ‘Machining properties of an austenitic NiTi shape memory alloy’, Production Engineering-Research and Development, Annals of the German Academic Society for Production Engineering, Vol. 10, No. 1, pp.9–12.
