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Experiments of Ti6Al4V manufactured by low-speed wire cut electrical discharge machining and electrical parameters optimization

  • Jinglan Guo EMAIL logo , Yanchen Wu , Chuang Pan , Li Wen and Shuangzhu Song
Published/Copyright: July 19, 2022
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 41 Issue 1

Abstract

Titanium alloy Ti6Al4V is widely used in aerospace, shipbuilding, petrochemical, and other industrial fields. Low-speed wire-cut electrical discharge machining (LS-WEDM) has the advantage of high machining accuracy. However, there are few research studies on the comprehensive effects of the electrical parameters of TC4 (Ti6Al4V) made by LS-WEDM on the machining surface. This paper focuses on LS-WEDM machining of titanium alloy TC4 and investigates the effects of electrical parameters on the surface roughness, kerf width, and cutting speed of TC4 specimens based on orthogonal tests. Five electrical parameters are optimized using the grey correlation method and the response surface method. The surface roughness of 1.744 μm, the kerf width of 215.432 μm, and the cutting speed of 24.759 mm2·min−1 are found to be the best process indicators, with errors of 3.3, 3.2, and 12.5% compared with the predicted values. The optimized results show that the surface roughness value is reduced by 50.9%, the kerf width is reduced by 29.4%, and the cutting speed is increased by 23%, which proves the accuracy of the optimized method.

Keywords: LS-WEDM; TC4; grey correlation analysis; surface response method; electrical parameter optimization

1 Introduction

Titanium alloy material TC4 has a series of excellent properties such as high hardness, high specific strength, and high corrosion resistance, which has always been used in aerospace, shipbuilding, petrochemical, and medical applications. TC4 belongs to an (α + β)-type titanium alloy with high strength, poor thermal conductivity, and easy deformation, which is a typical difficult-to-machine material [1]. Therefore, it is necessary to propose the high-quality and high-efficiency processing methods of TC4.

At the present stage, titanium alloy Ti6Al4V is mainly machined by turning, milling, and grinding. Sun et al. [2] reviewed the current status of research on titanium alloy machining tools and process technology and described the current challenges for limiting the efficient machining of titanium alloy. Zhang et al. [3] elaborated on the current status of research on surface integrity in the manufacturing process of titanium alloys from four process perspectives: cutting, grinding, composite machining, and special machining. Although scholars have conducted a lot of research in this area, high cutting temperature, cutting deformation, and cold hardening phenomena are severe during machining, resulting in easy tool wear and poor surface quality, and tool life and machining quality have not been effectively solved during conventional machining. Compared with traditional machining methods, low-speed wire-cut electrical discharge machining (LS-WEDM) has the advantages of simple machining operation, high machining accuracy, and no tool needed in the field of non-traditional machining. The need for titanium alloy TC4 using LS-WEDM is increasing. Due to its non-mechanical contact, high machining accuracy, and good surface quality, LS-WEDM has irreplaceable advantages for producing precise and complex three-dimensional structures [4]. Wire electrical discharge machining (WEDM) is an effective and reasonable alternative to machine these hard-to-machine alloys among the other available advanced machining processes [5]. Sun et al. [6] and Gong et al. [7] investigated the effects of process parameters on machining time, kerf width, and surface roughness during the process of titanium alloy TC4 made by LS-WEDM. The grey correlation analysis method was used to transform multi-objective optimization into single-objective optimization using the grey correlation degree. The optimal parameter combination of LS-WEDM under multiple process requirements was then obtained. Simultaneously, the surface damage characteristics of TC4 specimens were analyzed. Finally, multiple cutting strategies were combined to improve the surface quality and reduce surface defects. Sarkar et al. [8] optimized the process parameters of titanium alloy made by WEDM. The relationship between surface roughness and cutting speed was obtained. Kiyak and Cakir [9] found that reducing the pulse width and increasing the pulse interval can reduce the surface roughness of 40CrMnNiMo864 effectively. Mahapatra and Patnaik [10] used the Taguchi method to optimize the discharge parameters of WEDM and established a nonlinear regression model of surface roughness. Bisaria and Shandilya [11,12] studed unveils the variation of surface integrity aspects (micro-hardness, surface crack density, and surface characteristics) for Ni(55.95)Ti(44.05) shape memory alloy (SMA) with wire electric discharge machining (WEDM) process parameters, namely, pulse off time, wire tension, spark gap voltage, wire feed rate, and pulse on time. A mathematical model was developed for surface roughness and material removal rate considering servo voltage, pulse on time, wire tension, wire feed rate, and pulse off time using a response surface methodology technique. Manoj et al. [13] studied the cutting velocity, surface roughness, recast layer, and microhardness variations of the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. Nas et al. [14] investigated the effects of machining parameters on the experimental and statistical results using the electric discharge method in the machining of AISI D2 cold work tool steel. Ayyildiz et al. [15] established the experimental layout using the Taguchi L-18 orthogonal array, and experimental data were examined via a statistical analysis of variance (ANOVA).

In summary, the machining processes of high-speed steel and cemented carbide made by LS-WEDM are relatively mature, in which some built-in electrical parameters have existed in the machine tool system of LS-WEDM. However, with respect to the difficult-to-machine material titanium alloy TC4, the electrical parameters have an important influence on the surface roughness, kerf width, and cutting speed. There are no available electrical parameters for WEDM machining now. Therefore, this paper optimizes the electrical parameters for LS-WEDM machining of titanium alloy TC4. Then, the effects of various electrical parameters, including pulse width, pulse interval, peak current, servo reference voltage (SV), and servo speed on the surface roughness, kerf width, and cutting speed, are analyzed, respectively. Finally, the appropriate electrical parameters for machining the titanium alloy TC4 are obtained using grey correlation analysis and response surface method (RSM), which can provide some references for machining titanium alloy made by LS-WEDM.

2 Experimental set-up

The machine tool used in this paper is the DK7632A low-speed unidirectional WEDM produced by Suzhou Sanguang Technology Co., Ltd. The material used for the specimen is titanium alloy TC4, the metallographic organization is (α + β) type, the thickness is 10 mm, the tensile strength is 915 MPa, the yield strength is 845 MPa, the modulus of elasticity is 114 MPa, and the hardness is 36 HRC. The specimen is connected to the positive pole of the pulse power supply, and the chemical composition is shown in Table 1. Φ 0.2 mm brass wire is used as the tool electrode to connect to the negative electrode of the pulse power. The machining method is immersion machining. The working medium is deionized water. The wire speed is 14 m·min−1. The machining fluid pressure is 18 kg·cm2. The machining fluid conductivity is 5 × 104 Ω·cm. The machining fluid flow is 5 L·min−1. The wire tension is 12 N.The distance between the wire guide roller and workpiece is 0.1 mm.

Table 1

Chemical composition of Ti6Al4V

Element C Fe N O Al V H Ti
Content (%) 0.01 0.12 0.01 0.14 6.1 4.1 0.001 Margin

For the LS-WEDM process, since the machined surface is made up of a large number of discharge pits, the shape and size of the discharge pits determine the values of surface roughness. The formation of the discharge pits is directly related to the discharge current of the pulse power supply. Meanwhile, the discharge current affects both the kerf width and the cutting speed. By analyzing the manual of processing parameters for LS-WEDM, five electrical parameters are selected under the conditions of electrode wire speed of 14 m·min−1 and electrode wire tension of 11.76 N for experimental analysis, including pulse width (ON), pulse interval (OFF), peak current (IP), servo reference voltage (SV), and servo speed (SF), which have important effects on the discharge current. Pulse widths of 5–20 s, pulse intervals of 90–105 s, peak currents of 7.5–9.0 A, servo reference voltages of 30–60 V, and servo speeds of 5.0–15 mm·min−1 are chosen based on a lot of experiments, which can ensure stable and continuous machining. In fact, this experimental machine has just four gear settings within these electric parameter ranges, which are listed in Table 2. With surface roughness, kerf width, and cutting speed as process indexes, the L16(45) orthogonal test scheme is shown in Table 3.

Table 2

Orthogonal test factors and levels

Levels (A) ON (μs) (B) OFF (μs) (C) IP (A) (D) SV (V) (E) SF (mm·min−1)
1 5 90 7.5 30 15
2 10 95 8.0 40 10
3 15 100 8.5 50 7.5
4 20 105 9.0 60 5.0
Table 3

Orthogonal test schemes of L16(45)

Test number (A) On (μs) (B) OFF (μs) (C) IP (A) (D) SV (V) (E) SF (mm·min−1)
1 5 90 7.5 30 15
2 5 95 8.0 40 10
3 5 100 8.5 50 7.5
4 5 105 9.0 60 5.0
5 10 90 8.0 50 5.0
6 10 95 7.5 60 7.5
7 10 100 9.0 30 10
8 10 105 8.5 40 15
9 15 90 8.5 60 10
10 15 95 9.0 50 15
11 15 100 7.5 40 5.0
12 15 105 8.0 30 7.5
13 20 90 9.0 40 7.5
14 20 95 8.5 30 5.0
15 20 100 8.0 60 15
16 20 105 7.5 50 10

3 Results and discussions

3.1 Results

The NewView series white light interferometer from zygo, USA, is used for non-contact measurement of 16 test samples with the size of 12.5 mm × 8 mm × 10 mm after machining. The surface roughness and the kerf width are obtained. Eight groups of these surface morphology and surface roughness results are shown in Figure 1. The results are sorted in the order of the surface roughness values. The surface roughness value R a is measured three times, and the average value is calculated as the R a result. The cutting speed value is calculated by recording the cutting time.

Figure 1 The surface morphology and surface roughness in Table 3: (a) 6th group, (b) 15th group, (c) 1st group, (d) 16th group, (e) 13th group, (f) 11th group, (g) 3rd group, and (h) 10th group.
Figure 1

The surface morphology and surface roughness in Table 3: (a) 6th group, (b) 15th group, (c) 1st group, (d) 16th group, (e) 13th group, (f) 11th group, (g) 3rd group, and (h) 10th group.

It can be seen from Figure 1 that the surface machined by LS-WEDM is composed of countless small pits that overlap each other. This is completely different from the surface morphology of traditional machining methods. The reason is that the surface of the titanium alloy specimen is partially melted and vaporized after each pulse discharge. The part of the metal is thrown off the surface of the workpiece, and the rest is re-solidified to form a discharge pit with convex edges [16,17]. Analysis shows that the change of electrical parameters has a greater effect on the surface roughness.

On-line detection of kerf width is shown in Figure 2.

Figure 2 On-line detection of kerf width under the ON is 20 μs, OFF is 95 μs, IP is 8.5 A, SV is 30 V, and SF is 5.0 mm·min−1: (a) three-dimensional graphics and (b) identification.
Figure 2

On-line detection of kerf width under the ON is 20 μs, OFF is 95 μs, IP is 8.5 A, SV is 30 V, and SF is 5.0 mm·min−1: (a) three-dimensional graphics and (b) identification.

From Figure 2, the kerf width has an important influence on the dimensional accuracy of machining. A stable kerf width is a prerequisite for achieving high dimensional accuracy machining. The LS-WEDM kerf is mainly composed of three parts: the diameter of the electrode wire, the discharge gap, and the maximum amplitude of the electrode wire. Figure 2 shows the 14th group of test data in Table 3, and the value of kerf width is 304.95 μm, which reaches the maximum value of kerf width. It indicates the lowest machining accuracy of the sample with these parameters.

3.2 Processing

This paper uses the orthogonal experimental design method to solve the difficulties of analyzing electrical parameters on-line. Surface roughness, kerf width, and cutting speed are used as evaluation indicators. Furthermore, process analysis and parameter optimization are performed. The orthogonal table and test results are shown in Table 4.

Table 4

The test results of the orthogonal table

No. A B C D E R a (μm) Kerf width (μm) Cutting speed (mm2·min−1)
1 5 90 7.5 30 15 0.765 229.847 1.032
2 5 95 8.0 40 10 0.806 234.737 1.115
3 5 100 8.5 50 7.5 2.754 262.387 28.210
4 5 105 9.0 60 5.0 2.629 275.421 20.365
5 10 90 8.0 50 5.0 1.051 208.655 1.644
6 10 95 7.5 60 7.5 0.945 229.004 1.092
7 10 100 9.0 30 10 3.008 271.415 35.714
8 10 105 8.5 40 15 3.040 269.785 30.879
9 15 90 8.5 60 10 3.137 289.346 40.258
10 15 95 9.0 50 15 3.219 284.566 51.440
11 15 100 7.5 40 5.0 1.465 234.018 1.637
12 15 105 8.0 30 7.5 1.587 228.305 1.562
13 20 90 9.0 40 7.5 3.555 281.304 55.066
14 20 95 8.5 30 5.0 3.502 304.950 53.694
15 20 100 8.0 60 15 1.939 235.643 1.852
16 20 105 7.5 50 10 1.416 219.336 1.773

Because the three process evaluation indicators of WEDM are different in units and properties, when the three process indexes are comprehensively considered to optimize the electrical parameters, it is necessary to conduct the dimension normalization treatment [18]. The results are shown in Table 5.

Table 5

Orthogonal table test results using dimension normalization processing

No. A B C D E y 1 y 2 y 3
1 5 90 7.5 30 15 1.000 0.780 0.000
2 5 95 8.0 40 10 0.985 0.729 0.002
3 5 100 8.5 50 7.5 0.287 0.442 0.503
4 5 105 9.0 60 5.0 0.332 0.307 0.358
5 10 90 8.0 50 5.0 0.897 1.000 0.011
6 10 95 7.5 60 7.5 0.935 0.789 0.001
7 10 100 9.0 30 10 0.196 0.348 0.642
8 10 105 8.5 40 15 0.185 0.365 0.552
9 15 90 8.5 60 10 0.150 0.162 0.726
10 15 95 9.0 50 15 0.120 0.212 0.933
11 15 100 7.5 40 5.0 0.749 0.737 0.011
12 15 105 8.0 30 7.5 0.705 0.796 0.010
13 20 90 9.0 40 7.5 0.000 0.246 1.000
14 20 95 8.5 30 5.0 0.019 0.000 0.975
15 20 100 8.0 60 15 0.580 0.720 0.015
16 20 105 7.5 50 10 0.767 0.889 0.014

3.3 The effects of electrical parameters on surface roughness

According to the analysis, the primary and secondary relationship between electrical parameters and the surface roughness can be obtained: peak current > pulse width > pulse interval > servo speed > SV. Because the surface of the processed titanium alloy TC4 is composed of many irregular small pits, pulse discharge energy has a significant effect on surface roughness. With the increase of single pulse energy, the metal erosion increases. Also, the pits become larger to make the surface roughness get worse. Peak current and pulse width are important parameters that affect the energy of a single pulse and, therefore, have the greatest impact on surface roughness. Although the servo speed and SV have a certain effect on the surface roughness, the effect is much smaller than the peak current and pulse width [19]. The best combination parameter for single-objective optimization of surface roughness is C3A4B3E1D2/C3A4B3E1D1, which is shown in Figure 3. The best combination parameter is the peak current of 8.5 A, the pulse width of 20 μs, the pulse interval of 100 μs, the servo speed of 15 mm·min−1, and the SV of 40 V, or the peak current of 8.5 A, the pulse width of 20 μs, the pulse interval of 100 μs, the servo speed of 15 mm·min−1, and the SV of 30 V.

Figure 3 The effect trends of the electrical parameters on surface roughness.
Figure 3

The effect trends of the electrical parameters on surface roughness.

ANOVA is performed on the test results to obtain the degree and significance of the effect of each electrical parameter on the surface roughness, as shown in Table 6. From Table 6, it can be seen that the peak current and pulse width have an extremely significant effect on the surface roughness of TC4 machining. This is because the surface quality of EDM is related to the amount of removal per discharge, and the amount of removal per discharge depends on the size of the individual pulse energy, which is mainly determined by two factors: peak current and pulse width. Within a certain discharge time, the increase in peak current can increase the pulse energy, resulting in an increase in the diameter and depth of the discharge pits and thus inducing an increase in the surface roughness value. Therefore, to improve the surface roughness, the size of the individual pulse energy must be controlled.

Table 6

Variance analysis of the surface roughness

Source of variance Sum of squares of deviations f Mean square F P Conspicuousness
A 0.222 3 0.074 55.913 0.004 **
B 0.010 3 0.003 2.460 0.240 —
C 1.785 3 0.595 450.503 0.000 **
D 0.004 3 0.001 1 0.5 —
E 0.007 3 0.002 1.641 0.348 —
Overall error 2.027 15 0.135 — — —

**represents the importance of the second level.

3.4 The effects of electrical parameters on kerf width

From Figure 4, the peak current has the greatest influence on the kerf width, followed by pulse width, pulse interval, SV, and servo speed. The kerf width is greatly affected by the wire diameter and pulse energy. Under the premise that the diameter of the electrode wire remains the same, metal erosion and the kerf width increase with the increase of pulse energy. So we find that the peak current and pulse width have the greatest influence on the kerf width. The SV affects the discharge gap, the vibration of the electrode wire, and the processing stability. Compared with the servo speed, the SV has a greater influence on the kerf width. The best combination parameter for single-objective optimization of kerf width is C3A4B2D1E4, in which the peak current is 8.5 A, the pulse width is 20 μs, the pulse interval is 95 μs, the SV is 30 V, and the servo speed is 5.0 mm·min−1.

Figure 4 Trend chart of influence on kerf width.
Figure 4

Trend chart of influence on kerf width.

The test results are analyzed using ANOVA to obtain the degree and significance of the effect of each electrical parameter on the kerf width, as shown in Table 7. It can be seen that the peak current has a significant effect on the kerf width. At a certain discharge time, the peak current increases, leading to an increase in the discharge gap and an increase in the vibration of the electrode wire, which results in a larger kerf width.

Table 7

Variance analysis of the kerf width

Source of variance Sum of squares of deviations f Mean square F P Conspicuousness
A 0.070 3 0.023 9.137 0.051 —
B 0.057 3 0.019 7.456 0.067 —
C 1.190 3 0.400 155.255 0.000 *
D 0.060 3 0.020 7.793 0.063 —
E 0.008 3 0.003 9.137 0.5 —
Overall error 1.384 15 0.092 — — —

*represents the importance of the first level.

3.5 The effects of electrical parameters on cutting speed

The peak current and pulse width have the greatest influence on the cutting speed, which is shown in Figure 5. Obviously, the SV and servo speed have the least influence on the cutting speed. The peak current increases, which can enhance single pulse energy can add the discharge trace of the workpiece, so the cutting speed increases rapidly. If it continues to increase the peak current, the stability of processing will become worse and the processing speed will decrease significantly. The best combination parameter for single-objective optimization of cutting speed is C4A4B2D1E3. The parameter includes that the peak current is 9 A, the pulse width is 20 μs, the pulse interval is 95 μs, the servo reference voltage is 30 V, and the servo speed is 7.5 mm·min−1.

Figure 5 Trend chart of influence on cutting speed.
Figure 5

Trend chart of influence on cutting speed.

The results of ANOVA for the degree and significance of the effect of electrical parameters on the cutting speed are shown in Table 8. The results show that peak current, pulse width, and pulse interval have an extremely significant effect on the cutting speed. The increase in pulse width means that the action time of the discharge increases and makes the pulse energy increase. At a certain discharge time, the increase in peak current can also increase the pulse energy and further improve machining speed. Additionally, under other conditions remain the same, the interval time is reduced, then the frequency of the pulse is increased, and the machining speed becomes faster. However, the pulse interval should not be too small; otherwise, it is not sufficient for the elimination of ionization, and the products of galvanic corrosion are not excluded in time, which can make the machining unstable and easy to burn the workpiece and break the wire.

Table 8

Variance analysis of the cutting speed

Source of variance Sum of squares of deviations f Mean square F P Conspicuousness
A 0.191 3 0.064 39.300 0.007 **
B 0.160 3 0.053 32.867 0.009 **
C 1.981 3 0.661 408.031 0.000 **
D 0.042 3 0.014 8.578 0.055 —
E 0.005 3 0.002 1 0.5 —
Overall error 2.378 15 0.159 — — —

**represents the importance of the second level.

In summary, the peak current has a significant effect on surface roughness, kerf width, and cutting speed. The effect of pulse width on surface roughness and cutting speed is also significant. The pulse interval has a significant effect on cutting speed. The effects of servo reference voltage and servo speed on the three process indicators are not significant.

4 Optimization

4.1 The correlations between electrical parameters and process indicators

During the WEDM machining process, interactions among electrical parameters are strong. To simplify the optimization process, the optimization objectives with a low correlation degree can no longer be considered. The grey system theory puts forward the concept of grey correlation analysis of each subsystem and finds out the relationship among the subsystems (or factors). Therefore, the grey correlation analysis provides a quantitative measure for the development trend in a system, which is suitable for such dynamic process analysis. Based on grey correlation analysis, the correlations among the five electrical parameters and the surface roughness, the kerf width, and the cutting speed are analyzed to obtain the optimization objectives with higher correlation.

Grey correlation analysis mainly includes grey absolute correlation analysis, grey relative correlation analysis, and grey comprehensive correlation analysis. In this paper, the grey relative correlation analysis method is used to evaluate the correlation of various parameters. The specific calculation steps include [20,21] determining the analysis sequence, dimensionless processing of parameters, calculating the difference sequence and the maximum and minimum difference of two poles, and calculating the correlation coefficient and correlation degree. The grey relative correlation algorithm [22] can be realized by the following equations:

(1) γ 0 i = 1 + ∣ s 0 ′ ∣ + ∣ s i ′ ∣ 1 + ∣ s 0 ′ ∣ + ∣ s i ′ ∣ + ∣ s i ′ − s 0 ′ ∣ ∣ s ′ 0 ∣ = ∑ k = 2 n − 1 x 0 ′ 0 ( k ) + 1 2 x i ′ 0 ( n ) , ∣ s ′ i ∣ = ∑ k = 2 n − 1 x i ′ 0 ( k ) + 1 2 x i ′ 0 ( n ) ∣ s ′ i − s ′ 0 ∣ = ∑ k = 2 n − 1 ( x i ′ 0 k ) − x 0 ′ 0 ( k ) ) + 1 2 ( x i ′ 0 ( n ) − x 0 ′ 0 ( n ) ) ,

where γ is the correlation degree, x is a factor, x′ is the initial image of x, x′0 is the initial zero image of x, s 0 ′ and s i ′ are intermediate variables, k is the serial number, and n is the total number of serial numbers.

In order to obtain the correlation degree of two different sequences (e.g. electrical parameters and surface roughness) with X 0 = {x 0(1), x 0(2),…, x 0(n)} and X i = {x i (1), x i (2), …, x i (n)}, first the initial image needs x′(k) = x(k)/x(1), k = 1, 2,…, n, should be calculated. Then, the initial zero image x′0(k) = x′(k) − x′(1). This initial zero image is used along with equation (1) to calculate the correlation degree between the two factors. The advantage of grey relative correlation analysis is that no artificial resolution coefficients are required, and the grey correlation coefficients are not computed first, which improves the efficiency of calculation.

Different reference sequences and comparison sequences could constitute the grey correlation analysis matrix. There are five electrical parameters under investigation, including pulse width, pulse interval, peak current, servo reference voltage, and servo speed. The three process indicators are surface roughness, kerf width, and cutting speed. Combining the levels of the 16 groups of factors, the grey correlation analysis matrix is obtained. According to equation (1), the correlations among the electrical parameters and the three process indicators are calculated, as shown in Table 9 and Figure 6.

Table 9

Grey correlation value

Electrical parameter Surface roughness Kerf width Cutting speed Average
ON 0.889 0.547 0.538 0.658
OFF 0.530 0.901 0.503 0.645
IP 0.536 0.981 0.504 0.673
SV 0.638 0.634 0.514 0.595
SF 0.607 0.672 0.511 0.597
Figure 6 Relative correlation between electrical parameters and process indicators.
Figure 6

Relative correlation between electrical parameters and process indicators.

From Table 9 and Figure 6, the five electrical parameters are closely related to the process indexes, in which the relative correlation degrees are all above 0.5. Among them, the relative correlation between the surface roughness and the pulse width is 0.889. The relative correlation degrees between the servo reference voltage, servo speed, and the surface roughness is greater than that between the pulse interval, the peak current, and the surface roughness. The pulse width and peak current are the determinants of the average discharge energy of LS-WEDM. Within a certain range, the increase of pulse width and peak current can increase the average discharge energy, improve the processing efficiency, and shorten the machining time. Meanwhile, the increase of the discharge energy also increases the single-pulse material removal, making the single-pulse electrolytic pit become deeper and larger. The wire-cut machining surface is composed of a large number of electro-erosion pits, so the surface roughness value can greatly increase. The kerf width has the strongest correlation with the peak current, having a relative correlation degree of 0.981. Meanwhile, the relative correlation between kerf width and pulse interval is 0.901, followed by the servo speed and the servo reference voltage. The relative correlation between slit width and pulse width is the lowest, and the value is 0.547. The results show that the increase of the peak current can increase the average discharge energy between the electrode wire and the workpiece, thereby increasing the discharge gap, causing a significant raise in the kerf width. Additionally, cutting speed has a lower connection with the five electrical parameters than surface roughness and kerf width do with the five electrical parameters. Among them, the relative correlation between the cutting speed and the pulse width is larger, which is 0.538. The pulse width determines the pulse discharge time. Thus, the increase of the pulse width can increase the cutting speed accordingly.

In summary, the peak current has the closest correlation with the three process indicators, and the average correlation degree is 0.673. The relative correlation degrees of pulse width, pulse interval, servo speed, and SV on process indicators are 0.658, 0.645, 0.597, and 0.595 respectively. This is consistent with the findings of [3]. It can be seen that in the selection of electrical parameter objectives, peak current, pulse width, and pulse interval can be regarded as important electrical parameter optimization objectives. According to the order of importance, the initial conditions for multi-objective process optimization based on the RSM are obtained.

4.2 Optimization of electrical parameters based on RSM

The RSM is a multi-objective optimization method based on response surface analysis. Its basic principle is to use multiple quadratic regression equations to fit the functional relationship between control variables and optimization objectives and obtain the optimal electrical parameters by analyzing the regression equation [23]. Due to the sufficient data from the previous test results, it can be regarded as the full parameter experiment method; thus, the historical data design (HDD) could be used. Its advantage is that any parameter and any number of horizontal values can be selected. HDD adopts a second-order design, which obtains an accurate approximation of the response surface in a small range near the optimal value in the region of x 1, x 2,…, x k , and identifies the optimal process conditions. Near the best point of the response surface, the second-order model is used to approximate the response surface [24], given as follows:

(2) y = β 0 + ∑ i = 1 k β i x i + ∑ i = 1 k β i i x i 2 + ∑ i < j k β i j x i x j + ε k .

According to the results of response surface analysis, the response surfaces of surface roughness, kerf width, and cutting speed to electrical parameters are obtained, some of which is shown in Figure 7 (ON presents the pulse width, OFF presents the pulse interval, IP presents the peak current, SV presents the SV, and SF presents the servo speed). From Figure 7, it can be seen that the surface roughness value increases with the increase of the pulse width from 5 to 20 μs. When the pulse width is 5 μs, the surface roughness increases with the decrease of the SV. The reason is that the pulse discharge time increases with the increase of pulse width, resulting in large pulse energy and bad surface roughness. In the range of the peak current from 7.5 to 9 A, the kerf width increases with the increase of the peak current. This is because the energy of a single pulse increases due to the increase of peak current; thus, the kerf width becomes larger. Furthermore, the cutting speed increases with the increase of the pulse width within 5–20 μs.

Figure 7 Response surface of surface roughness, kerf width, and cutting speed to electrical parameters: (a) surface roughness and ON, SV; (b) surface roughness and ON, SV; (c) kerf width and IP, OFF; (d) kerf width and IP, OFF; (e) cutting speed and ON, SV; and (f) cutting speed and ON, SV.
Figure 7

Response surface of surface roughness, kerf width, and cutting speed to electrical parameters: (a) surface roughness and ON, SV; (b) surface roughness and ON, SV; (c) kerf width and IP, OFF; (d) kerf width and IP, OFF; (e) cutting speed and ON, SV; and (f) cutting speed and ON, SV.

The constraints of the process indicators are the surface roughness and the slit width are set as the minimum, and the cutting speed is set as the maximum. According to the grey correlation analysis in Section 3.1, the importance of the optimization objective is obtained as the initial conditions of the optimization process. According to the analysis and calculation of the RSM, the desirabilities of different electrical parameters are obtained, as shown in Figure 8.

Figure 8 Desirability under different electrical parameters: (a) ON and SV; and (b) ON and IP.
Figure 8

Desirability under different electrical parameters: (a) ON and SV; and (b) ON and IP.

From Figure 8, the smaller the pulse width, the lower the pulse interval and the lower the peak current has, the higher the desirability is, indicating that these combinations can enhance the coordination degree of the three process indicators, and can better meet the optimized initial conditions. The pulse width plays an important role in determining discharge energy. Decreasing the pulse width reduces discharge energy and machining speed. The ionization between the electrode wire and the workpiece is affected by the pulse interval. The discharge energy of the gap increases as the pulse interval decreases, as does the processing speed. The peak current decreases, the individual pulse energy decreases, the workpiece discharge trace decreases, thus the cutting speed decreases rapidly. Among them, the most desirable electrical parameter is at the pulse width of 5.007 μs, the pulse interval of 90.034 μs, the peak current of 7.844 A, the SV of 60.000 V, and the servo speed of 6.858 mm·min−1. Meanwhile, the predicted values of surface roughness, kerf width, and cutting speed under this combination are 1.688 μm, 208.655 μm, and 22.002 mm2·min−1, respectively.

5 Verification

Verification experiments are conducted using the optimized electrical parameters for LS-WEDM wire-cutting titanium alloy TC4. Because the electrical parameters of the machine tool cannot be accurately controlled, the electrical parameters used in the experiment are parameters close to the theoretical values, which are set as the pulse width of 5 μs, the pulse interval of 90 μs, the peak current of 8 A, and the SV of 60 V. There are a total of four gears of servo speed in the machine parameters, and the corresponding parameter values of each gear are 5.0, 7.5, 10, and 15 mm·min−1. The servo speed is 7.5 mm·min−1 using the closest gear. The experimental setup is the same as those described in Section 1. Non-contact measurement of processed specimens using NewView series white light interferometer from Zygo company of the United States. The surface roughness and kerf width values are obtained, and the cutting speed values are calculated, which are 1.744 μm, 215.432 μm, and 24.759 mm2·min−1, as shown in Figure 9. The errors between the actual and predicted values are 3.3, 3.2, and 12.5%, respectively. Compared with the orthogonal test, the surface roughness value is decreased by 50.9%, the kerf width is lowered by 29.4%, and the cutting speed is raised by 23%. Also, the machining efficiency of titanium alloy TC4 using LS-WEDM is enhanced that the effectiveness of the LS-WEDM electrical parameter optimization method based on the grey correlation method and RSM.

Figure 9 Non-contact measurement of experimental specimens under optimal electrical parameters: (a) surface roughness; (b) kerf width.
Figure 9

Non-contact measurement of experimental specimens under optimal electrical parameters: (a) surface roughness; (b) kerf width.

6 Conclusion

  1. Peak current has a considerable influence on surface roughness, kerf width, and cutting speed under the machining conditions described in this work. Surface roughness and cutting speed are also affected by pulse width. The cutting speed is significantly affected by the pulse interval.

  2. Grey correlation analysis identifies the most essential optimization objectives as peak current, pulse width, and pulse interval. The ideal electrical parameters are derived using the response surface optimization method, which are the pulse width of 5 s, the pulse interval of 90 s, the peak current of 8 A, the SV of 60 V, and the servo speed of 7.5 mm·min−1.

  3. Surface roughness, kerf width, and cutting speed based on experimental verification are 1.744 μm, 215.432 μm, and 24.759 mm2·min−1, respectively. The errors between the actual and predicted values are 3.3, 3.2, and 12.5%, respectively.

  4. The surface roughness value is decreased by 50.9%, the kerf width is lowered by 29.4%, and the cutting speed is raised by 23% with the ideal electrical parameters, demonstrating the correctness of the optimization process.

  1. Funding information: The authors are pleased to acknowledge the financial support by the National Natural Science Foundation of China (No. 51375126).

  2. Author contributions: Jinglan Guo: writing – original draft, writing – review and editing; Yanchen Wu: writing – original draft, methodology, project administration; Chuang Pan: formal analysis; Li Wen: visualization; Shuangzhu Song: resources.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: All data generated or analyzed during this study are included in this published article.

References

[1] Kuriachen, B., K. P. Somashekhar, and J. Mathew. Multiresponse optimization of micro-wire electrical discharge machining process. The International Journal of Advanced Manufacturing Technology, Vol. 76, No. 1–4, 2015, pp. 91–104.10.1007/s00170-014-6005-2Search in Google Scholar

[2] Sun, J., K. Guo, and B. Yang. Review on cutting tool and processing technology for titanium alloy aviation components. Aeronautical Manufacturing Technology, Vol. 64, No. 16, 2021, pp. 74–86.Search in Google Scholar

[3] Zhang, G. G., Y. L. Sun, W. L. Fan, L. Y. Wang, F. X. Yang, Y. Sheng, et al. Research progress and future development of surface integrity on machined surface of titanium alloys. Aeronautical Manufacturing Technology, Vol. 65, No. 4, 2022, pp. 36–55.Search in Google Scholar

[4] Arjmand, A. A., S. K. Amineh, H. Salimijazi, and A. F. Tehrani. Etch assisted ultrasonic floating abrasion process, a new concept for material removal. Microelectronic Engineering, Vol. 129, No. 12, 2014, pp. 31–37.10.1016/j.mee.2014.06.023Search in Google Scholar

[5] Bisaria, H. and P. Shandilya. Surface integrity aspects for NiTi shape memory alloys during wire electric discharge machining: A review. Journal of Materials Research, Vol. 35, No. 6, 2020, pp. 537–558.10.1557/jmr.2020.32Search in Google Scholar

[6] Sun, Y., Y. D. Gong, and Y. Liu. Experimental research and parameter optimization for low speed wire electrical discharge machining TC4. Journal of Northeastern University (Natural Science), Vol. 37, No. 7, 2016, pp. 979–983.Search in Google Scholar

[7] Gong, Y. D., X. T. Ma, and Y. Sun. Experimental study on the wire electrical discharge machining of rotary parts based on the response surface method. Journal of Northeastern University (Natural Science), Vol. 39, No. 7, 2018, pp. 1005–1010.Search in Google Scholar

[8] Sarkar, S., S. Mitra, and B. Bhattacharyya. Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy. Journal of Materials Processing Technology, Vol. 159, No. 3, 2005, pp. 286–294.10.1016/j.jmatprotec.2004.10.009Search in Google Scholar

[9] Kiyak, M. and O. Cakir. Examination of machining parameters on surface roughness in EDM of tool steel. Journal of Materials Processing Technology, Vol. 191, No. 1–3, 2007, pp. 141–144.10.1016/j.jmatprotec.2007.03.008Search in Google Scholar

[10] Mahapatra, S. S. and A. Patnaik. Optimization of wire electrical discharge machining (WEDM) process parameters using taguchi method. International Journal of Advanced Manufacturing Technology, Vol. 34, No. 9–10, 2007, pp. 911–925.10.1007/s00170-006-0672-6Search in Google Scholar

[11] Bisaria, H. and P. Shandilya. Wire electric discharge machining induced surface integrity for Ni (55.95) Ti (44.05) shape memory alloy. Proceedings of the Institution of Mechanical Engineers Part E-journal of Process Mechanical Engineering, Vol. 235, No. 2, 2020, pp. 178–185.10.1177/0954408920951146Search in Google Scholar

[12] Bisaria, H. and P. Shandilya. Surface integrity of Ni-Rich NiTi shape memory alloy at optimized level of wire electric discharge machining parameters. Journal of Materials Engineering and Performance, Vol. 28, No. 12, 2019, pp. 7663–7675.10.1007/s11665-019-04477-2Search in Google Scholar

[13] Manoj, I. V., H. Soni, S. Narendranath, P. M. Mashinini, and F. Kara. Examination of machining parameters and prediction of cutting velocity and surface roughness using RSM and ANN using WEDM of Altemp HX. Advances in Materials Science and Engineering, Vol. 2022, 2022, id. 5192981.10.1155/2022/5192981Search in Google Scholar

[14] Nas, E., O. Ozbek, F. Bayraktar, and F. Kara. Experimental and statistical investigation of machinability of AISI D2 steel using electroerosion machining method in different machining parameters. Advances in Materials Science and Engineering, Vol. 2021, 2021, id. 1241797.10.1155/2021/1241797Search in Google Scholar

[15] Ayyildiz, E. A., M. Ayyildiz, and F. Kara. Optimization of surface roughness in drilling medium-density fiberboard with a parallel robot. Advances in Materials Science and Engineering, Vol. 2021, 2021, id. 6658968.10.1155/2021/6658968Search in Google Scholar

[16] Bleys, P., J. P. Kruth, B. Lauwers, B. Schacht, V. Balasubramanian, L. Froyen, et al. Surface and sub-surface quality of steel after EDM. Advanced Engineering Materials, Vol. 8, No. 1–2, 2006, pp. 15–25.10.1002/adem.200500211Search in Google Scholar

[17] Somashekhar, K. P., S. Panda, J. Mathew, and N. Ramachandran. Numerical simulation of micro-EDM model with multi-spark. International Journal of Advanced Manufacturing Technology, Vol. 76, No. 1–4, 2013, pp. 83–90.10.1007/s00170-013-5319-9Search in Google Scholar

[18] Wang, L. H., Y. Z. Zhang, G. X. Shen, and Z. Q. Wang. Optimization of machining parameters of WEDM based on weighted mean method. Machine Tool & Hydraulics, Vol. No.17, 2007, pp. 81–83.Search in Google Scholar

[19] Feng, Q. B. and J. J. Zhou. Study on influence factors of surface quality in lswedm processing. Machinery Design & Manufacture, Vol. No.8, 2010, pp. 185–186.Search in Google Scholar

[20] Hong, Q., Y. Y. Shi, D. N. Lu, and Y. M. Guo. Multi-response parameter optimization for the composite tape winding process based on grey relational analysis and response surface methodology. Acta Materiae Compositae Sinica, Vol. 12, No. 36, 2019, pp. 2822–2832.Search in Google Scholar

[21] Shen, M. X., X. F. Xue, and X. S. Zhang. Determination of discrimination coefficient in grey incidence analysis. Journal of Air Force Engineering University (Natural Science Edition), Vol. 1, 2003, pp. 68–70.Search in Google Scholar

[22] Kong, L., X. Chen, and Y. Du. Evaluation of the effect of aggregate on concrete permeability using grey correlation analysis and ANN. Computers and Concrete, Vol. 17, No. 5, 2016, pp. 613–628.10.12989/cac.2016.17.5.613Search in Google Scholar

[23] Gao, X. P., Z. X. Qin, H. T. Zhu, and B. W. Sun. Multi-objective optimization of side inlet/outlet shape based on response surface model. Journal of Huazhong University of Science and Technology (Natural Science Edition), Vol. 47, No. 11, 2019, pp. 127–132.Search in Google Scholar

[24] Yang, N. N., P. K. Das, and X. L. Yao. Application of response surface method for reliability analysis of stiffened laminated plates. Ships and Offshore Structures, Vol. 10, No. 6, 2015, pp. 653–659.10.1080/17445302.2014.943388Search in Google Scholar
Received: 2021-10-11
Revised: 2022-04-04
Accepted: 2022-05-06
Published Online: 2022-07-19

© 2022 Jinglan Guo et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/htmp-2022-0037
Keywords for this article
LS-WEDM; TC4; grey correlation analysis; surface response method; electrical parameter optimization
Creative Commons
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