Parameter optimization for wire-cut electrical discharge machining of stir cast AA6063 alloy/SiC (black and green) using Taguchi method with grey relational analysis
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Introduction
Composite is a term used when two or more unlike materials are mixed or combined, with the subsequent product having much better properties than each of the precursor materials [1]. One such kind of composite is the metal matrix composite (MMC), in which the metal constitutes the matrix and required reinforcements are added to the matrix to enhance the essential properties of the composite [2]. Because of the enhanced properties of MMCs compared with their pristine form, they have a wide range of applications in varied industries, such as marine vessels [3], structural engineering [4], manufacturing of engineering goods [5], machining industries [6], automobiles [7], aerospace [8], etc. From this, it is clear that MMCs are widely useful in applications wherein sturdy mechanical properties constitute the major requirement. With the drastic advancement in the industrial sector, there are numerous reports emphasizing the enhancement of the mechanical behavior of metals, especially aluminum (Al) and its alloys. Al and its alloys are known for their low-density nature, but they have high strength comparable to that of steel [9]. Besides, Al has natural corrosion resistance behavior, whereby the Al2O3 film formed over the surface acts as a good passivation layer, preventing further corrosion [10]. Recently, due to the advancements in industrial technology and its subsequent applications, there is a requirement to improve the mechanical properties of Al, which can be made possible using MMCs. Therefore, researchers and scientists have been trying to reinforce the Al matrix with suitable materials without deteriorating the former's inherent properties. One such reinforcement is the silicon carbide (SiC) particle. SiC particle is most widely reported due to its low cost and wide availability and because it imparts high strength to the matrix owing to its high Young's modulus [11]. Further, SiC particles are known for their lightweight property [12], and therefore, when reinforced in aluminum alloys, they can effectively improve the Young's modulus without deteriorating the lightweight applications. Hu et al. [13] fabricated Al-based MMCs by using SiC particles to reinforce A356 and 6061 alloys, facilitated by vacuum-assisted high-pressure die casting. The tensile strength of A356 and 6061 alloys reinforced with SiC particles were found to be 286 MPa and 246 MPa. Likewise, Mousavian et al. [14] fabricated Al-SiC composite using the hot extrusion process. Other metallic constituents, such as copper, chromium, and titanium, were also supplemented separately along with the composite. In all the cases, the yield strength was higher than that of the A356 alloys, substantiating the fact that SiC improves the yield strength of the Al matrix. Similarly, Fenghong et al. [15] prepared Al6061 composites with the introduction of SiC and WC. The presence of reinforcing materials improved the hardness, tensile strength, and wear resistance of the hybrid composites. Even though many reports in the literature have dealt with the reinforcement of the Al matrix with SiC, while fabricating the Al-SiC composite through the stir casting technique, the Al4C3 phase is formed, which deteriorates the performance of the composite by converting it to Al(OH)3 in the presence of atmospheric moisture [16]. In the commercial market, SiC is available in green (SiCg) and black (SiCb) grades, which depend upon the preparation technique. SiCg and SiCb have different mechanical properties as the percentage of SiC in each material is different; SiCg has a higher percentage of SiC than SiCb. Therefore, it is clear that SiCb has more SiO2 in its composition. Moreover, it has been reported that the presence of SiO2 can considerably reduce the formation of the Al4C3 phase, resulting in a better microstructure and impact strength of the Al-SiC composite [16].
Even though SiCb is reported to be the apt reinforcement material for Al and its alloys, reports dealing with a comparative study of the machining property of SiCg- and SiCb-reinforced Al matrixes are very limited. Further, it has been reported that if the reinforcement is harder than the matrix material, then the reinforcement induces the precipitation hardening of the metal matrix [17]. Precipitation hardening is nothing but the obstruction of the dislocation movement of the metal matrix by the impurity phase, resulting in the reduction of plastic deformation. Upon improvement in the strength of the Al MMCs, it is difficult to process or machine the MMCs similar to that of the pristine Al. Therefore, the machining parameters have to be changed or optimized according to the reinforcement and matrix materials.
One of the industrially used machining processes is wire-cut electrical discharge machining (WEDM), wherein unwanted materials are removed by erosion triggered by spark discharges to obtain the desired shape [18]. WEDM is widely used for machining MMCs due to its efficient processing capabilities toward brittle and hard materials, providing control over complex shapes and designs [19, 20]. Owing to its ability to develop intricate structures that otherwise are not possible, it has applications in various industrial divisions. But the machining parameters have a substantial influence on the production rate and quality of the machined products. In this context, to carry out an efficient machining process, the processing parameters have to be optimized for each MMC because the reinforcement, when added to the metal matrix, enhances the mechanical behavior of the resultant MMC. The primary processing parameters of WEDM include applied voltage, flushing pressure (FP), pulse on and off time, wire feed (WF) rate, dielectric medium, etc. [21]. Manually, optimizing all the processing parameters requires a large number of experiments, which is a tedious and time-consuming process. Therefore, computational techniques are most widely preferred to optimize the process parameters since they require limited experiments, are time efficient, and are cost-effective. In this regard, many results for the optimization of WEDM parameters for the machining of Al and other alloys have been reported. Nguyen et al. [22] utilized Taguchi-based grey relational analysis to optimize the machining process of high-chromium steel and elucidated the optimum parameters, such as peak current, gap voltage, pulse on time, and pulse off time, on responses such as material removal rate (MRR), surface roughness, microhardness, and average white layer thickness. Selvakumar et al. [23] optimized the WEDM parameters of machining 5083 Al alloy, such as pulse on time, pulse off time, peak current, and wire tension using Taguchi design (L9 orthogonal array) for responses such as maximum cutting speed and minimum surface roughness. Mandal et al. [24] utilized Taguchi grey relational analysis method to optimize the machining parameters of WEDM against Al 7075 alloy. The WEDM input parameters, such as pulse on and pulse off times, FP, and applied voltage were studied to analyze the responses, such as machining speed and corner inaccuracy. Through grey relational analysis, a set of parameters that show the highest ranking was elucidated. Further, the confirmational experimental results coincide with the values predicted from regression equations.
In this context, the present work focuses on the preparation of AA6063/SiCg and AA6063/SiCb composites through the stir casting process, and the influence of the components on the WEDM processing parameters is evaluated. The machining parameters such as pulse on time, WF rate, and FP are optimized for each SiC type through the Taguchi method with grey relational analysis, and the the process outcomes are experimentally evaluated.
Material and methods
Materials required
The aluminum alloy AA6063 in rod form was obtained from Parshwamani Metals, Mumbai, Maharashtra, India. The material composition of AA6063 includes Al (≤97.5 wt.%), Si (0.20–0.60 wt.%), Mg (0.45–0.90 wt.%), Cu (≤0.10 wt.%), Mn (≤0.10 wt.%), Fe (≤0.35 wt.%), Ti (≤0.10 wt.%), Cr (≤0.10 wt.%), Zn (≤0.10 wt.%), and others (≤0.15 wt.%). SiCg and SiCb of size 100 μm, used as reinforcement, were obtained from Parshwamani Metals. SiCg has >97% SiC, whereas SiCb has >95% SiC.
Fabrication of composites using stir casting technique
In the present work, 100-μm-sized SiCb and SiCg particles were added separately to the AA6063 aluminum alloy matrix as reinforcement through the stir casting technique. First, the AA6063 matrix is melted in a graphite crucible at 850°C. The melted AA6063 was stirred using a graphite stirrer for 45 min at 250 rpm. SiCb and SiCg were added separately to the melted AA6063. Before the addition, the SiC particles were preheated to 250°C to enable even mixing in the melted AA6063. The preheating process along with the stir casting process enabled good wetting and uniform distribution of the SiC particles in the molten AA6063. After a successful stir casting process, the molten composite was poured into a mold and cooled at room temperature; the blocks were removed and used for conducting experiments on WEDM.
WEDM process
The WEDM machining process was performed using the Agie Charmilles (Biel, Switzerland) WEDM machine. Even though many parameters affect the machining of a composite using WEDM, the present work emphasizes the parameters such as pulse on time (TON), WF rate, and FP, which are considered the input variables. TON is an important parameter since anodic dissolution happens during the TON stage and, therefore, material removal occurs at this stage. The WF rate also directly influences the material removal process, but it is affected by the parameter FP. When the WF rate is high, the material is eroded rapidly, which affects the surface finish, but with high FP, the eroded materials can be efficiently cleared from the machining surface, leading to a better surface finish. Therefore, WF rate and FP were included as the process parameters for the optimization process. Other important parameters are pulse off time (TOFF), voltage, and current. Optimum TOFF is required to remove all the dissolved products from the machining area with running water and this also restricts the increase in temperature of the work-piece. However, high TOFF leads to an unnecessary increase in the machining time, and therefore, TOFF was kept constant at 50 μs. Likewise, the voltage and the current were kept constant for the experimental studies as 60 V and 6 A, respectively. Table 1 displays the input parameters and their levels for designing an L9 orthogonal array for machining both AA6063/SiCg and AA6063/SiCb composites. The input parameters were set as per the preliminary experimental results, where higher values of TON and WF rate lead to a decline in the surface finish, while lower TON and WF rate result in negligible change. The lower and higher levels of FP were selected in such a way that beyond the set point, the change in results was insignificant. Using an orthogonal array, the experimental size can be considerably reduced.
Input machining parameters for both AA6063/SiCg and AA6063/SiCb composites and their levels
Factor
Symbol
Units
Level
Level 1
Level 2
Level 3
Pulse on time
TON
μs
50
75
100
Wire Feed
WF
m/min
6
12
18
Flushing Pressure
FP
MPa
1
2
3
Here, AA6063/SiCg or AA6063/SiCb of dimension 200 mm × 80 mm × 20 mm (length × breadth × height) was used as the workpiece, whereas brass wire with a diameter of 0.25 mm was used as the machining wire electrode. Deionized water was selected as the dielectric medium for all the experimental trials. The workpiece was fixed using a clamp to avoid any kind of disturbances during the WEDM process. Other machining parameters, such as the temperature of the dielectric medium and angle of machining, were kept constant at 27°C and 90°, respectively. The machining was performed along a 20-mm-diameter region for all the samples. During machining, the continuous flow of the dielectric medium was ensured. Through the Taguchi method, an L9 orthogonal array was constructed using three factors and three levels using Minitab statistical software (Table 2). As per the experimental design, WEDM processes were carried out for both AA6063/SiCg and AA6063/SiCb. From each experimental analysis, the MRR and surface roughness (Ra) were calculated. The MRR of the machining process was evaluated through the following equation [25]:
MRR=\frac{W_{i}-W_{f}}{t\times\rho}
where the initial and final weights of the samples are denoted as Wi and Wf, t represents the time, and ρ is the density of the material. The Ra values of the machined samples were evaluated using the surface roughness tester (SJ-410; Mitutoyo, Aurora, United States). Here, a 2-μm tip inclined at 60° was used to analyze the surface. The surface was scanned for 2 mm at a speed of 10 mm/min. Three measurements were carried out at random positions. The complete profile was then leveled using the least-squares method and filtered using the Gaussian profile filter setting the cutoff length as 0.8 mm. For better precision, each experiment was conducted thrice, and the average value is provided. In the machining process through WEDM, higher MRR and lower Ra constitute efficient performance of the process and, therefore, we selected the same for the grey relational analysis. Grey relational analysis was performed to construct a single response from the actual multiple responses. This technique enables easy grading of parameters according to the final single constructed response. To perform this task, at first, the responses were normalized, wherein the equation for lower-the-better strategy is expressed as follows [26],
X_{i}\left( k\right) =\frac{\max {x_{i}\left( k\right) -x_i(k)}}{\max x_{i}\left( k\right) -\min x_{i}\left( k\right)}
Experimental design for L9 orthogonal array and the experimental data
Sl. no.
TON
WF
FP
AA6063/SiCb
AA6063/SiCg
MRR-1 (mm3/min)
Ra-1 (μm)
MRR-2 (mm3/min)
Ra-2 (μm)
1
50
6
1
8.57
1.88
8.81
1.82
2
50
12
2
9.09
1.59
9.27
1.63
3
50
18
3
9.78
1.11
9.94
1.24
4
75
6
2
10.51
1.65
10.83
1.69
5
75
12
3
11.23
1.67
11.19
1.81
6
75
18
1
11.86
2.45
11.98
2.40
7
100
6
3
13.25
2.56
13.05
2.72
8
100
12
1
13.52
3.09
13.66
3.05
9
100
18
2
13.61
3.10
14.13
3.17
MRR-1 and Ra-1 = MRR and surface roughness of AA6063/SiCb; MRR-2 and Ra-2 = MRR and surface roughness of AA6063/SiCg.
All the experiments were conducted thrice, and the average values are provided.
FP, flushing pressure; MRR, material removal rate; TON, pulse on time; WF, wire feed.
For larger-the-better strategy, the expression is as follows:
X_{i}\left( k\right) =\frac{x_{i}{\left( k\right)- {\min x}_i(k)}}{\max x_{i}\left( k\right) -\min x_{i}(k)}
where Xi (k) is the normalized value, max xi (k) and min xi (k) are the maximum and minimum of the experimental values, xi(k) is the experimental value, and k is the position of the response. After obtaining the normalized value, the grey relational coefficient was evaluated through the following equation [26]:
{\xi{}}_{i}\left( k\right) =\frac{\Delta{}\min +\Psi{}\Delta{}\max}{\Delta{}0_{i}\left( k\right) +\Psi{}\Delta{}\max}
where Δ0i (k) = ‖xi (k) − xi (k)‖, Δmin and Δmax are the minimum and maximum values of Δ0i, and Ψ is the distinguishing coefficient whose value is taken as 0.5. The grey relational grade (GRG) was calculated using the following expression [26]:
{\gamma{}}_{i}=\frac{1}{n}\sum_{k=1}^{n}{\xi{}}_{i}(k)
Here, n denotes the number of responses. From the GRG values, the highest values were chosen as the optimal parameters for better performance of the WEDM process.
Characterization techniques
The crystalline phases of SiCb, SiCg, and Al were evaluated through the X-ray diffraction (XRD) pattern recorded using an X-ray diffractometer (D8 Advance ECO XRD Systems; Bruker, Massachusetts, United States of America). The morphologies of SiCb and SiCg were observed from the scanning electron microscopic (SEM) images (EVO 18, Carl Zeiss, Oberkochen, Germany). The elemental constitution over the machined surfaces of SiCb- and SiCg-reinforced AA6063 was examined through energy-dispersive X-ray spectroscopic (EDX) mapping analysis. Further, the elemental composition of the debris was also analyzed using EDX.
Results and discussion
The crystalline phases of SiCb, SiCg, and AA6063 were determined through XRD analysis and are shown in Figures 1A–1C. Figures 1A and 1B display the diffraction patterns of SiCb and SiCg, respectively, where the peaks matched well with the standard Joint Committee on Powder Diffraction Standards (JCPDS) no. 00-001-1118. Apart from the SiC peaks, the diffraction patterns display impurity phases attributed to the fact that the obtained SiCb and SiCg are not 100% pure. The impurities may have been introduced during the preparation stage. Further, another noteworthy feature is that the intensities of the impurity phases are lower in the case of SiCg compared to the same for SiCb, substantiating the fact that a higher percentage of SiC phase is present in SiCg than in SiCb. Figure 1C shows the XRD pattern of AA6063, wherein the diffraction peaks corresponding to Al are validated through JCPDS No. 01-089-2769 [27]. From the diffraction pattern, the crystal system of the Al phase is estimated as cubic, with a space group of Fm-3m. Even though alloying elements such as silicon, magnesium, etc. are present in AA6063, their presence is not seen in the diffraction pattern, which validates the completion of the alloying process.
Fig. 1
XRD patterns of (A) SiCb, (B) SiCg, and (C) AA6063. XRD, X-ray diffraction
The morphological properties of the purchased SiCb and SiCg were assessed from the SEM micrographs, which are provided in Figures 2A and 2B. From the images, it is clear that the SiC particles are in the size of ~100 μm, with irregularly shaped blocks. Further, SiCg shows sharp edges compared to SiCb, suggesting the extra-brittle nature of SiCg.
Fig. 2
SEM images of (A) SiCb and (B) SiCg. SEM, scanning electron microscopy
After the stir casting process, wherein SiCb and SiCg were added to AA6063 as reinforcements separately, they were subjected to the WEDM process. The WEDM process was carried out according to the constructed L9 orthogonal array, whereby the MRR and Ra of AA6063/SiCg and AA6063/SiCb were calculated, as summarized in Table 2. From the table, it is inferred that both MRR-1 and MRR-2 increase as TON increases, indicating direct proportionality of the response. Likewise, as TON increases, the values of Ra-1 and Ra-2 increase, which is not the required criterion, since for Ra, the strategy “smaller the better” is applicable. Similar results were obtained by Pramanik et al. [28], whereby an increase in TON directly influenced the MRR but had a negative impact on Ra. As TON increases, the machining time increases, leading to higher MRR. In contrast, high TON leads to the generation of large thermal energy, resulting in a large crater and this causes the Ra to increase. MRR-1 and MRR-2 display the same trend for the increase in WF rate as that of TON, indicating that higher WF rate is essential for good MRR. Kapoor et al. [29] also obtained the same results, whereby the MRR increased with WF rate. However, the values of both Ras do not tend to have a particular proportionality with the increase in WF rate. This fact is attributed to the interference by the parameter FP, whereby the machining debris is easily removed if the FP is higher, resulting in a reduced Ra value. Therefore, all the parameters are intertwined with each other, and the most influential parameters can be predicted using the signal-to-noise (S/N) ratios for all the responses.
Figures 3A–3D display the influence of the input parameters on the S/N ratios of MRR-1, Ra-1, MRR-2, and Ra-2, respectively, where the strategy “larger is better” is applied for MRRs and “smaller is better” is applied for Ras. From Figure 3A, it is inferred that MRR-1 is influenced by the input parameters in the order TON>WF rate > FP. Likewise, the S/N ratios of MMR-2 also display the same pattern of parameter influence (Figure 3C). As the TON is increased, the heat generated due to the electrical discharge becomes high, leading to the melting and evaporation of the base metal, constituting better MRR. Following TON, the WF rate influences the WEDM process to yield better MRR. During the WEDM process, the electrical discharge from the machining wire tends to remove the materials, making the space vacant. Therefore, for continuous machining, the wire is fed along the direction of machining. Hence, the WF rate constitutes an important parameter for MRRs, whereby a low rate may cause dampened MRR and a high rate may hinder the machining performance. The dielectric medium (parameter FP) is used to keep the temperature under control and remove all the debris from the machining area. Therefore, its pressure determines how far the temperature is controlled and how well the debris is removed. For MRR-1 and MRR-2, the parameter FP displays the least influence, validating that not much temperature difference is caused due to different FPs. Further, the debris removal process does not have a significant effect on the MRR. Figures 3B and 3D show the effect of the input parameters on the S/N ratio of Ra-1 and Ra-2, respectively. For both Ras, the input parameters that influence the S/N ratio are in the order TON>FP >WF rate. It is obvious that as TON increases, the MRR increases, leading to high Ra. However, for better machining performance, the Ra should be minimum, and therefore, high TON has a negative impact on Ra. Unlike the S/N ratio of MRR, FP has a significant impact on Ra. This may be due to the efficient removal of debris from the machining area with high FP. If the debris is not removed efficiently, the debris itself melts during the WEDM process, forms a layer over the machined surface, and solidifies, constituting a recast layer. This recast layer will not be a smoothened surface but is rough, which tends to increase the Ra value of the machined surface. Therefore, as the FP increases, the Ra tends to reduce. Ra is least affected by the WF rate. However, a high WF rate constitutes high MRR and should result in a high Ra. But, due to the influence of FP, the influence of the WF rate becomes a combined effect.
Fig. 3
Influence of the input factors on the S/N ratio of (A) MRR-1, (B)Ra-1, (C) MRR-2, and (D) Ra-2. MRR-1, material removal rate; Ra, surface roughness; S/N, signal-to-noise
The analysis of variance (ANOVA) for the responses MRR-1, Ra-1, MRR-2, and Ra-2 are summarized in Table 3. For MRR-1 and MRR-2, the input parameter TON contributed significantly, i.e., 94.2% and 93.4%. Following TON, the WF rate showed 4.8% and 6.5% contribution towardMRR-1 and MRR-2, respectively; FP was the insignificant parameter, as presented in the above discussion. In all the cases, the error was < 5%, and therefore, the confidence level is >95%. From Table 2, it is inferred that the process parameters with high TON (100 μs) and WF rate (18 m/min) display high MRR-1 (3.61 mm3/min) and MRR-2 (14.13 mm3/min). For Ra-1 and Ra-2, the input parameter TON contributed 77.1% and 84.8%, respectively. The FP influences the Ra more than the WF rate, contributing 18.1% and 10% for Ra-1 and Ra-2, respectively. For the Ras, the WF rate has the least significant contribution. From Table 2, it is to be noted that Ra-1 (1.11 μm) and Ra-2 (1.24 μm) have the least value when TON is the least (50 μs) and FP is the highest (3 MPa). Here, the WF rate is found to be the highest (18 m/min) but does not have a significant contribution. Therefore, the input parameter FP is insignificant to the response MRR although displaying a significant effect on Ra.
ANOVA for MRR-1, Ra-1, MRR-2, and Ra-2
Response
Source
DF
Adj. SS
F-value
P-value
MRR-1R2 = 99.65%
TON
2
27.9286
268.49
0.004
WF
2
1.4216
13.67
0.068
FP
2
0.1940
1.87
0.349
Error
2
0.1040
Total
8
29.6483
Ra-1R2 = 96.64%
TON
2
3.07616
22.98
0.042
WF
2
0.05429
0.41
0.712
FP
2
0.72142
5.39
0.157
Error
2
0.13389
Total
8
3.98576
MRR-2R2 = 97.82%
TON
2
27.4332
1818.10
0.001
WF
2
1.8955
125.62
0.008
FP
2
0.0138
0.91
0.523
Error
2
0.0151
Total
8
29.3575
Ra-2R2 = 96.26%
TON
2
3.19647
22.69
0.042
WF
2
0.05627
0.40
0.715
FP
2
0.37520
2.66
0.273
Error
2
0.14087
Total
8
3.76880
Adj., adjusted; ANOVA, analysis of variance; DF, degrees of freedom; F, F-ratio; FP, flushing pressure; MRR-1, material removal rate-1; P, probability; Ra, surface roughness; SS, sums of squares; TON, pulse on time; WF, wire feed.
The response function for the responses influenced by the input parameters is compiled and is expressed as follows:
\begin{align}MRR1&=3.722+0.08627\ T_{ON}\\ &+0.0811\ WF+0.052\ FP\end{align}\begin{align}MRR2&=3.989+0.08547\ T_{ON}\\ &+0.09333\ WF-0.0450\ FP\end{align}\begin{align}R_{a1}&=0.541+0.02780\ T_{ON}\\ &+0.0158\ WF-0.347\ FP\end{align}\begin{align}R_{a2}&=0.352+0.02833\ T_{ON}\\ &+0.0161\ WF-0.250\ FP\end{align}
The predicted values are comparable to the experimental values, with marginal errors (Supporting Information, Figure S1). From these data, it is clear that for high MRR, Level 3 of TON and Level 3 of WF rate are required, and for low Ra, Level 1 of TON and Level 3 of FP are required. Therefore, selecting the set of optimal parameters without compromising both MRR and Ra is a tedious task.
Grey relational analysis is a technique that measures the performance of the set of parameters as per the responses. Generally, grey relational analysis is performed when there are >1 responses. As per the formulas presented in Section 2.3, the normalized values, grey relational coefficient, and GRGs are computed for the WEDM process of both AA6063/SiCb and AA6063/SiCg and are tabulated in Tables 4 and 5. For evaluating normalized data for MRRs, the formula “higher the better” is utilized, whereas for Ra, the formula “smaller the better” is used. The grey relational coefficient is calculated using the normalized data and consequently, GRGs are computed using the grey relational coefficient. The GRGs use the influence of both the responses, i.e., MRR and Ra. The ranks are allotted as per the grades, whereby higher the grade values, better is the rank for each set of processing parameters. From Tables 4 and 5, it is clear that for both the experimental schemes, the parameters TON = 50 μs, WF rate = 18 m/min, and FP = 3 MPa display the highest GRG, i.e., Rank 1, with the responses as follows: MRR-1 = 9.78 mm3/min, Ra-1 = 1.11 μm, MRR-2 = 9.94 mm3/min, and Ra-2 = 1.24 μm. Here, to obtain an optimal machining performance, TON is found to be the least of the set values, whereas the WF rate and FP are the highest. If the TON increases, the MRR increases drastically, which leads to the formation of more recast layers, resulting in a high Ra [30]. Likewise, WF rate is also directly proportional to MRR, but the optimum WF rate is found to be high, probably to cope with the reduced value set for TON. The FP value is the highest of the set levels, which is required to flush the machined debris from the working site [31]. Furthermore, optimum FP restricts the buildup of heat on the working sample, which avoids the fusion of melted debris over the samples, which may in turn affect the overall machining process.
Grey relational analysis for the WEDM process of AA6063/SiCb
Both AA6063/SiCb and AA6063/SiCg are found to have comparable MRR and Ra values, owing to the presence of the same matrix material. However, AA6063/SiCg has a slightly higher value, probably attributed to the influence of SiCg, which is more brittle than SiCb. Therefore, SiCg is easily removed during the WEDM process, leading to higher MRR. Subsequently, the Ra also increases due to the high MRR. These facts can be validated by recording the surface morphology of the machined surface. Figures 4A and 4B show the SEM micrographs of the WEDM-processed surface of AA6063/SiCb. Here, the laminate-like structures are the recast layers of removed or melted AA6063/SiCb, where the size of the grains is higher, compared to the grain size of AA6063/SiCg (Figures 4C and 4D]. The recast layer is formed due to the inefficient flushing of the debris during the WEDM process. The material that is removed by melting solidifies to form a recast layer. Even though the FP (3 MPa) is high, the recast layer is formed. However, the grain size of the recast layer is different for AA6063/SiCb and AA6063/SiCg due to the influence of the reinforcement material. Due to the brittle nature of SiCg, they are easily removed. However, while recasting, SiCg shows a particle-like morphology over the machined surface. In contrast, SiCb forms a layer over the machined surface, providing a smooth surface. This fact can be further confirmed using the topographical texture of the SEM image computed using Gwyddion image processing software, Jihlava, Czechia. Figures 5i–iii and 5iv–vi show the surface texture profile computed from Figures 4B and 4D, respectively, at three distinct positions. The red arrow mark represents the 100-count position on the y-axis. From Figure 5, it is clear that the AA6063/SiCb machined surface has lower-intensity peaks compared to the machined surface of AA6063/SiCg, due to the influence of the different kinds of SiC particles. Therefore, to impart a smooth surface while machining using WEDM, using SiCb to reinforce AA6063 provides a better surface finish compared to the use of SiCg. Likewise, in applications where higher MMR is required, SiCg can be used as reinforcement to AA6063.
Fig. 4
SEM micrographs of WEDM-processed surfaces: (A, B) AA6063/SiCb and (C, D) AA6063/SiCg. SEM, scanning electron microscopy; WEDM, wire-cut electrical discharge machining
Fig. 5
Texture analysis from Figure 4B (i–iii) and Figure 4D (iv–vi) at three different positions using Gwyddion image processing software (the red arrow denotes the 100-count mark)
The finish of the machined surfaces of AA6063/SiCb and AA6063/SiCg can be further estimated through EDX with mapping analysis. From the Al mapping spectrum of AA6063/SiCb shown in Figure 6, it is clear that the surface is considerably even. The other elements in the composites such as Mg, SiC, and C are found to have been evenly distributed without any aggregation, resulting in low Ra-1. However, while checking the Al mapping spectrum of AA6063/SiCb, shown in Figure 7, the surface is not even and has large deformities. Likewise, other elements are also unevenly distributed, showing irregularities in the topography. In both the samples, the presence of O substantiates the fact that metallic oxides are formed during the WEDM process. Another noteworthy feature in Figures 6 and 7 is the higher atomic percentage of C compared to Si. As SiC is used as reinforcement, the atomic percentage should be the same for Si and C. However, from the obtained data, it is clear that the atomic percentage of C is 50 times higher than that of Si. This is due to the fact that C dissociates from SiC during the high-energy WEDM process and is deposited over the surface as a layer. In both the cases, i.e., for AA6063/SiCb and AA6063/SiCg, almost the same percentage of C is formed, validating the above statement. From the EDX mapping results obtained, it is clear that AA6063/SiCb has lower Ra than AA6063/SiCg for the optimized set of parameters. Therefore, in conditions where a smooth finish is a prerequisite, AA6063/SiCb can be used, whereas in conditions where MRR is the major concern, AA6063/SiCg can be used.
Fig. 6
EDX mapping analysis over the machined surface of AA6063/SiCb. EDX, energy dispersive X-ray spectroscopy; C, carbon; O, oxygen; Mg, magnesium; Al, aluminium; Si, silicon
Fig. 7
EDX mapping analysis over the machined surface of AA6063/SiCg. EDX, energy dispersive X-ray spectroscopy; C, carbon; O, oxygen; Mg, magnesium; Al, aluminium; Si, silicon
Further, the surface morphology and elemental analysis of debris generated during the WEDM process are recorded and shown in Figures 8A–8F. The images display laminar melt-cast-like structures for both AA6063/SiCb (Figures 8A and 8B) and AA6063/SiCg (Figures 8C and 8D), which are fused together due to the high temperature generated during the WEDM process. Moreover, the EDX spectra show the same trend as that of the machined surface, i.e., C is dissociated from SiC, showing a higher atomic percentage of C than of Si (Figures 8E and 8F).
Fig. 8
SEM micrograph of debris generated from (A and B) AA6063/SiCb and (C and D) AA6063/SiCg. EDX analysis of debris generated from (E) AA6063/SiCb and (F) AA6063/SiCg. EDX, energy dispersive X-ray spectroscopy; SEM, scanning electron microscopy
Conclusion
AA6063 was reinforced with 10% SiCb and SiCg separately using the stir casting technique. The stir casting technique was performed at 850°C, whereby 10 wt.% SiC was dispersed in the molten AA6063 alloy. The machining parameters, such as pulse on time, WF rate, and FP, for the responses such as MRR and surface roughness were optimized for the WEDM process using Taguchi L9 orthogonal array with grey relational analysis. From the GRG, TON = 50 μs, WF rate = 18 m/min, and FP = 3 MPa were found to be the optimum set of parameters for better machining performance. Further, confirmation experiments were performed and the machining performances were evaluated using SEM with EDX mapping analysis and texture analysis. From the obtained results, it is clear that while performing the WEDM process, AA6063/SiCb gives a better surface finish, but AA6063/SiCg provides a better MRR. Therefore, as per the requirement, SiC grade can be used as reinforcement for AA6063.
