Relational analysis is one of the most popular methods for making decisions based on multiple criteria. One of the important and useful features is the ability to measure quantitative and qualitative relationships between variables and factors using relatively small amounts of data. A GRA was conducted to determine the optimal process variables that combine the multiple objectives of minimizing the wear rate and COF of the AA7075-SiC-Gr composite surface. The smaller it is, the better value was selected as the objective function for the wear rate and COF of the AA7075-SiC-Gr surface composite. Gray relationship coefficient (GRC) and gray relationship degree (GRG) were obtained using the standard GRA formula. GRG can obtain the best combination of control parameters and the contribution of each experimental parameter combination. In Table 1, the combination of minimum wear rate and COF is obtained at 12 for the maximum value of GRA, which represents the objective of the optimal combination. Figure 3 plots between input parameters and GRG imported from GRA. It represents the most realistic parameters to reduce wear rate and COF. As shown in Table 2, the S/N ratio and its delta value results were used to rank the wear parameters and investigate their impact on the combined objective function. The order of influencing factors from high to low is determined as load, reinforcement. And the sliding speed and load of GRG have the strongest influence on the composite objective function among other wear parameters. Similarly, the COF of the composites affected the order of reinforcement amount, load and sliding speed of the GRG.
Table 1
GRA values of AA7075-SiC-Gr surface composites
S.No | Normalization | Deviation Sequence | GRC | GRG | Rank |
Wear rate | COF | Wear rate | COF | Wear rate | COF |
1 | 0.085 | 0.285 | 0.914 | 0.714 | 0.353 | 0.411 | 0.382 | 12 |
2 | 0.490 | 0.5 | 0.509 | 0.5 | 0.495 | 0.5 | 0.497 | 7 |
3 | 0.764 | 0.821 | 0.235 | 0.178 | 0.679 | 0.736 | 0.708 | 3 |
4 | 0.840 | 0.428 | 0.159 | 0.571 | 0.758 | 0.466 | 0.612 | 4 |
5 | 0.047 | 0.178 | 0.952 | 0.821 | 0.344 | 0.378 | 0.361 | 14 |
6 | 0.417 | 0.25 | 0.582 | 0.75 | 0.461 | 0.4 | 0.430 | 10 |
7 | 0.579 | 0.571 | 0.420 | 0.428 | 0.543 | 0.538 | 0.540 | 6 |
8 | 0.933 | 0.678 | 0.066 | 0.321 | 0.882 | 0.608 | 0.745 | 2 |
9 | 0.035 | 0.107 | 0.964 | 0.892 | 0.341 | 0.358 | 0.350 | 15 |
10 | 0.340 | 0.285 | 0.659 | 0.714 | 0.431 | 0.411 | 0.421 | 11 |
11 | 0.455 | 0.357 | 0.544 | 0.642 | 0.478 | 0.437 | 0.458 | 8 |
12 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 |
13 | 0 | 0 | 1 | 1 | 0.333 | 0.333 | 0.333 | 16 |
14 | 0.277 | 0.035 | 0.722 | 0.964 | 0.408 | 0.341 | 0.375 | 13 |
15 | 0.464 | 0.25 | 0.535 | 0.75 | 0.483 | 0.4 | 0.441 | 9 |
16 | 0.761 | 0.321 | 0.238 | 0.678 | 0.676 | 0.424 | 0.550 | 5 |
Table 2
Response table for S/N ratio of GRG
S/N ratio of wear rate |
Level | wt.% of SiC | Load | Sliding velocity |
1 | 4.622 | 9.293 | 6.383 |
2 | 5.591 | 6.972 | 6.021 |
3 | 6.361 | 5.343 | 5.388 |
4 | 6.756 | 1.721 | 5.537 |
Delta | 2.134 | 7.572 | 0.996 |
Rank | 2 | 1 | 3 |
S/N ratio of COF |
Level | wt.% of SiC | Load | Sliding velocity |
1 | 4.095 | 8.647 | 7.573 |
2 | 6.522 | 7.755 | 7.260 |
3 | 7.602 | 5.792 | 6.299 |
4 | 8.571 | 4.595 | 5.657 |
Delta | 4.475 | 4.053 | 1.917 |
Rank | 1 | 2 | 3 |
In Fig. 4, the main effect plot for GRG wear rate shows that to obtain a high wear rate, the optimal process parameters should be a high level for reinforcement weight percentage, a low level for load, and a medium level for sliding velocity. Furthermore, it is also observed from the main effect plot for GRG wear rate that sliding velocity has the strongest influence on GRG followed by load and amount of reinforcement. Figure 5 shows that the main effect plot of GRG is followed to obtain minimum COF, reinforcement is more significant followed by load and sliding velocity. From Figs. 6 and 7 shows the interaction plots for GRG wear rate and COF respectively, it is seen that the interaction of reinforcement weight percentage with load is significant for lower loads and insignificant at higher loads. Percentage of weight of reinforcement with load Percentage of weight of reinforcement with sliding velocity Interactions of load with sliding velocity are found to be negligible as the interaction lines are almost parallel.
In the contour plots, the contour with higher value of GRG provides the best range of parameters to attain the combined objective. Contour plots show the range of GRG against (i) load, reinforcement; (ii) sliding velocity, reinforcement; (iii) Sliding velocity, load for wear rate and COF. Figure 8 (a-c) shows the range of wear rate against (a) load, reinforcement; (b) sliding velocity, reinforcement; and (c) sliding velocity, load. It is apparent even higher reinforcement content does not significantly contribute to wear resistance at higher loads as wear mechanisms like abrasive ploughing, severe plastic deformation, and delamination that occurs at very high loads which reduces the wear resistance. Figure 9 (a-c) shows the range of COF against (a) load, reinforcement; (b) sliding velocity, reinforcement; and (c) sliding velocity, load. This shows that the GRG is high in the range of 0.7–0.8 for high sliding velocity at both low and high loads. The formation of oxides in the higher sliding velocity takes place even at extremities of load conditions which cultivates the wear resistance and decreases the COF.
A regression equation is an algebraic representation of a regression line used to describe the relationship between output and input variables. The relationship between wear rate and COF and process parameters is determined by multiple regression analysis. It has been used in industry to understand how standard prices affect predictable variables. It is necessary to verify that the regression model fits the analyzed data set. The relationship between processing parameters and output response was obtained by multiple linear regression using Minitab 17 software. The following regression equation was fitted for wear rate and COF with R-Sq values of 90.96% and 85.16%..
GRG of Wear rate = 0.1543 − 0.01051 R + 0.01555 L + 0.0311 S (1)
GRG of COF = 0.2703 − 0.01850 R + 0.00878 L + 0.0533 S (2)
Where
R - Reinforcement in wt. % (0, 5, 10, 15)
L - Load in N (10, 20, 30, 40)
S - Sliding velocity in m/s (1, 2, 3, 4)
Corrosion Studies
Table 3
Polarization results of AA7075-SiC-Gr surface composites
Specimens | Ecorr ( mV vs SEC) | Icorr (µA/cm2) | Corrosion rate (mm/year) |
AA7075 | -772.214 | 5.335 | 2.636 |
0% SiC | -799.631 | 4.805 | 1.925 |
5% SiC | -805.574 | 3.182 | 1.893 |
10% SiC | -850.179 | 1.314 | 0.925 |
15% SiC | -817.645 | 2.733 | 1.228 |
Potentiodynamic polarization and electrochemical impedance spectroscopy tests were performed on AA7075-SiC-Gr surface composites in 3.5% NaCl solution. The electrochemical impedance spectroscopy of the surface composites was recorded for three hours to allow for the contribution of oxide film to the corrosion resistance on AA7075-SiC-Gr surface composites. The polarization curves for AA7075-SiC-Gr surface composites are shown in Fig. 10 (a-e). Corrosion parameters, corrosion potential and corrosion current density were calculated, and the corrosion rate was calculated using Faraday's law and the values are shown in Table 3. The observed result indicates that reinforcing 10% SiC and 5% Gr surface composite has high corrosion resistant in base materials. In the case of passive metals such as aluminium, the mechanically unaffected metal surface is usually protected by an oxide film. During corrosion, the used surface and the protected surface form a galvanic connection. Removing metal particles and passive film is called depassivation. Potential shift to lower value due to the removed passive film. This reduced area is exposed to electrolyte at a higher rate (using accelerated corrosion) until another passive film is formed, known as repassivation. The elements in the intermetallic phases are oxidised to yield MgO, SiO2 and FeO which decreases the rate of corrosion in composites. Nyquist plots of the AA7075-SiC-Gr surface composites were viewed by spectroscopy as shown in Fig. 11 (a-d) respectively. It is observed that development of the surface oxide film formation due to rise in diameter of the capacitance arc with respect to increase in reinforcement of composites from the Nyquist plots. From the studies, it is evident that the charge transfer controls the corrosion process of the composites.