Optimization of erosion wears of Al–Mg–Si–Cu–SiC composite produced by the PM method

2.1 AMMC production process

In the present experiment, a metallurgical powder method for processing AMMC was adopted, and metal powder reinforcement was added to the matrix element. Aluminum used as the main raw material, called matrix element and has been reinforced with Si, Mg, Cu and SiC. The composite was manufactured, followed by different metal powder selections, the weighing, the blending/mixing, the compact pressing and the sintering. AMMC was reinforced with 5 wt% of silicon-carbide in the matrix-metal powder. Silicon (Si) with 325 mesh particle size with 99.87% purity, Magnesium (Mg) with 100 mesh particle size with 99.80% purity, Copper (Cu) with 325 mesh particle size with 99.77% purity and silicon carbide (SiC) with 325 mesh particle size with 99.55% purity were blended/mixed with aluminum powder with particle size of 325 mesh and purity of 99.55%. Silicon-carbide powder particles with 5 vol% fraction are blended properly in the 91.5Al–0.5Mg–0.5Si–2.5Cu mixture. The blended/mixed powders were filled inside a steel C-45 die and compacted inside digital compression test machine at a rate of loading 0.208 kN per second and the load was given up to 521.02 MPa (250.3 kN). Then the compacted green samples were collected from the die cavity and sintered inside a muffle type furnace maintained at temperature 620 °C. It was annealed for a day i.e. 24 h. The detail production of AMMC process route is shown in Figure 1.

Figure 1:

AMMC (Al–0.5Si–0.5Mg–2.5Cu–5SiC) composite material preparation route.

2.2 Methodology

The present research addresses the corrosion and erosion behaviors of newly engineered AMMC material manufactured by powder metallurgy technique. The samples of Al–0.5Si–0.5Mg–2.5Cu–5SiC AMMCs were cut for the corrosion as well as erosion wear tests. The tests were done in conformity with ASTM G76 standards. Size of the samples (25 × 25 × 3) mm were prepared and mechanically polished with different grades emery papers. Corrosion tests were carried out in 5% NaCl solution (pH 8.37), which was prepared using standard procedures. The samples were degreased with acetone and then rinsed in distilled water before immersion in static 5% NaCl solutions in de-ionized water exposed to atmospheric air. The solution-to-specimen surface area ratio was about 150 ml/cm². The results of the corrosion test were evaluated by measuring the weight loss every day for seven days (168 h). The weight loss in mg/cm² for each sample was assessed by dividing the weight loss (measured with a digital electronic balance) by its total surface area, in accordance with ASTM G31 standard practice (Rohatgi et al. 1986). Here, the induction time period for the pit initiation of the composite was shorter than the matrix alloy. The growth rate of the oxide film in the composite was four to five times higher as compared to other researches (Deuis et al. 1997). The density of the oxide in MMC was also lower compared to the matrix metal and therefore, less resistant to diffusion of chlorine ions (Cl⁻) and subsequent localized corrosion. The 5 vol% fraction of fine SiC reinforcing particles resulted in greater resistance to active corrosion. The porosity of the gas trapped in the MMC encouraged corrosion rate by increasing the surface-area obtained for corrosion.

Similarly, sand blasting type erosion device (Figure 2) is employed for erosion wear testing where particles under the desired pressure falls on a stationary target. Erosion wear test of the AMMC materials were experimented under various operating parameters. The control parameters for the erosion wear testing were the angle of impingement/impact, impingement/impact velocity, and stand-off distance, each at four levels. Dry silica sand was used as an erosion agent i.e. erodent. Solid particles impinged the surface at a fixed velocity and, therefore, had momentum and kinetic energy, which has resulted the surface a shock effect and caused erosive wear. Samples were eroded at different angles of impingement, viz., 30°, 45°, 60° and 90°. The erodent was impinged at velocities 15, 45, 60 and 75 m/s. The samples were tested at different stand-off distance, i.e., at 10, 20, 30 and 40 mm. The range of the process parameters was selected after carrying out some pilot experiments with a fixed stand-off distance, and a detailed literature review was carried out to select the operational range of the process parameters. Table 1 presents the pilot experimental results of the erosion rate of metal matrix composite (Al–0.5Si–0.5Mg–2.5Cu–5SiC) when exposed to solid silica sand particles erosion of 10 mm fixed SOD at various impingement angles with different impingement velocities. A methodical approach popularly known as Taguchi’s L₁₆ orthogonal array (OA) design of experiments was employed as experimental plan layout for erosion testing associated with sixteen numbers of trial runs. The performance of erosion tests was evaluated in term of erosion rate. Mathematically, the erosion wear rate was formulated as:

Figure 2:

Experimental setup for erosion wear test.

The experimental design-layout and the result of trials are reported in Table 2. The constraints under which the tests were executed, is presented in Table 3.

Once the experiments were executed, the results of erosion wear rate were analyzed by various statistical tools such as, ANOVA, 3D surface effect plots, normal-probability plot associated with Anderson–Darling test, Kiviat radar diagram, and desirability plot. Additionally, eroded surface morphology was observed by implementing scanning electron microscope (JEOL, JSM-6480LV). A schematic layout of the methodologies proposed in this work, are presented pictorially in Figure 3.

Figure 3:

Schematic layout of methodology proposed.

3.1 Mechanical characterization and corrosion behaviour of AMMC

The AMMC material (Al–0.5Si–0.5Mg–2.5Cu–5SiC) manufactured by the powder metallurgy process is observed by scanning electron microscope. From Figure 4, it is seen that the mechanical properties are improved with an unchanging dispersion of reinforcing particles inside manufactured composite. The composite microstructure includes a mixture of primary alpha aluminum and primary copper along with the eutectic phase. The grain average size of the matrix element decreases with the instigation of silicon carbide. This is because the hardened particles can act as effective grain refiners. The grain size of the matrix decreased with increasing the number of reinforcing particles. The grains of the reinforced composites were purified by silicon carbide particles. The grain seeding sites are enhanced in the fabricated composites. The grains of the compounds made were cleaned, in part grain nucleation sites are enhanced in made compounds. This is due to the fact that the percentage of mass in the reinforcement phase, which prevents the free development of the α-Al grains and, in addition, refines the grains. The hardness of the AMMC (Al–0.5Si–0.5Mg–2.5Cu–5SiC) was measured by Vicker’s tester and a hardness value as 187.5 HV (i.e. at average of 10 measurements). The hardness was found to be more than that of aluminum metal and its density was recorded as 2.61 g/cm³. Thus, it was observed that the hardness of the Al metal–matrix-composite was affected its erosion wear behavior.

Figure 4:

SEM micrograph of Al–0.5Si–0.5Mg–2.5Cu–5SiC MMC.

The corrosion behavior of MMC produced with respect to aluminum was tested with 5% weight of NaCl solution. The samples of both aluminum and (Al–Mg–Si–Cu–SiC) MMC prepared were immersed in NaCl solution in a beaker and the weight loss due to corrosion was measured periodically. The corrosion weight loss on each day for MMC and cast aluminum is illustrated in Figure 5. Initially corrosion mass loss of cast aluminum was more but after seventh day mass loss of MMC was more, and after 10th day the corrosive mass loss was uniform. The mass loss of the composites in comparison to the Al was higher which indicates that the Al–SiC particulate composites can be used satisfactorily in marine / saltwater environments. Figure 6 shows typical SEM micrographs of corroded surfaces of pure Al and AMMC, after exposure for 168 h in 5% by weight of NaCl solution at room temperature. It is clear that the surface of pure Al was severely damaged, especially in the grain boundaries, when compared to the corroded surfaces of the AMMC. The cracks developed along the grain boundaries. In addition to grain boundary corrosion, pitting corrosion was observed in the pure aluminium matrix, as well as in the AMMC.

Figure 5:

Corrosion rate with respect to number of days.

Figure 6:

SEM micrographs of the corroded surfaces of (a) pure Al, and (b) AMMC after exposure for 168 h (seven days) in NaCl solution at ambient temperature.

3.2 Development of predictive model by response surface methodology (RSM)

In the study, Design expert 11.0 is employed to examine the achieved experimental results of erosion rate in accordance with response surface methodology. It was used to develop the best fitted empirical-model that demonstrates a co-relation between the machining response characteristic (here_, erosion rate ER) with the different process parameters (IA, SOD, IV). Regression equation for the response ER is presented by Eq. (1);

The results obtained for the erosion rate (ER) from experimental trails were analysed by employing statistical ANOVA to identify the validation of obtained experimental result. It is also involved to find the remarkable effect of the selected process-parameters and its interaction effect upon corresponding output response. The analysis of variation (ANOVA) table comprises the degree-of-freedom (DoF), sum of square (SS) and mean of squares (MS), factor contribution in total variation (Contr. %), probability (P) and Fisher (F) values. P-value and F-value are introduced to identify the statistical significance and adequacy of the developed regression model. If the P-value for any input parameter found to be under 0.05 (i.e. for 95% confidence level), then that of input parameter may be considered as having statistically-significant influence on corresponding output. If the calculated F-value is lower than the standardized Fisher’s value or P-value is greater than 0.05 for any factor, then that parameter considered as no effect on output result. ANOVA results of erosion rate (ER) model are presented in Table 4. It is found that the developed regression model for erosion rate by RSM is significant. At 95% confidence level, insight of probability (P) value followed by Fisher’s (F) value reveals that the most significant factor on ER is impact velocity with 80.42% contribution of total variation. The next excellent significant contribution on ER comes from the individual effect of impact angle which explains only 8.71% contribution. However, the factor stand-off distance and interactive terms (IA*IV, SOD*IV) reflect in-significant impact on ER, as their contribution are in considerable due to their larger P-value and lower F-value. To conclude, the error percentage of contribution is very little (i.e. 0.63% to ER), which indicates that no important parameter has been missed or any large measurement error has been involved.

To avoid the misleading conclusion, different diagnostic tests such as adequacy, effectiveness and goodness-of-fit were carried out for proposed regression model (ER). When the regression coefficient (R²-value) approaches to one, the predicted-model effectively fits within the actual data. For erosion rate (ER), the calculated R²-value was 0.994 which is very close to unity, which depicts statistical significance as well as goodness-of fit for the proposed model. Moreover, there is a very good degree of resemblance between the experimental and predicted value, as shown in Figure 7a. Thus, it is concluded that the proposed model has high effectiveness with good predictability. From the normal probability plot (see Figure 7b), it is noticed that all the parameters related to the regression model of ER are statistically remarkable/significant as the remaining points are falling linearly, which concluded that the associated errors were normally allocated. With lower AD-statistic (0.342 for ER) as well as larger P-value (i.e. 0.445 for ER) was established from Anderson-Darling test method. It was confirmed that the null-hypothesis can’t be rejected. Finally, it is concluded that, the proposed predictive model for erosion rate using RSM is efficient, statistically significant, adequate and also probabilistically validate as it has low probability value (less than 0.05), higher R²-value and larger AD-test P-value.

Figure 7:

(a) Comparison between experimental and predicted values, and (b) normal probability plot for erosion rate.

3.3 Analysis on erosion rate

The process parameters (impact-angle, impact-velocity and stand-off distance) effects on the erosion-rate of the machined component are graphically analyzed by three-dimensional (3D) surface plot. The typical 3D surface plot shown in Figure 8 illustrates the impact of two process variables like impingement velocity and impingement angle on ER. It is identified that the rate-of-erosion decreases with increase in angle of impingement regardless of the velocity of impact. Erosion wear rate is the highest and maximum at the lowest impingement angle 30° and lowest and minimum at the highest impingement angle 90°. Since maximum erosion-wear occurs at lower impingement-angle, so the erosion is due to the ductile mechanism. The ductile material exhibits the maximum erosion-rate at an angle of impingement i.e. from 15° to 30°, while the erosion is highest at 90° for the brittle materials. Particles, when they hit the surface of the sample at a certain angle, the tangential-velocity of the sand-particles leads to a plastic-deformation in the composite. The erosion-rate shown in Figure 9 is higher at higher velocities. The higher the velocities, the greater the kinetic energy of the particles, consequently the rate of erosion will increase. Erosion rate at different impact angle with different impact velocity are shown in the Table 1 illustrates that increasing trend of erosion rate. Figure 10 presents the scanned electron microscopic images of the eroded surfaces of Al–Mg–Si–Cu–SiC MMC when the solid erodent particle subjected to different impact velocity erosion at SOD = 10 mm; IA = 60°. Figure 11 shows that the rate of erosion decreases with increasing SOD. This may be due to an increase in the stand-off distance increases the path traveled by the particles of erodent to impact on the surface. Therefore, particles lose a lot of kinetic energy before they hit the surface. When the impact rate decreases, the erosion rate also decreases. It is seen that the change in the rate of erosion during SOD is not very noteworthy/significant.

Figure 8:

Effect plots for illustration of impingement angle and impingement velocity on erosion rate.

Figure 9:

Effect plots for illustration of stand-off distance and impingement velocity on erosion rate.

Figure 10:

Influence of impact velocity on eroded surface of Al–Mg–Si–Cu–SiC MMC at SOD = 10 mm; IA = 60°.

Figure 11:

Effect plots for illustration of impact angle and stand-off distance on erosion rate.

In SEM micrograph (see, Figure 12), the silicon carbide particles in an Al–Mg–Si–Cu–SiC matrix composite actually changes the place of the predominant effect, reducing the net surface area of the reinforced particles. The erosion wear will also reduce due to agglomeration of silicon-carbide particles in the matrix-composite. The erosion rate that is detected on an eroded-surfaces where the SiC particles were not removed from the aluminum matrix due to erosion/abrasion. The higher the energy of the erosive impacts of the erodent, the more severe the attack on the composite. Therefore, at higher velocities, the suspended erodent particles scratch on the composite surface, rather than having a tangible effect because of the low toughness of SiC-particles and increased erosion resistance of the matrix elements.

Figure 12:

SEM of micrograph of eroded surface of Al 0.5Si–0.5Mg–2.5Cu–5SiC composite at 90° of impact angle, 40 mm of stand-off distance, and 75 m/s of impact velocity.

3.4 Optimization using desirability function approach

The present study includes a mono-objective optimization based on desirability functional approach of RSM, which keeps the erosion rate to a minimum level. The parameters design is an effective way to improve the product quality as well as the process efficiency. Desirability functional approach is a statistical base multiple response robust parameter design methodology, which is employed for the solving the multi-objective optimization problems. The approach had focused with the correct amalgamation level parameters which would take the responsibility for fulfilling the requirements placed on each response. The criterion for achieving the optimization result is evaluated on the basis of general desirability, which is the weighted average geometric value. The respective desirability for the different performance characteristics is expressed within the range of 0–1. The response will be unaccepted or undesirable, if the desirability value approaches to ‘0’ and it will be more desirable or accepted if the value is close or equal to ‘1’.

For solving the parameters design problem by the desirability functional approach, the objective function may be written as;

The composite desirability function may be stated as Eq. (2);

Here, DF is a composite desirability function that finds the optimal setting by minimizing F(x) (i.e. maximizes DF, since it is highly desirable for optimization), d_i is the assigned desirability function for the ith desired output, and w_i is the weight of d_i (considered equally importance) in this study.

For a goal to minimization of output, individual desirability function can be defined as Eq. (3);

where, L_i and the H_i are the lowest and largest acceptable value of Y for the ith output response respectively.

Figure 13 shows the optimization plot based on desirability function analysis for erosion rate (ER), showing the optimum manufacturing conditions for AMMC (Al–0.5Si–0.5Mg–2.5Cu–5SiC) with an impact angle of 67°, stand-off distance of 26 mm, and impact velocity of 18 m/s. The estimated optimum value of erosion rate is 65.15 mg/kg.

Figure 13:

Optimization plot for erosion rate using desirability function approach.