Nowadays, among the aluminum-based alloys, aluminum-silicon cast alloys are the most familiar choice of materials for the automotive industry to produce robust engineering parts due to their lightweight and high strength-to-weight ratio, which is one cause for the appreciable improvement in the overall fuel economy by optimal weighting [1, 2]. Mechanical properties of the aluminum-silicon alloys, in particular the tensile strength, ductility, hardness, and fatigue, are strongly dependent on the dendritic structure, and on primary Al
Usually, the refinement of grains is carried out by introducing inoculating particles into the melt in the form of master alloys prepared by the Al-Ti-B ternary-based system [9,10,11]. Over the past 5 decades, 94%Al-5%Ti-1%B (Al-5Ti-1B) in particular has been widely used as a common grain refiner, because Ti is one of the most common elements for the refinement of
To overcome this poisoning effect, various attempts were made; however, despite being numerous, none of these approaches is up to the mark, because they have failed to fully address this poisoning effect. But in recent studies, Nb-B novel grain refiner has been used to enhance the grain refinement performance in aluminum-silicon cast alloys; also, “it was concluded that the developed Al-1Nb-1B grain refiner can efficiently refine
For this research, the design of experiments has been prepared using the Taguchi approach. Identification of significant process parameters, which is the most important stage, is carried out based on the experimenter's experience, and is mainly based on this researcher's previous investigations into optimization of the die casting process [26]. Thus, based on insights gained from previous experimentation, this researcher has selected mainly three process parameters, viz. molten metal, die temperatures, and injection pressure, recognizing them as the most critical parameters in the experimental design. The other parameters were kept constant throughout the entire experiment. These parameters are designated as process parameters for the present investigation, along with Al-3.5FeNb-1.3C grain refiner and Al-6Ni alloying element as composition factors, to obtain optimal settings for the die casting parameters, in order to yield the optimum casting density by reducing the porosity of the Al-Si alloy castings. Taguchi provided a well-organized approach to estimate the optimal levels of significant input process parameters with the help of the L27 orthogonal array system. The selected levels for these input parameters are given in Table 1. Experimental designs for the fabrication of casting samples at different levels and weight percentages of parameters have been prepared with reference to the L27 orthogonal array.
Process factors and their levels used in the experiments
A | Molten metal temperature (°C) | 720–780 | 720 | 750 | 780 |
B | Al-3.5FeNb-1.5C (wt%) | 0.0–0.1–1.0 | 0.0 | 0.1 | 1.0 |
C | Al-6Ni (wt%) | 0.0–0.5–5.0 | 0.0 | 0.5 | 5.0 |
D | Die temperature (°C) | 230–290 | 230 | 260 | 290 |
E | Injection pressure (MPa) | 12–24 | 12 | 18 | 24 |
The details of the materials and processing methods that are used to fabricate the composite materials are discussed below.
The commercial base alloy that is used in the present investigation is Al-Si9.8-Cu3.4. The chemical composition of the base alloy is shown in Table 2. Investigations are carried out with and without the addition of the new grain refiner and alloying element, the motive being to enhance the mechanical properties of the base alloy.
Composition of Al-Si9.8-Cu3.4 alloy (wt.%) considered for this study
86.18 | 9.839 | 3.474 | 0.189 | 0.184 | 0.041 | 0.008 | 0.008 | 0.043 | 0.013 | 0.007 |
Al-3.5FeNb-1.5C master alloy for grain refinement has been fabricated by an inoculation technique employing the melting furnace. Aluminum (99.9% pure) ingot, activated carbon powder (~150 μm), and ferroniobium (60% Nb) metals were used as raw materials. Initially, 1 kg of aluminum ingot was melted in a graphite crucible at 900°C and held for 1 h. Then, the preheated activated carbon powder, which was wrapped in an aluminum foil, was added to the molten metal at 1.7 wt.% of C, and after 4–5 min, 7.7 wt.% of ferroniobium (FeNb) metal was added to the melt. The melt temperature was increased up to 1,500°C and held for 6–7 min to promote the dissolution of Nb into Al. This molten metal was poured into a metallic die and allowed to solidify. The solidified Al-3.5FeNb-1.5C grain refiner was then used to conduct grain refinement investigation on pure aluminum and Al-Si cast alloys.
The Al-Si9.8-Cu3.4 alloy, which was in the ingot form, was cut into small pieces to facilitate accommodation in the crucible, and then melted in the induction electric resistance furnace; then it was heated to 800°C till the entire alloy in the crucible melted in an argon atmosphere. The slag formed on the surface of the melt was carefully removed, and preheated graphite-coated stirrer was carefully placed below the surface of the melt to carry out the stirring process to ensure homogenization of the temperature within the melt. Then the melt was then taken into a treatment ladle, and the corresponding predetermined amounts of FeNb-C in the form of the Al–3.5FeNb–1.5C master alloy and Al–6Ni (Al–6 wt.% Ni) were added to the mix and then poured into a pouring hole of the HPDC machine according to the detailed proportion of each casting, as shown in Table 3. After the die casting experimentation process, the cast part was taken from the permanent die. Similarly, experimental cast samples in two sets were obtained using the HPDC method [27], as shown in Figure 1A.
Input parameters and experimental values of output characteristics along with average porosity values for L27 orthogonal array
1 | 720 | 0 | 0 | 230 | 12 | 218.39 | 85 | 113 | 0.88 |
2 | 720 | 0 | 0.5 | 260 | 18 | 223.76 | 85 | 116 | 0.53 |
3 | 720 | 0 | 5 | 290 | 24 | 245.50 | 88 | 119 | 0.36 |
4 | 720 | 0.1 | 0 | 260 | 18 | 239.88 | 90 | 136 | 0.26 |
5 | 720 | 0.1 | 0.5 | 290 | 24 | 249.09 | 91 | 135 | 0.25 |
6 | 720 | 0.1 | 5 | 230 | 12 | 237.31 | 85 | 136 | 0.47 |
7 | 720 | 1 | 0 | 290 | 24 | 220.53 | 83 | 112 | 0.85 |
8 | 720 | 1 | 0.5 | 230 | 12 | 214.67 | 83 | 114 | 0.71 |
9 | 720 | 1 | 5 | 260 | 18 | 208.63 | 86 | 118 | 0.75 |
10 | 750 | 0 | 0 | 260 | 24 | 217.87 | 85 | 114 | 0.95 |
11 | 750 | 0 | 0.5 | 290 | 12 | 210.73 | 85 | 116 | 0.77 |
12 | 750 | 0 | 5 | 230 | 18 | 230.46 | 89 | 116 | 0.49 |
13 | 750 | 0.1 | 0 | 290 | 12 | 230.19 | 86 | 118 | 0.63 |
14 | 750 | 0.1 | 0.5 | 230 | 18 | 237.29 | 88 | 130 | 0.44 |
15 | 750 | 0.1 | 5 | 260 | 24 | 212.70 | 85 | 120 | 0.62 |
16 | 750 | 1 | 0 | 230 | 18 | 208.26 | 80 | 106 | 1.01 |
17 | 750 | 1 | 0.5 | 260 | 24 | 218.83 | 85 | 113 | 0.88 |
18 | 750 | 1 | 5 | 290 | 12 | 201.05 | 80 | 108 | 0.97 |
19 | 780 | 0 | 0 | 290 | 18 | 219.96 | 85 | 115 | 0.89 |
20 | 780 | 0 | 0.5 | 230 | 24 | 224.58 | 88 | 125 | 0.33 |
21 | 780 | 0 | 5 | 260 | 12 | 240.50 | 87 | 118 | 0.49 |
22 | 780 | 0.1 | 0 | 230 | 24 | 238.37 | 88 | 127 | 0.46 |
23 | 780 | 0.1 | 0.5 | 260 | 12 | 240.04 | 90 | 132 | 0.48 |
24 | 780 | 0.1 | 5 | 290 | 18 | 232.57 | 85 | 114 | 0.46 |
25 | 780 | 1 | 0 | 260 | 12 | 200.27 | 80 | 107 | 0.99 |
26 | 780 | 1 | 0.5 | 290 | 18 | 199.68 | 85 | 119 | 0.49 |
27 | 780 | 1 | 5 | 230 | 24 | 224.74 | 84 | 114 | 0.53 |
The cast samples sized 15 mm × 15 mm × 6 mm were cut for a metallographic investigation using the standard grinding method with SiC sandpapers at 120–1,200 grading, and polished with emery polishing papers (grade from 1/0 to 4/0); and finally, disc polishing was carried out with Al2O3 solution using a polishing machine. Further, Tucker's solution (15 mL HNO3 + 15 mL HF + 45 mL HCL + 25 mL H2O) was used for etching the polished surfaces to carry out the microstructural examination. Vickers hardness tester (as per ASTME-384 standard) has been used to measure the microhardness values and Brinell hardness values have been taken from Vickers cum Brinell hardness tester performed at 250 kg. For hardness results, an average of two values from each sample of two sets was derived. All the average values obtained using Brinell, as well as the microhardness values, are depicted in Table 3. To evaluate the tensile strength properties of the experimental casting, the rectangular specimens were prepared according to ASTM: B557M-15 standard using the CNC wire cutting machine, as indicated in Figure 1B. All of the ultimate tensile strength (UTS) values were obtained directly from the resolved program of the universal testing machine and tensile values, i.e., the average of two sample values (each from each set), are shown in Table 3. The microstructure and the extracted intermetallic particles of the Al-3.5FeNb-1.5C master alloy were examined by X-ray diffraction (XRD) and FE-SEM.
The densities of all experimental castings were calculated using the immersion technique. Initially, experimental castings were weighted in air, and then measured while immersed carefully in de-gassing processed distilled water. Using a Mettler balance, all experimental cast weighings were conducted with accuracy at 0.0001 g.
The density of degassed distilled water at 20°C is 998 kg/m3. Using the aforesaid method, the densities of the experimental cast parts were measured. The theoretical densities of the castings at different compositions prepared based on experimental design were measured using the rules of mixtures.
Theoretical density is measured by the following equation:
In 1982, GRA, which is part of the grey system, was introduced by Dr. Deng, as a decision-making approach for multi-criteria, i.e., it converts multi-outcomes (responses) into a single outcome problem [28]. Identification of the optimal solution is based on the grey relational grade (GRG) system because it shows the overall performance of experimental runs [29]. GRA involves the following procedural steps to change the multi-objective optimization problem into a single problem using GRG.
TOPSIS is a very easy and potent multi-objective decision analyzing method, and helps to carry out optimum single objective solutions among a large number of multi-objective alternate solutions. The basic principle of this method is to determine the optimum solution, which is the shortest distance to a positive ideal solution (PIS) and far from a negative ideal solution (NIS). TOPSIS analytical method is carried out in the following stages.
PIS and NIS will be determined as
In this stage, the CC of every alternative is assessed by Eq. (14). The CC values of every alternative to the ideal solution are computed using Eq. (11).
Figure 2A shows the XRD results of the Al-3.5FeNb-1.5C master alloy as a grain refiner. It was observed that the Al-3.5FeNb-1.5C grain refiner primarily contains the
The microstructures of the commercial Al-Si9.8-Cu3.4 base alloy with and without the addition of Al-3.5FeNb-1.5C master alloy at 720°C, 750°C, and 780°C are shown in Figure 3. Figures 3A–3C show the microstructures of the Al-Si9.8-Cu3.4 alloy without the addition of grain refiner, and reveal the coarseness of the grain structures, because their average grain sizes are approximately 61.22 ± 3 μm, 71.93 ± 3 μm, and 64.03 ± 3 μm, respectively. From Figures 3D, 3E, and 3F, it can be observed that the coarse grains of the base alloy are refined to small equiaxed ones by the addition of the Al-3.5FeNb-1.5C grain refiner. Primarily when the base alloy is inoculated with the 0.1 wt.% of Al-3.5FeNb-1.5C grain refiner, very fine grains of the base alloy have been observed, together with uniformity in grains and more number of grains per unit area. From Figure 3D, it can be observed that the average grain size of
Table 3 shows the output characteristics, such as tensile properties and Brinell and microhardness values, which are obtained from the 27 experimental high pressure die casts prepared in two sets at different weight percentages of Al-3.5FeNb-1.5C and Al-6Ni master alloys according to the experimental design. We observe significant effects of grain refinement on the mechanical properties of the commercial Al-Si9.8-Cu3.4 alloy, and changes are noticeable primarily in UTS and hardness values (Brinell and micro). Specifically, the UTS and hardness values of the Al-Si9.8-Cu3.4 alloy are improved pursuant to addition of the new grain refiner.
It also can be observed that the grain refinement of the commercial Al-Si9.8-Cu3.4 alloy utilizing the FeNb-C novel grain refiner leads to a significant improvement in the UTS and hardness measurements, as shown in the experimental runs
Quantitatively, the UTS of the unrefined Al-Si9.8-Cu3.4 alloy is about 218.39 ± 3 MPa, and after the addition of 0.1 wt.% of Al-3.5FeNb-1.5C and 0.5 wt.% of Al-6Ni master alloys, the UTS is increased to 249.08 ± 3 MPa. That is to say, the average value of the UTS is increased by 12.3%. The hardness values (Brinell and micro) of the Al-Si9.8-Cu3.4 are also increased from 85 ± 3 Hv to 91 ± 3 Hv and from 113 ± 3 Hv to 136 ± 3 Hv owing to the addition of these two master alloys, respectively. The UTS and macro- and microhardness values for all experimental runs are shown in Figures 4A and 4B. It is apparent that the average hardness (Brinell and micro) and UTS values of sample
The investigation aims to enhance the process improvement and strength of aluminum cast alloys, and thus all the output characteristics such as macro- and microhardness and tensile strength are to be maximized. The output characteristics were normalized for “higher-the-better” using Eq. (1). Table 4 expresses the normalized value of an experimental result, GRC, and GRG calculated through Eqs (1) and (3). The GRCs and overall GRG for each combination are depicted in Table 4.
Normalized data and deviation sequence values of the following characteristics
1 | 0.62 | 0.55 | 0.77 | 0.45 | 0.48 | 0.39 | 0.44 | 20 |
2 | 0.47 | 0.55 | 0.67 | 0.49 | 0.48 | 0.43 | 0.47 | 13 |
3 | 0.07 | 0.27 | 0.57 | 0.87 | 0.65 | 0.47 | 0.66 | 6 |
4 | 0.19 | 0.09 | 0.00 | 0.73 | 0.85 | 1.00 | 0.86 | 2 |
5 | 0.00 | 0.00 | 0.03 | 1.00 | 1.00 | 0.94 | 0.98 | 1 |
6 | 0.24 | 0.55 | 0.00 | 0.68 | 0.48 | 1.00 | 0.72 | 4 |
7 | 0.58 | 0.73 | 0.80 | 0.46 | 0.41 | 0.38 | 0.42 | 23 |
8 | 0.69 | 0.73 | 0.73 | 0.42 | 0.41 | 0.41 | 0.41 | 24 |
9 | 0.82 | 0.45 | 0.60 | 0.38 | 0.52 | 0.45 | 0.45 | 15 |
10 | 0.63 | 0.55 | 0.73 | 0.44 | 0.48 | 0.41 | 0.44 | 18 |
11 | 0.51 | 0.55 | 0.67 | 0.39 | 0.48 | 0.43 | 0.43 | 21 |
12 | 0.38 | 0.18 | 0.67 | 0.57 | 0.73 | 0.43 | 0.58 | 9 |
13 | 0.38 | 0.45 | 0.60 | 0.57 | 0.52 | 0.45 | 0.52 | 11 |
14 | 0.24 | 0.27 | 0.20 | 0.68 | 0.65 | 0.71 | 0.68 | 5 |
15 | 0.74 | 0.55 | 0.53 | 0.40 | 0.48 | 0.48 | 0.46 | 14 |
16 | 0.83 | 1.00 | 1.00 | 0.38 | 0.33 | 0.33 | 0.35 | 25 |
17 | 0.61 | 0.55 | 0.77 | 0.45 | 0.48 | 0.39 | 0.44 | 19 |
18 | 0.97 | 1.00 | 0.93 | 0.34 | 0.33 | 0.35 | 0.34 | 26 |
19 | 0.59 | 0.55 | 0.70 | 0.46 | 0.48 | 0.42 | 0.45 | 16 |
20 | 0.49 | 0.27 | 0.37 | 0.50 | 0.65 | 0.58 | 0.58 | 10 |
21 | 0.17 | 0.36 | 0.60 | 0.74 | 0.58 | 0.45 | 0.59 | 8 |
22 | 0.22 | 0.27 | 0.30 | 0.70 | 0.65 | 0.63 | 0.66 | 7 |
23 | 0.18 | 0.09 | 0.13 | 0.73 | 0.85 | 0.79 | 0.79 | 3 |
24 | 0.33 | 0.55 | 0.73 | 0.60 | 0.48 | 0.41 | 0.49 | 12 |
25 | 0.99 | 1.00 | 0.97 | 0.34 | 0.33 | 0.34 | 0.34 | 27 |
26 | 1.00 | 0.55 | 0.57 | 0.33 | 0.48 | 0.47 | 0.43 | 22 |
27 | 0.49 | 0.64 | 0.73 | 0.50 | 0.44 | 0.41 | 0.45 | 17 |
GRC, Grey relational coefficient; GRG, Grey relational grade.
For every outcome response, the “higher-the-better” condition is chosen. It is always desirable to get the highest value of the GRG. The higher GRG grade indicates a closeness to the optimal response in the process. It is noticed that experiment run 5 has the highest GRG of 0.98. The average optimal values of GRG for each parameter at levels 1–3 are given in Table 5 and plotted in Figures 5A and 5E. The main effects of the various parameters, when changed from the lower to a higher level, are also given in Table 5.
Mean values of GRG at different levels and their main effects
A | Molten metal temperature (°C) | 0.601 | 0.470 | 0.530 | 0.131 |
B | Al-3.5FeNb-1.5C (wt%) | 0.516 | 0.683 | 0.403 | 0.280 |
C | Al-6Ni (wt%) | 0.496 | 0.578 | 0.527 | 0.082 |
D | Die temperature (°C) | 0.539 | 0.537 | 0.525 | 0.015 |
E | Injection pressure (MPa) | 0.508 | 0.528 | 0.565 | 0.056 |
GRG, grey relational grade.
From Figure 4, we can ascertain that the parameter B is more prominent than other parameters; also, it is clear that average GRG appears to be maximum at the second level of the parameters B and C, at the first level of the parameters A and D, and then at the third level of the parameter E – i.e.,
The normalization, weighted normalization, separation, and their relative CC values, which were computed by Eqs (7) and (14), are depicted in Table 6. The experimental run that has the highest CC value, i.e., a value that is very close to 1, is considered the best experimental run. As per the values of CC, which are shown in Table 6, experimental run 5 has the highest CC value of 0.972. The ranks, which are prepared as per the grade (higher to lower) of CC values of experimental runs, are shown as:
TOPSIS results
1 | 0.187 | 0.191 | 0.183 | 0.062 | 0.064 | 0.061 | 0.016 | 0.008 | 0.324 |
2 | 0.192 | 0.191 | 0.188 | 0.064 | 0.064 | 0.063 | 0.014 | 0.010 | 0.416 |
3 | 0.211 | 0.198 | 0.192 | 0.070 | 0.066 | 0.064 | 0.010 | 0.016 | 0.628 |
4 | 0.206 | 0.202 | 0.220 | 0.069 | 0.067 | 0.073 | 0.003 | 0.021 | 0.886 |
5 | 0.214 | 0.204 | 0.218 | 0.071 | 0.068 | 0.073 | 0.001 | 0.023 | 0.972 |
6 | 0.204 | 0.191 | 0.220 | 0.068 | 0.064 | 0.073 | 0.005 | 0.020 | 0.786 |
7 | 0.189 | 0.187 | 0.181 | 0.063 | 0.062 | 0.060 | 0.016 | 0.007 | 0.306 |
8 | 0.184 | 0.187 | 0.184 | 0.061 | 0.062 | 0.061 | 0.017 | 0.007 | 0.283 |
9 | 0.179 | 0.193 | 0.191 | 0.060 | 0.064 | 0.064 | 0.016 | 0.008 | 0.350 |
10 | 0.187 | 0.191 | 0.184 | 0.062 | 0.064 | 0.061 | 0.016 | 0.008 | 0.333 |
11 | 0.181 | 0.191 | 0.188 | 0.060 | 0.064 | 0.063 | 0.016 | 0.008 | 0.321 |
12 | 0.198 | 0.200 | 0.188 | 0.066 | 0.067 | 0.063 | 0.012 | 0.013 | 0.511 |
13 | 0.197 | 0.193 | 0.191 | 0.066 | 0.064 | 0.064 | 0.012 | 0.012 | 0.500 |
14 | 0.204 | 0.198 | 0.210 | 0.068 | 0.066 | 0.070 | 0.005 | 0.018 | 0.779 |
15 | 0.182 | 0.191 | 0.194 | 0.061 | 0.064 | 0.065 | 0.014 | 0.009 | 0.391 |
16 | 0.179 | 0.180 | 0.171 | 0.060 | 0.060 | 0.057 | 0.022 | 0.003 | 0.110 |
17 | 0.188 | 0.191 | 0.183 | 0.063 | 0.064 | 0.061 | 0.016 | 0.008 | 0.334 |
18 | 0.172 | 0.180 | 0.175 | 0.057 | 0.060 | 0.058 | 0.022 | 0.001 | 0.059 |
19 | 0.189 | 0.191 | 0.186 | 0.063 | 0.064 | 0.062 | 0.015 | 0.009 | 0.370 |
20 | 0.193 | 0.198 | 0.202 | 0.064 | 0.066 | 0.067 | 0.009 | 0.014 | 0.598 |
21 | 0.206 | 0.196 | 0.191 | 0.069 | 0.065 | 0.064 | 0.010 | 0.014 | 0.582 |
22 | 0.204 | 0.198 | 0.205 | 0.068 | 0.066 | 0.068 | 0.006 | 0.017 | 0.727 |
23 | 0.206 | 0.202 | 0.213 | 0.069 | 0.067 | 0.071 | 0.004 | 0.020 | 0.845 |
24 | 0.200 | 0.191 | 0.184 | 0.067 | 0.064 | 0.061 | 0.014 | 0.011 | 0.452 |
25 | 0.172 | 0.180 | 0.173 | 0.057 | 0.060 | 0.058 | 0.022 | 0.001 | 0.032 |
26 | 0.171 | 0.191 | 0.192 | 0.057 | 0.064 | 0.064 | 0.018 | 0.008 | 0.309 |
27 | 0.193 | 0.189 | 0.184 | 0.064 | 0.063 | 0.061 | 0.015 | 0.009 | 0.380 |
CC, closeness coefficient.
Response table of average values of CC for TOPSIS
A | Molten metal temperature (°C) | 0.371 | 0.478 | 0.179 | |
B | Al-3.5FeNb-1.5C (wt%) | 0.454 | 0.240 | 0.464 | |
C | Al-6Ni (wt%) | 0.399 | 0.460 | 0.141 | |
D | Die temperature (°C) | 0.463 | 0.435 | 0.036 | |
E | Injection pressure (MPa) | 0.4147 | 0.465 | 0.104 |
CC, Closeness coefficient.
The optimum results which are obtained from GRA and TOPSIS multi optimized analytical techniques are compared and noticed that both have the same set of combination of process parameters i.e
The predicted value of GRG was 0.809 and CC was 0.948, i.e., the predicted grey grade value of GRA is less than the predicted closeness value of TOPSIS. Similarly, based on the experimental confirmation, the highest values of the grey grade and CC were 0.979 and 0.972, respectively. The results of confirmation are depicted in Table 8. Mainly, a significant improvement has been observed in grey grade value from the initial set of parameters to an optimal set of parameters combination was 0.540 and CC was 0.647. Further, it was observed that optimal process parameters’ combination results yield a CC value higher than the seventh experiment for the TOPSIS approach and higher than the 10th experiment in the GRA approach, which is the highest value in the L27 orthogonal array of the set of experiments.
Results’ comparison at initial and optimal levels by GRA and TOPSIS methods
1 | Tensile strength | 218.388 | – | 249.085 |
2 | Brinell hardness | 85 | – | 91 |
3 | Vickers microhardness | 113 | – | 135 |
4 | Grey relational grade | 0.4396 | 0.8087 | 0.9792 |
5 | Closeness coefficient | 0.3242 | 0.9481 | 0.9715 |
6 | Improvement of GRG | – | 0.5396 | – |
7 | Improvement of CC | - | 0.6473 | – |
CC, closeness coefficient; GRA, grey relational analysis; GRG, grey relational grade.
SEM images with EDX examination of the experimental runs
The microstructural and mechanical properties of commercial Al-Si9.8-Cu3.4 alloy die castings influenced by grain refiner and alloying additions have been investigated successfully. During this investigation, the TOPSIS methodology along with GRA for optimization of the die casting process for the fabrication of aluminum-silicon alloy casts has been implemented. The following summarized conclusions can arrived at based on the experimental investigations:
Appreciable grain refinement of Al-Si9.8-Cu3.4 alloy has been observed, particularly at lower level of addition of 0.1 wt.% of Al-3.5FeNb-1.5C; conversely, poor grain refining efficiency was observed at a higher level of addition of 1.0 wt.% of Al-3.5FeNb-1.5C, which results in the agglomeration of NbC and formation of The developed Al–3.5FeNb–1.5C grain refiner conducts a significant performance of grain refinement on the commercial Al-Si9.8-Cu3.4 alloy die castings due to the inauguration of niobium silicides (NbSi2, Nb3Si, and Nb5Si3), which are more stable than titanium silicides, effectively encountering the so-called poisoning effect as a result. So, the mechanical properties of the Al-Si9.8-Cu3.4 alloy die casting are significantly improved by the addition of the 0.1 wt.% Al-3.5FeNb-1.5C master alloy and Al-6Ni alloy. In quantitative terms, the improvement in mechanical properties can be expressed thus: UTS, Brinell hardness, and microhardness values have been increased by 12.3%, 7.0%, and 20%, respectively. Hence the effective grain refinement performance of the novel Al-3.5FeNb-1.5C mater alloy for Al-Si cast alloys, where silicon content is more than 4 wt.% is accepted and recommended to industries. Both the TOPSIS and GRA have an identical set of optimal parameters’ combination, i.e.,