This paper presents the findings of an experimental investigation on the effects of applied load, sliding velocity, wt.% of reinforcement and hardness of the counterface material in dry sliding wear studies performed on red mud-based aluminum metal matrix composites (MMC). The specific wear rate and the coefficient of friction are considered as the output quality characteristics. Taguchi-based L9 orthogonal array has been used to accomplish the objective of the experimental study. Analysis of variance (ANOVA) is employed to find the optimal setting and the effect of each parameter on the output performance characteristics. It has been observed that optimal factor setting for each output performance is different. In order to minimize the two responses simultaneously, multiobjective optimization based on ratio analysis (MOORA) is adopted. MOORA revealed that the optimal combination of the dry sliding wear parameters for the multiperformance characteristics of the red mud based aluminium is the set normal load at 20 N, sliding velocity 3 m/s, % of reinforcement 20%, and counterface hardness of the material 58 HRC.
The metal matrix composites exhibit the significant increase in mechanical strength, wear resistance and damping properties when compared to matrix alloy [
Huda et al. [
Sahin and Ozdin [
A few attempts have been made to fabricate MMC to increase the wear resistance characteristics using low cost reinforcement like bauxite, corundum, granites, and sillimanite [
So, in this work an attempt is made to use red mud as reinforcement material and aluminium as matrix material. The present investigation has been carried out to optimize the wear and the coefficient of friction of the red mud reinforced aluminum metal matrix composite fabricated through the stir casting process.
LM 25 aluminum alloy and red mud were used as matrix and reinforcement material. LM 25 aluminum alloy finds application in the electrical sliding contacts, cylinder blocks, cylinder heads, brakes, and other engine body castings. Reinforcement materials are added to the LM 25 alloy to enhance the strength of the part being manufactured. Tables
Chemical composition of aluminum alloy.
Element | Cu | Mg | Mn | Fe | Ni | Zn | Al |
---|---|---|---|---|---|---|---|
wt.% | 7.15 | 0.49 | 0.11 | 0.47 | 0.002 | 0.07 | Rest |
Chemical composition of red mud.
Element | Fe2O3 | Al2O3 | TiO2 | SiO2 | Na2O | CaO | V2O5 | Others |
---|---|---|---|---|---|---|---|---|
wt.% | 53.8 | 14.3 | 3.9 | 8.34 | 4.3 | 2.5 | 0.38 | Balance |
The red mud used for the present investigation is collected from the National Aluminum Company Limited (NALCO) Damanjodi, Odisha, India. The size of the red mud particles used for the study is in a range of 125–150
LM 25 aluminum alloy was cut into pieces from the ingot and pickled in 10% sodium hydroxide solution at 95–100°C for 10 minutes. The sinut formed was removed by immersing in a mixture which equally contains nitric acid and water, before washing in methanol. Immediately after drying up in the air, the weighed quantity of pickled aluminum was melted in a crucible. The required quantity of red mud (15, 20 and 25%) was taken in the powder containers. The red mud was preheated in the electrical furnace up to 800°C and maintained the temperature before mixing with aluminum melt.
The weighed quantity of pickled aluminum was melted to desired superheating temperature of 750 ± 10°C in graphite using crucible electrical resistance furnace with temperature controlling device. After melting was over, the required quantity of red mud particulates was preheated to around 800 ± 10°C to add to the molten metal and stirred continuously by using mechanical stirrer. The stirring time was maintained between 120 s at an impeller speed of 600 rpm. To enhance the wettability of red mud particles, small quantities of magnesium were added to the melt during stirring. The melt with the reinforced particulates was poured to prepare composite specimen. The prepared composite was subjected to machining to produce a size of
Optical microscope was used to study the distribution of red mud reinforcement in aluminum matrix. Figures
Properties of the 20% red mud reinforced composites.
Sl. Number | Description | Value and units |
---|---|---|
1 | Density | 2.62 g/cm3 |
2 | Modulus of elasticity | 42.8 GPa |
3 | Hardness | 55.6 VHN |
4 | Impact strength | 8.74 kg m/cm2 |
SEM micrograph and EDX 20 wt.% red mud casted composite.
Pin-on-disc wear testing apparatus (ASTM G99-95 (2006) standard) was used for performing dry sliding wear test for evaluating wear characteristics of the fabricated composites. Before conducting the test, the testing pin and the disc surfaces were polished with emery papers, and surface roughness (
Properties of the counterface material.
Sl. Number | Description | Value and units |
---|---|---|
1 | EN 32 steed hardness | 58 and 60 HRC |
2 | Density | 8.03 g/cm3 |
3 | Tensile strength | 386 MPa |
4 | Yield strength | 284.4 MPa |
The dry sliding wear performance of the composites was studied as function of load, sliding velocity, wt.% of reinforcements, and counter face hardness of the material. The dry sliding wear tests were carried out at controlled temperature with sliding velocity of 2, 3, and 4 m/s. The applied normal load varied from 10 to 30 N with a step of 10 N. Constants sliding distance was maintained at 3000 m for all the tests. The coefficient of friction (cof) was computed from the applied load and the tangential load which was obtained from the strain gauges. Specific wear rate of the composites was calculated from the ratio of volume loss to applied load and sliding distance. The worn surfaces at the end of the tests were examined and analyzed using SEM.
Taguchi design of experiment is a powerful analyzing tool for modelling and analysis the effect of control factors on output response. In design of experiment, the most important stage is selection of control factors. The specific wear rate and coefficient of friction characteristics of the dry sliding wear are affected by applied load (
Dry sliding wear parameters and levels.
Dry sliding wear parameter | Level 1 | Level 2 | Level 3 |
---|---|---|---|
Applied load ( |
10 | 20 | 30 |
Sliding velocity ( |
2 | 3 | 4 |
wt.% of reinforcement ( |
15 | 20 | 25 |
Hardness of the counterface ( |
58 | 60 | 62 |
Four process parameters at three levels led to the total of 9 dry sliding wear tests. The constant sliding distance was maintained at 3000 m for all the experiments. In this study, the standard L9 orthogonal array was chosen which had 13 rows corresponding to the number of parameters combination with 8 degrees of freedom.
The objective of the experimentation is to reduce the specific wear rate and coefficient of friction as small as possible. In Taguchi method, signal-to-noise (S/N) ratio is used to represent a performance characteristic and the largest value of S/N ratio is required. There are three types of S/N ratio—the lower-the-better, the higher-the-better, and the nominal-the-better. In this work, the lower-the-better characteristic is required for all output responses hence lower-the-better characteristic can be expressed as;
Table
Experimental runs and results.
Sl. Number | Applied load in N | Sliding velocity in m/s | wt.% of reinforcement | Hardness in HRC | Specific wear rate |
Coefficient of friction ( |
---|---|---|---|---|---|---|
1 | 10 | 2 | 15 | 58 | 0.13 | 0.48 |
2 | 10 | 3 | 20 | 60 | 0.08 | 0.57 |
3 | 10 | 4 | 25 | 62 | 0.01 | 0.44 |
4 | 20 | 2 | 20 | 62 | 0.15 | 0.62 |
5 | 20 | 3 | 25 | 58 | 0.16 | 0.61 |
6 | 20 | 4 | 15 | 60 | 0.08 | 0.59 |
7 | 30 | 2 | 25 | 60 | 0.11 | 0.64 |
8 | 30 | 3 | 15 | 62 | 0.15 | 0.56 |
9 | 30 | 4 | 20 | 58 | 0.11 | 0.62 |
Optimum factor level.
Factors | Specific wear rate | Coefficient of friction |
---|---|---|
|
1 | 1 |
|
3 | 3 |
|
3 | 1 |
|
3 | 3 |
Main effect plot. (a) Specific wear. (b) Coefficient of friction.
The small error of 3.1, and 4.4% between the predicted and experimental value for specific wear rate and coefficient of friction, respectively, proves the stability of the resulting model.
The relative influence factor of the dry sliding parameter on the specific wear rate and the coefficient of friction is determined by ANOVA. Tables
ANOVA for specific wear rate.
Factors | Levels S/N | DF | SS |
|
|
|
||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | ||||||
|
26.55 | 18.11 | 18.00 | 2 | 0.0049 | 0.0024 | 0.0024 | 22.33 |
|
17.79 | 17.96 | 26.91 | 2 | 0.0062 | 0.0031 | 0.0031 | 28.28 |
|
18.56 | 19.07 | 25.03 | 2 | 0.0070 | 0.0035 | 0.0035 | 31.79 |
|
17.47 | 21.02 | 24.17 | 2 | 0.0038 | 0.0019 | 0.0019 | 17.5 |
| ||||||||
Total | 8 |
DF: degree of freedom, SS: sum of squares,
ANOVA for coefficient of friction.
Factors | Levels S/N | DF | SS |
|
|
|
||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | ||||||
|
6.079 | 4.310 | 4.305 | 2 | 0.024 | 0.012 | 0.0120 | 67.47 |
|
4.760 | 4.692 | 5.242 | 2 | 0.002 | 0.0009 | 0.0009 | 5.059 |
|
5.268 | 4.377 | 5.049 | 2 | 0.005 | 0.0025 | 0.0025 | 14.22 |
|
4.908 | 4.428 | 5.357 | 2 | 0.005 | 0.0023 | 0.0023 | 13.23 |
| ||||||||
Total |
8 |
MOORA was introduced by Brauers and Zavadskas [
The experimental result of various performance characteristics is normalized and turned into nondimensional values using (
Normalized ratio and MOORA ranking.
Sl. Number | Normalized S/N ratio | Subtracted value | Rank |
---|---|---|---|
1 | 17.72 | −0.3212 | 6 |
2 | 21.93 | −0.2765 | 8 |
3 | 40.00 | −0.1426 | 9 |
4 | 16.47 | −0.3896 | 2 |
5 | 15.91 | −0.4004 | 1 |
6 | 21.93 | −0.2829 | 7 |
7 | 19.17 | −0.3393 | 5 |
8 | 16.02 | −0.3846 | 3 |
9 | 18.78 | −0.3402 | 4 |
The mean of the subtracted value for each level of the dry sliding wear parameter can be calculated by averaging the subtracted value. For the applied load, the experiment numbers are 1–3 for level 1, experiment numbers 4–6 for level 2, and experiment numbers 7–9 for level 3. Similarly, it is calculated for the respective levels for applied load, sliding velocity, wt.% of reinforcement, and hardness of the counterface material. The larger the value of the subtracted values, that the better the multiresponse characteristics. Figure
Graph for subtracted value.
Once the optimal level of the dry sliding wear parameters is identified, the following step is to verify the improvement of the performance characteristics. Table
Confirmation experiment.
Response | Initial parameter setting | Optimal parameter setting | Improvement in % |
---|---|---|---|
Setting level |
|
|
— |
Specific wear rate |
|
|
4.2 |
Coefficient of friction | 0.44 | 0.41756 | 5.1 |
The optimized result can be correlated with the SEM micrographs of the worn surface of red mud reinforced metal matrix composites. In aluminum—20 wt.% of red mud reinforcement clearly shows fine grooves in the surface and plastic deformation at few places when applying 3 m/s sliding velocity and 58 HRC counter face hardness of the material as shown in Figure
Worn surface of red mud reinforced aluminium matrix at optimal setting.
From Figure
Worn surface of red mud reinforced aluminium matrix at 30 N and 4 m/s.
The use of the Taguchi method combined with the MOORA to optimize the dry sliding wear parameters of the red mud-based aluminum metal matrix composites by considering the multiple quality characteristics has been reported in this paper.
The following conclusions were made. For the lowest specific wear rate, 10 N applied load, 4 m/s sliding speed, 25 wt.% of reinforcement, and 62 HRC counterface hardness of the material were used. For the lowest coefficient of friction, 10 N applied load, 4 m/s sliding speed, 15 wt.% of reinforcement, and 62 HRC counterface hardness of the material were used. By analyzing the response graph of the average subtracted value, it is found that the largest value of the subtracted value of for the applied load is 20 N ( From the ANOVA, it is understood that the specific wear rate highly depends upon the wt.% of reinforcement (31.79%) followed by sliding velocity (28.28%), applied load (22.33%), and counter face hardness of the material (17.58%). It is also observed from the ANOVA table that the coefficient of friction of the composite material is highly affected by applied load (67.17%), % of reinforcement (5.05%), counter face hardness of the material (14.22%), and sliding velocity (13.23%). Through the MOORA method, the reduction in the specific wear rate and the coefficient of friction of dry sliding wear parameter of red mud based aluminum metal matrix composites was observed when compared to the From this study it is also concluded that the wear resistance of the dry sliding wear parameter of the red mud based aluminum metal matrix composites has been enhanced greatly through MOORA method.