Dry Sliding Friction andWear Studies of Fly Ash Reinforced AA-6351 Metal Matrix Composites

Fly ash particles are potentially used inmetal matrix composites due to their low cost, low density, and availability in large quantities as waste by-products in thermal power plants.is study describesmultifactor-based experiments that were applied to research and investigation on dry sliding wear system of stir-cast aluminum alloy 6351 with 5, 10, and 15wt.% �y ash reinforced metal matrix composites (MMCs). e effects of parameters such as load, sliding speed, and percentage of �y ash on the sliding wear, speci�c wear rate, and friction coefficient were analyzed using Grey relational analysis on a pin-on-disc machine. Analysis of variance (ANOVA) was also employed to investigate which design parameters signi�cantly affect the wear behavior of the composite. e results showed that the applied load exerted the greatest effect on the dry sliding wear followed by the sliding velocity.


Introduction
Metal matrix composites (MMCs) have received substantial attention due to their reputation as stronger, stiffer, lighter, and excellent wear properties over the monolithic alloys [1][2][3].ough MMCs possess superior properties, they have not been widely applied due to the complexity of fabrication [4].e conventional stir casting is an attractive processing method for fabrication, as it is relatively inexpensive and offers wide selection of materials and processing conditions.Stir casting offers better matrix particle bonding due to stirring action of particles into melts [5].
Wear is one of the most commonly encountered industrial problems, leading to frequent replacement of components, particularly abrasion.Abrasive wear occurs when hard particles or asperities penetrate a soer surface and displace material in the form of elongated chips and slivers [6].Extensive studies on the tribological characteristics of aluminum MMCs containing various reinforcements such as silicon carbide, alumina, and short steel �ber are already done by researchers [7][8][9].e variables such as composition of the matrix, particle distribution, and interface between the particles and the matrix affect the tribological behavior of metal matrix composites.ese conditions include the type of countersurface, applied load, sliding speed, contact area, geometry, and environment [10].e principle tribological parameters such as applied load [11][12][13], sliding speed [14,15], and percentage of �y ash control the friction and wear performance.Fly ash is one of the residues generated in the combustion of coal.e addition of �y ash leads to the increase in wear resistance, hardness, elastic modulus and proof stress compared to unreinforced alloy.e �y ash particle size and its volume fraction also signi�cantly affect the wear and friction properties of composites [16,17].
e aim of the present study is to investigate the dry sliding wear of stir cast AA 6351 with 5, 10, and 15 wt.% �y ash reinforced MMCs, using a pin-on-disc type of wear machine.Furthermore, ANOVA was employed to investigate which design parameters signi�cantly affect the wear behavior of the composite.Con�rmation test was also conducted to verify the improvement of the quality characteristic using optimal levels of the design parameters.

Materials and Experimental Procedures
2.1.Raw Material Description and Its Composition.Aluminum alloy 6351 is used in heavy engineering applications due to its strength, bearing capacity, ease of workability, and weldability.It is also used in building ship, column, chimney, rod, mould, pipe, tube, vehicle, bridge, crane, and roof [18].e elements of aluminum alloy 6351 and the weight in % are tabulated in Table 1.
Fly ash is an industrial by-product recovered from the �ue gas of coal burning electric power plants.Fly ash particles are mostly spherical in shape.e �y ash is collected from Tuticorin thermal power station, India, with an average particle size of 2-10 microns, and its typical micrograph is presented in Figure 1.

Fabrication of Composite.
AA-6351 with 5, 10, and 15 wt.% �y ash reinforced MMCs are prepared by stir casting process.e melting was carried out in a resistance furnace.e �y ash particles were preheated on an open hearth furnace for one hour to remove the moisture.Scraps of AA 6351 were preheated at 450 ∘ C for 3 to 4 h before melting.e preheated aluminum scraps were �rst heated above the liquidus temperature to melt them completely.ey were then slightly cooled below the liquidus to maintain the slurry in the semisolid state.is procedure has been adopted while stir-casting aluminum composites [19,20].Typical specimen is shown in Figure 2.

Wear
Tests.e pin-on-disc machine was used to evaluate the friction and wear response to the sliding contact surface of the specimens as shown in Figure 3. Tests were conducted under dry conditions as per ASTM G99-95 standards.e pin was initially cleaned with acetone and weighed accurately using a digital electronic balance.e test was carried out by applying load (9.81, 19.62, and 29.43 N) and run for a constant sliding distance (3000 m) at different sliding velocities (1, 2, and 3 m/s).e disc material was made  of EN-32 steel with the hardness of 65 HRC.At the end of each test, the weight loss of the specimen was noticed.e results obtained during this work have been presented in terms of sliding wear, speci�c wear rate, and friction coefficient.Sliding wear is related to interactions between surfaces and more speci�cally the removal and deformation of material on a surface as a result of mechanical action of the opposite surface.Speci�c wear rate is the volume loss per sliding distance and load.Friction coefficient is the ratio of the force of friction between two bodies and the force pressing them together.

Selection of Testing Parameters and eir Levels. e application of design-of-experiments (DoE) requires careful
planning, prudent layout of the experiment, and expert analysis of results.Taguchi has standardized methods for each of these DoE application steps.e experiment speci�es three principle wear testing conditions including applied load (A), sliding speed (B), and percentage of �y ash (C) as the process parameters.e experiments were carried out to analyze the in�uence of sliding wear, speci�c wear rate and friction coefficient on the MMCs.Control factors and their levels are shown in Table 2. is table shows that the experimental plan had three levels.
e standard Taguchi experimental plan with notation L 9 (3) 4 was chosen based upon the degree of freedom.e degrees of freedom for the orthogonal array should be greater than or at least equal to those of the process parameters.Table 3 shows the L 9 (3) 4 orthogonal array.
2.5.Experimental Details.e experimental results are transformed into a signal-to-noise (S/N) ratio.e-lower-thebetter characteristics were selected for sliding wear, speci�c wear rate and friction coefficient due to the wear resistance of the tested samples.A pin-on-disc type of apparatus was employed to evaluate the wear characteristics at a constant sliding distance of 3000 m.Table 4 shows the experimental results of sliding wear, speci�c wear rate, and friction coefficient and their S/N ratios.

Multiresponse Optimization Using Grey Relational Analysis.
Taguchi method is designed to optimize single response characteristic.e-higher-the-better performance for one factor may affect the performance because another factor may demand the-lower-the-better characteristics.Hence, multiresponse optimization characteristics are complex.Here the Grey relational analysis optimization methodology for multiresponse optimization is performed with the following steps.
(i) Normalizing the experimental results.
(ii) Performing the Grey relational generating and calculating the Grey relational coefficient.
(iii) Calculating the Grey relational grade by averaging the Grey relational coefficient.
(iv) Performing statistical analysis of variance (ANOVA) for the input parameters with the Grey relational grade to �nd which parameter signi�cantly affects the process.
(v) Selecting the optimal levels of process parameters.
(vi) Conducting con�rmation experiment and verifying the optimal process parameters setting.
In Grey relational analysis, the complex multiple response optimizations can be simpli�ed into the optimization of a single response Grey relational grade.

Grey Relational Generation.
In the grey relational analysis, when the range of the sequence is large or the standard value is enormous, the function of factors is neglected.However, if the factors goals and directions are different, the Grey relational analysis might also produce incorrect results.erefore, one has to preprocess the data which are related to a group of sequences, which is called "Grey relational generation." For the-lower-the-better quality characteristics data preprocessing is calculated by where   is the th experimental results in the jth experiment.Table 5 shows the preprocessed data results.e grey relational coefficients of each performance characteristic are calculated in Table 6 is the distinguishing coefficient,   0, .

Grey Relational Grade.
Table 7 shows the in�uence of process parameters of Grey relational grade.e higher value of the Grey relational grade represents the stronger relational degree of the reference sequence and the given sequence.e grey relational grade is obtained by where   is the Grey relational grade for the jth experiment and m is the number of performance characteristics.As a result, optimization of the complicated multiple performance characteristics can be converted into optimization of single grey relational grade.e higher Grey relational grade represents that the experimental result is closer to the ideal normalized value.

Wear Mechanism
e typical wear mechanism of composites is presented in this section.At lower loads the sliding wear, speci�c wear rate, and friction coefficient decrease with increase in �y ash percentage, but as the load increases, they tend to increase with increase in �y ash percentage.e composites were also noticed to have mild wear as the sliding speed increases.e results were related and veri�ed to the tests conducted by various researches [21,22].Examinations of the worn surfaces of the composites were done by scanning electron microscope.Figure 4(a) shows the worn surface of the AA-6351-�yash composite (@ 19.62 N, 3 m/s, and 5 wt.% �yash), featuring some shallow grooves that are produced when there is abrasion in the tribological pair.Figure 4(b) shows the worn surface of AA 6351-�yash composite (@ 29.43 N, 1 m/s and 15 wt.% �yash), featuring some shallow grooves and damages.Among the experiments conducted there was no seizure condition noticed.one parameter in a single sequence; one has to do the analysis repeatedly to obtain other factors for the experiment.

Analysis of Variance
Since the experimental design is orthogonal, it is then possible to separate the effects of each process parameter at different levels.For example, the mean of grey relational grade for the applied load at level 1, 2, and 3 can be calculated by taking the average of the grey relational grade for the experiments 1-3, 4-6, and 7-9, respectively.e mean of the grey relational grade for each level can be computed in similar manner.e mean of the grey relational grade for each level of the combining parameters is summarized in the multiresponse performance which is shown in Table 8. Figure 5 shows the main effects plot for S/N ratios (dB).
ANOVA of the response quality characteristics is shown in Table 9, and it is observed that applied load is the signi�cant factor for minimizing the sliding wear, speci�c wear rate, and friction coefficient.

�. Con�����ion ��s�s
e �nal step is to verify the improvement of the quality characteristic using optimal levels of the design parameters (A2B3C1).Table 10 shows a comparison of the predicted and the actual design parameters.According to Table 10, the improvement of the performance is noticed when the optimum conditions were used.

Conclusions
(i) Aluminum matrix reinforced with 5, 10, and 15 wt.% �y ash was successfully prepared by stir-casting process, and the wear behavior of the composites was investigated using pin-on-disc machine.
(ii) e L 9 (3) 4 orthogonal array was adopted to investigate the effects of operating variables on the abrasive wear of various composites.
(iii) e experimental results show that the composites retain the wear resistance properties at lower loads with increase in �yash percentage.Mild wear was also observed in the composites as the sliding speed increases.
(iv) For all the trials it is observed that mild-to-severe wear exists, and it is witnessed by the microscopic results.
(v) e applied load and sliding speed are the most in�uencing factors, and it is observed that their contributions to wear behavior are 49.71% and 30.43%, respectively.
(vii) e con�rmation experiment is conducted with the level A2B3C1 to verify the optimal design parameter, and it exhibits better wear performance.

F 4 :F 5 :
(a) SEM of worn surface with grooves.(b) SEM of worn surface with grooves and damages.T 10: Results of the con�rmation experiment.Main effects plot for S/N ratios (dB).
T 2: Control factors and their levels.
T 4: Experimental results of wear and friction coefficient and their S/N ratios.
e analysis of variance (ANOVA) is used to investigate which design parameters signi�cantly a�ect the quality characteristic.e traditional statistical technique can only obtain T 9: Results of analysis of variance.