Fabrication of AA7075 Hybrid Green Metal Matrix Composites by Friction Stir Processing Technique

In the current scenario, MMCs have a vast application in aircraft, automobile, structural, non-structural, marine, transportation and biomedical applications [1]. Excellent properties, like lightweight, high specific stiffness to weight ratio and high strength to weight ratio makes it very much imperative and suitable for many applications [2]. Issues such as waste management and environmental safety are becoming more important to the introduction of new materials and products. The eggshells, rice husk, coconut shell and coir have the problem of decomposition and harm the environment also. To avoid decomposition and reduce the harmful effect on the environment, the eggshells are selected as reinforcing material for the fabrication of green composites.

Friction stir processing is an innovative technology based on the Friction Stir Welding (FSW) principle that locally eliminates casting defects and refines microstructures by conveying high-level energy in the solid-state form, resulting in improved mechanical properties, increase resistance to corrosion and fatigue, enhance formability, and improve other properties [3]. FSP is also an emerging process to produce surface composites by reinforcing the different types of metal particles into the matrix in the solid-state. Figure 1 shows the schematic arrangement of FSP process used to incorporate the reinforcement particles into the matrix matrix material with the help of frictional heat.

Sarmadi et al. [4] observed that the Cu–graphite composites have low friction coefficient and it can be used as bearing materials instead of materials containing lead which cause environmental problems. Singh Subhash and Pal Kaushik [5] analyzed the effect of texture evolution on mechanical properties and damping properties of SiC/ZnAl₂O₄/Al composite fabricated by friction stir processing. Zhao et al. [6] observed in the situ nano-ZrB₂/2024Al composites made-up from 2024Al–K2ZrF₆–KBF₄ system were processed by FSP to achieve superplasticity of the composites. Thangarasua et al. [7] concluded that FSP was significantly influenced the distribution of TiC particles and sliding wear behavior of the AA6082/TiC Aluminium Matrix Composites (AMCs). Chainarong et al. [8] were evaluated about the Friction stir processing of SSM356 Aluminum Alloy. Dinaharan [9] studied the Effect of ceramic particulate types on microstructural and tensile strength of AMCs produced using FSP. Joyson et al. [10] observed that the FSP has been accepted as a potential method to produce AMCs without the disadvantages of fluid metallurgy approaches. The grains of the AA6063 were widely refined by the blend of the thermo-mechanical effect of FSP and the pinning effect of quartz elements. Dinaharan et al. [11] demonstrated the Microstructure and wear characterization of Cu/TiB₂ copper matrix composites fabricated by friction stir processing. They also observed that by reinforcing hard ceramic particles, the poor wear performance of copper is improved. Rana et al. [12] were evaluated about the fabrication of Aluminium 7075 / B₄C surface composite by FSP and investigation on wear properties. They found that wear resistance is to be improved by 100 % compared to the base metal. Ratna et al. [13] were concluded that surface MMCs where the surface of the material is modified by dispersing secondary phase in the form of particles or fibers and the core of the material experience no change in chemical composition and structure. Ahmadkhaniha et al. [14] were concluded that the composite layers containing ~0.8% volume Al₂O₃ nanoparticles were produced on AZ91 magnesium alloy by Raju and Kumar [15] observed that the influence of three factors, such as volume percentage of reinforcement particles (i.e. Al₂O₃), tool tilt angle and concave angle of the shoulder, on the mechanical properties of CueAl₂O₃ surface composites fabricated by FSP. Rathee et al. [16] were evaluated about aluminium matrix surface composites are gaining alluring role exclusively in aerospace, defense, and marine industries. They studied, AA6061/SiC surface composites were fabricated and the effect of tool plunge depth on the pattern of reinforcement particle dispersion in the metal matrix was investigated.

In the present study, an attempt is made to optimize the set of process parameters of the friction stir process to get the desired hardness value.

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Figure 1. Schematic arrangement of FSP

After fabrication hardness was observed with the help of hardness tester and results are tabulated in Table 3. The base metal hardness was tested as 92 HRB (Hardness, Rockwell B) and fabricated composites were showing good improvement after reinforcement and FSP. The mean responses of fabricated raw data and Signal to Noise (S/N) ratios of hardness for each process parameter at all levels were calculated by Minitab 19 software and are presented in Table 4, 5, 6, 7 and 8.

The average Signal-to-Noise ratios and average values of Mean for at all levels are tabulated in Table 8.

Analysis of variance result for Signal-to-Noise ratios and means of hardness were observed that the third process parameter (i.e. tool angle) is insignificant. To identify the significant process parameters in hardness testing the pooled version of ANOVA is calculated through software. The pooled analysis of variance for hardness is given in Table 8. This gives a strong indication that reinforcement (RF) and tool speed (TS) significantly affect the S/N ratio and mean.

It is evident from Figure 7 and Figure 8 that the hardness is highest at the third level of reinforcement i.e. Ti + Egg Shell (TE), third level of tool revolving speed (TS, i.e. 2250 rpm) and second level of tilt angle (TA, i.e. 2⁰). The S/N ratio plot also proposes the same levels as the best level for the maximum hardness of FSP composite. Three confirmation experiments were conducted at the optimal level of process parameters. The average mean value of the tensile strength and hardness of FSP is found within the confidence interval as reported in Table 9.

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Figure 6. FSP Setup

Table 3. Experimental hardness

Table 4. Estimated Model Coefficients for SN ratios

Table 5. Analysis of variance for SN ratios

Table 6. Estimated model coefficients for means

Table 7. Analysis of variance for means

Table 8. Response table for signal to noise ratios and mean larger is better

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Figure 7. Effects of process parameters on hardness (S/N effects)

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Figure 8. Effects of process parameters on hardness (main effects)

Table 9. Responses at optimum levels of process parameters

Responses	Predicted mean values	Experimental values (average)
Hardness (HRB)	132	130