• International Journal of Technology (IJTech)
  • Vol 14, No 5 (2023)

Development of Zirconia Reinforced AA7075/AA7050 Aluminum Chip-Based Composite Processed Using Hot Press Forging Method

Development of Zirconia Reinforced AA7075/AA7050 Aluminum Chip-Based Composite Processed Using Hot Press Forging Method

Title: Development of Zirconia Reinforced AA7075/AA7050 Aluminum Chip-Based Composite Processed Using Hot Press Forging Method
Yahya M. Altharan, S Shamsudin, Sam. Al-Alimi, Mohammed A. Jubair

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Cite this article as:
Altharan, Y.M., Shamsudin, S., Al-Alimi, S., Jubair, M.A., 2023. Development of Zirconia Reinforced AA7075/AA7050 Aluminum Chip-Based Composite Processed Using Hot Press Forging Method. International Journal of Technology. Volume 14(5), pp. 1134-1146

Yahya M. Altharan Sustainable Manufacturing and Recycling Technology, Advanced Manufacturing and Materials Center (SMART?AMMC), Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia
S Shamsudin Sustainable Manufacturing and Recycling Technology, Advanced Manufacturing and Materials Center (SMART?AMMC), Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia
Sam. Al-Alimi Sustainable Manufacturing and Recycling Technology, Advanced Manufacturing and Materials Center (SMART?AMMC), Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia
Mohammed A. Jubair Department of Computer Technical Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, 66002 Al-Muthanna, Iraq
Email to Corresponding Author

Development of Zirconia Reinforced AA7075/AA7050 Aluminum Chip-Based Composite Processed Using Hot Press Forging Method

The solid-state recycling technique has gained significant attention for its ability to reduce metal losses, energy consumption, and solid waste. This study introduced solid-state recycling method to develop zirconia-reinforced AA7075/AA7050 aluminum chip-based matrix composite via a hot press forging process (HPF). The chips were cold-compacted at 35 tons and then hot-forged through a dog bone-shaped die. Full factorial and response surface methodology (RSM) designs were applied using Minitab 18 software. The Face Centred Composite (CCF) of RSM was adopted to rank each factor's effect and analyze interactions between input factors and output responses, followed by process optimization. The selected factors of temperature (Tp) and volume fraction of zirconia (ZrO2) nanoparticles (Vf) were set at 450, 500, and 550 °C with 5, 10, and 15 wt %, respectively. The analyzed responses were ultimate tensile strength (UTS) and microhardness (MH). SEM micrograph revealed a slightly uniform distribution of ZrO2 particles in the matrix. The developed composite gained the maximum strength of 262.52 MPa, a microhardness of 135.5 HV and a density of 2.828 g/cm3 at 550 °C and 10 wt % setting. RSM optimization results suggested 550 °C and 10.15 wt % as optimal conditions for maximum UTS and MH. The preheating temperature exhibited a more significant influence than the ZrO2 volume fraction on the composite's mechanical properties; however, both had a slight effect on grain size. The future prospects of this work are briefly addressed at the end. In conclusion, the HPF process was found to be an efficient recycling method for mitigating environmental impacts by conserving energy and materials.

Composite, Mechanical properties, Microstructure, Recycling


Aluminum alloys are the most commonly used materials in automotive and aerospace structures due to their lightweight properties and enhanced fuel efficiency to reduce CO2 emissions (Rana, Purohit, and Das, 2012). However, the intensive industrial production of aluminum due to high demands caused negative environmental impacts such as CO2 emissions and large amounts of solid waste (Agboola et al., 2020). According to the International Aluminum Institute, the primary aluminum industry is accountable for 1.1% billion tonnes of total CO2 emissions due to smelting processes (International Aluminium Institute, n.d.). In detail, 60% of the indirect emissions come from electric power generation

and 40% come from the aluminum production processes. Primary aluminum production (mining from bauxite ore) requires 113 GJ of energy per tonne, while secondary production (recycling) needs just 13.6 GJ per tonne (Cui and Roven, 2010). This substantial decrease in energy consumption encourages aluminum scrap recycling instead of disposal.

The considerable amounts of scrap and chips generated during machining are able to be recycled and repurposed to achieve sustainability. Numerous researchers have studied aluminum waste recycling to save energy and reduce environmental issues (Yong et al., 2019; Keoleian and Sullivan, 2012). However, recycling aluminum by remelting still consumes high energy and emits CO2, according to some studies (Yong et al., 2019; Wan et al., 2017; Rana, Purohit, and Das, 2012). Meanwhile, other research indicated that converting chips and scraps into semi-product without remelting is eco-friendly since it utilizes 95% less energy and emits just 5% of the greenhouse gas than the primary production process (International Aluminium Institute, n.d.; Shamsudin, Lajis, and Zhong, 2016). Hot press forging (HPF) is a preferred conversion recycling method for chip-based products with good mechanical and physical properties (Lajis, Yusuf, and Ahmad, 2018). Therefore, this work proposes HPF as a novel direct recycling technique for secondary aluminum production.

In the current research, AA7075/AA7050 chip was recycled through HPF and reinforced by zirconia particles (ZrO2) with an average size of  ZrO2-nanoparticle was chosen due to its mechanical properties, high-temperature stability, wear, corrosion, and chemical resistance (Parveen, Chauhan, and Suhaib, 2019). Incorporating ZrO2 particles in aluminum chips improve the tensile strength and hardness of aluminum metal matrix composites (AMCs) (Reddy et al., 2020).  The characteristics AMCs are determined by the interface between the reinforcement and the matrix (Srivyas and Charoo, 2018). AMCs combine the good properties of matrix metal (high ductility and low density) and ceramics (high modulus and strength). However, developing quality-effective and satisfactory end products of AMCs with fundamental geometrical remains a significant challenge. When recycling composite, experimental factors like processing temperature, cold compaction, matrix morphology, and reinforcement material must be well-designed. Design of experiment (DOE) is an efficient technique to investigate the effect of different factors and determine the optimum parameters. In multifactorial experiments, optimization is typically conducted by varying a single factor while all other factors are fixed at a particular set of conditions (Jankovic, Chaudhary, and Goia, 2021). It is not just time-consuming but also unable to determine the true optimum as it neglects the interactions among the variables. Hence, DOE was used to design the process parameters and study the influence of input factors on responses via response surface methodology (RSM) using Minitab 18 software. Analysis of variance (ANOVA) was also adopted to determine the significant parameters influencing responses and reveal optimal design with desired mechanical and physical properties. Besides the microstructural examination, composite samples were tested for ultimate tensile strength (UTS), yield strength (YS), elongation at break (EAB), and microhardness (MH). Tensile strength and microhardness were the qualitative responses to be optimized based on the effect of optimal input factors.
     This research aims to recycle AA7075/AA7050 aluminum chip by HPF and investigate the influence of preheating temperatures and ZrO2 addition on the mechanical and physical properties of the forged composite. The developed composite material was able to be used in the transportation industry.  However, the profile quality is able to be improved by heat treatment. This research has a tendency to contribute to further attention toward direct recycling technologies to conserve energy and natural resources.

Experimental Methods

            2.1.  Materials preparation

    The materials used, such as aluminum chips and zirconia reinforcement material, were supplied by SMART-AMMC, UTHM. The chips were produced from AA7075/AA7050 aluminum bulk with 3.30 × 1.12 × 0.095 mm average size using Sodick-MC430L high-speed milling. The chip was cleaned using acetone (C3H6O) in an ultrasonic bath based on ASTM G131-96 and then dried at 60 °C for 30 min. The prepared chips were mixed with zirconia nanoparticles averaging  in size using a 3D mixer.

2.2.    Rule of mixing

     The aluminum chip and reinforcement particles were mixed to develop uniform distribution throughout the composite. The density-based mixtures rule method was used to determine the required amount of chips and zirconia nanoparticles for the composite production, as presented in the following equations:

Where is composite’s density, V refers to volume with subscripts z and m for zirconia nanoparticles and metal matrix, respectively.   are the mass and volume of the composite, respectively. The corresponding volume fraction is calculated by the given relation:

Where Vf is the volume fraction of particles. Mm and  are mass and density of the particles and matrix, respectively. 

2.3.   Experimental design

Design of experiments (DOE) was used to determine the influence of significant factors and their interactions to optimize the responses via RSM. The input factors were temperature (Tp: 450, 500, and 550 °C) and ZrO2 (Vf: 5, 10, and 15 wt %). The UTS and MH responses of the forged composite were investigated by varying the input factors. To analyze the influence of different settings of Tp and Vf on UTS and MH, the 2k full factorial design (k is number of factors) with 2 replicates and 3 center points for curvature effect analysis was chosen as it is very useful in screening the significant factors of the experiment. Eleven runs were involved, corresponding to the experimental design selected and the run scheme given in Table 1. The star points correspond to  value of 1 to evaluate the interaction between the parameters. RSM was used to obtain the optimal setting that resulted in the highest UTS and MH. The model's regression general equation (6) determines the correlation between the dependent (responses) and independent variables (input factors).

Where  is dependent variable,  is constant,  are coefficient and  are the independent variables.

2.1.  Hot Press Forging process (HPF)

The mixture of chip and ZrO2 particles was weighed at 14 g as per the rule of mixing result and filled up into Flat-Face dog bone-shaped die in Figure 1(a), then cold compacted at 35 tons and four times pre-compacting cycle. The billet die was preheated for 45 min of homogenization time at the desired temperature followed by 2 hours of holding time and forging temperatures (Tp) of 450–550 °C, between the solidus and recrystallization point.

Figure 1 (a) Top and bottom forging die, (b) Forging machine, (c) Tensile testing machine, (d) Forged specimens, (e) Hardness tester and (f) SEM microscope

2.1.  Experimental Tests

The exact geometric dimensions of specimens were based on ASTM E8/E8M (Figure 2). The tensile test of samples was performed using a universal testing machine (Shimadzu EHF-EM0100K1-020-0A). The hardness specimens were tested by Vickers microhardness tester, under a predetermined force of 2.943 N load for 10 s (ASTM E384-11). Microstructure tests were conducted utilizing a scanning electron microscope SEM-JSM T330. The fracture surface morphology was examined by SEM Hitachi SU1510 based on Standard ASTM E3 and ASTM E340 through an optical microscope (Olympus BX60M). The testing specimens were ground using 240, 600, and 1200 SiC paper for 3 min, polished to 6 µm TEXPAN, 1 and 2 µm NAPPAD for 540 s each, then etched at 12 V DC for 2 minutes by Barker's reagent. The density test was carried out in distilled water for whole specimens using HR-250AZ-Compact Analytical Balance Density Determination Kit. Small billet specimens were weighed in air and distilled water to record the weight in various environments. The room temperature was recorded to calculate the relative density by using the following equation:

Where  m, and V are density, mass on air, and volume in liquid, respectively

Figure 2 Plate-type Tension Test Specimen (ASTM E8M) (ASTM E8/E8M-21, 2022)

Table 2 The chemical composition of AA7075/7075 (ASTM B221M -13, 2015,354) 

Results and Discussion

3.1. Ultimate tensile strength (UTS)

      The UTS results with different temperatures and ZrO2 volume fractions, including four additional experiments suggested by DOE for process optimization are shown in Table 3. UTS increased by 288.13% from 56.94 to 221 MPa for 550 °C-forged samples (S1) and 450 °C-forged samples (S2), despite both two samples being reinforced by 5 wt % ZrO2 particles. The UTS of S2 and S13 embedded with 5 and 10 wt % and 550 °C-forged increased by18.78% from 221 MPa to 262.52 MPa. The composite's dislocation density exceeded that of the zirconium oxide nanoparticles. In metal deformation, the strength increases linearly with dislocation density (S. Al-Alimi et al., 2020a). The UTS of the composites increased up to 10 wt% of ZrO2. However, deteriorated by increasing ZrO2 weight content to 15 %, as recorded in samples S4 and S8. This is because a higher volume fraction of ZrO2 caused particle agglomeration and availability of pores. However, low ZrO2 content diffusion was not enough to destruct the oxide layer, causing partial disruption in an immature state of chip consolidation (Al-Alimi et al., 2022).

      The findings show that the UTS was high at 550 °C with different wt % of ZrO2. The higher operating temperature above the solidus point resulted in good metallic bonding between consolidated chips. High processing temperature and average weight content of ZrO2 resulted in relatively recrystallized grains, where grain coarsening was metallurgically bonded (Sabbar et al., 2021). Sample S13 (90% chips + 10 wt % ZrO2) that forged at 550 550 °C had the highest UTS of 262.521 MPa. The UTS of this sample (S13) increased 23.18% compared to S16 (100% chip). This was agreed by (Al-Alimi et al., 2022; Reddy et al., 2020) that ZrO2 enhances the UTS of recycled MMCs. Experimentally, UTS increases linearly with forging temperature and ZrO2 content 10 wt % addition.

Table 3 Results of Elongation at Break, Yield strength, UTS, and MH tests for all samples

3.2. Microhardness
      Microhardness results at different operating temperatures and ZrO2 volume fractions are listed in Table 3. The highest value of hardness was observed at 550 °C and 10 wt % of ZrO2. With 5 wt % ZrO2 addition and forging temperatures of 450 and 550 °C, hardness increased by 38.42% (S1 and S2). However, the hardness of S12 and S13 forged at 450 to 550 °C increased by 54.7% from 87.6 to 135.5 HV with 10 wt % ZrO2 addition.

        As shown in Table 3, the MH of the 100% chip sample (S16) was 98.03 HV, whereas the sample reinforced with 10 wt % ZrO2 (S13) had the highest MH of 135.50 HV. The hardness increment was 38%, although both two samples (S13 and S16) were preheated at 550 °C. Sample S13 recorded the highest hardness of 135.5 HV, presenting the considerable effect of 10 wt % of ZrO2 particles and 550 °C forging temperature. This result corresponds to the trend observed in the UTS results. Moreover, it has been demonstrated that increasing temperature above 500 °C contributed to increased strength due to finer particle dispersion (Arivazhagan, Mahalashmi and Boopathi, 2016; Shamsudin, Lajis, and Zhong, 2016). The hardness of 550 °C-preheated samples began to drop when the ZrO2 content increased to 15 wt % (S4 and S8). The conglomeration of a high content of reinforcement particles causes porosity in composite material (Maniam et al., 2020). The hardness increases linearly with temperature due to grain size reduction and refinement (Sabbar et al., 2021; Rahimian et al., 2009).

3.3. Modelling and optimization of the experimental factors for MMC performance

3.3.1 ANOVA of ultimate tensile strength and microhardness using RSM

     The obtained UTS and MH data were used for further analyses by ANOVA and regression analysis. ANOVA and RSM were carried out to determine the significance of each factor considered in the experiment. The ANOVA results of the full factorial and curvature test suggested further optimization due to the positive effect of curvature. Therefore, four more experiments were added.

     For data analysis, checking the goodness of the model's fit is required. The model adequacy checking includes regression model test for significance, model coefficients significance, and p-value of lack of fit test (Analyse it- Software, 2022). The well-developed chosen model should indicate an insignificant lack of fit. The coefficient of determination (R2) reports how closely the model fitting to the experimental data. The R2 values for both tensile strength and microhardness were 99% and 96% respectively, which were within the acceptable range  The p-value is applied to test the null hypothesis for each term when the coefficient has no effect (0). The p-value (<0.05) means that the null hypothesis is able to be rejected because the coefficient is likely not equal to zero. The ANOVA result in Tables 4 and 5 shows that the quadratic model is considered statistically significant for UTS and MH responses, except for Vf, 2-Way Interaction and TpVf terms. The calculated p-values of the model's rest terms, such as temperature (Tp) and ZrO2 volume fraction (Vf), are less than 0.05, indicating that the model is statistically significant. Consequently, the model fits the experimental data, and input factors affect responses. The lack of fit value of 0.458 and 0.168 for TS and MH respectively is greater than 0.05, signifying that the model is non-significant relative to the noise and denoted a well-developed chosen model, as shown in Tables 4 and 5.

        In UTS result analysis, R2, adjusted R2, and predicted R2 have respective values of 0.9902, 0.9863, and 0.9769. The results prove the impact of zirconia particles on the TS of the developed composite. Meanwhile, the R2, adjusted R2, and predicted R2 values for MH are 0.967, 0.949, and 0.8985, respectively (refer to Table 5). The R2 value of 0.967 is close to 1, which explains the strong correlation between the experimental factors and output responses. The predicted R2 value of 0.8985 is in reasonable agreement with the adjusted R2 value of 0.949, as shown in Table 5.

Table 4 Response Surface Regression: TS versus Tp, Vf  ANOVA