Cast aluminium alloy pistons are widely used in engines, and their wear significantly affects their service life. The finite element simulation analysis approach can accurately predict the amount of wear, reducing product development and production time. This study investigated the wear resistance by incorporating alumina reinforcing particles into the cast aluminium alloy matrix. The friction coefficient was measured using the MM-200 friction and wear testing equipment, and the wear rate was estimated after introducing alumina particles with varying contents into the aluminium alloy matrix. It was found that the cast aluminium alloy with a particle concentration of 2 wt% exhibited the lowest wear rate. A realistic finite element model of ring block friction and wear was developed based on Archard's theory, and wear simulations were conducted for cast aluminium alloys with different particle concentrations. By comparing the experimental and simulated wear scar widths, it was observed that the simulation findings closely matched the experimental data, demonstrating the accuracy of the finite element model. Additionally, the long-term wear behavior of various particle reinforced cast aluminium alloys was predicted by extending the wear simulation duration, revealing the wear characteristics of these alloys with different particle concentrations.
Article
Wear simulation analysis and prediction of cast aluminum alloy material based on ring and block geometric model
https://doi.org/10.21203/rs.3.rs-3856621/v1
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Particle enhancement
Friction and wear
Archard model
Ring block friction
Finite element analysis
UMESHMOTION
Aluminium alloy is a lightweight and easily processed material that offers a wide range of applications. Its exceptional mechanical properties have led to its extensive use in aerospace and internal combustion engines[1, 2]. In particular, aluminium alloy pistons are commonly produced through casting. To enhance their performance, various metal or non-metal compounds particles can be added to the aluminium base. Some commonly used reinforcing elements include SiC, Al2O3, MgO, TiC, B4C, TiO2, FlyAsh, TiB2, B, and Graphite[3, 4]. The as-cast structure of the aluminium alloy is influenced by factors such as temperature and ageing time, which subsequently affect its mechanical properties and wear characteristics. Moreover, the addition of reinforcing phases like copper and silicon, as well as the implementation of heat treatment processes, have shown promise in improving the wear resistance of aluminium alloy[5–7].
It is well known that alumina functions as a load-bearing component in a multicomponent system, thereby enhancing the wear resistance of the composite. The addition of alumina particles to the aluminium alloy matrix can effectively reduce wear. This is due to the hard nature of alumina particles, which obstruct grinding, ploughing, and penetration by the steel counterpart. Consequently, the mass loss decreases with the inclusion of alumina particles under all loads [8, 9]. Conducting friction and wear experiments involving the addition of randomly distributed reinforced particles to aluminium metal materials is a time-consuming and labor-intensive task. However, random distribution has been found to be widely applied in various fields [10–12]. For instance, in civil engineering, steel, concrete [13], and composite glass materials are extensively used in large-scale engineering structures like bridges and high-rise buildings. On the other hand, in the aerospace, aviation, high-speed train, and ship industries, metal matrix composites with randomly distributed fibrous and particulate reinforcing phases hold greater potential. Ahmad F et al. [14] conducted a triaxial compression test to investigate the response of randomly distributed fibers in reinforcing silt strength. The results of their study demonstrated that the addition of randomly distributed discrete fibers significantly improved the strength of damaged fly ash and coating fibers.
Nam Ho Kim et al. [15] conducted a wear rate test on metal materials using the reciprocating pin-disc method. However, their experiments solely focused on verifying the wear rate and did not delve into discussing the coefficient of friction. J.H. Lee et al. [16] performed numerical simulations to analyze the friction and wear behavior of polycarbonate based on a ball-disk friction and wear model. It is important to note that they assumed a deformation state of plane strain, rather than considering the three-dimensional nature of the pin-on-disk tests. Li Xin et al. [17] proposed a thermal-structural coupled finite element analysis method to predict wear in seals. Ryota Tamada et al. [18] developed a simulation to estimate the variation in heel/toe wear performance among tires. Saad Mukras et al. [19] proposed numerical integration schemes and parallel computation methodologies to analyze wear in bodies experiencing oscillatory contact using the finite element method. Hossein Ashrafizadeh et al. [20] investigated the impact of different particle densities on the wear of plates and established a relationship between shear-normal impact energy and wear rate during mechanical erosion. Mukras Saad [21] explored computer simulations and predictions of wear on mechanical components. However, there is limited research available on the friction model performance of particle-enhanced cast aluminum alloys using the finite element method. Additionally, there is a lack of relevant studies applying the finite element method to solve wear issues associated with discontinuities.
This study aims to investigate the friction and wear characteristics of the ZL109 metal material under conditions of random particle distribution using finite element simulation analysis techniques. The wear state at different time periods is predicted. Initially, friction coefficients and wear rates of cast aluminium alloys with varying particle contents were determined through friction and wear experiments. Subsequently, a finite element model was developed to simulate friction and wear in particle-enhanced cast aluminium alloys with different mass fractions. The wear rate obtained from the experiments was then incorporated into ABAQUS for wear simulation using the Fortran computer language. Finally, the validity of the finite element model was demonstrated through experimental verification, allowing for accurate prediction of the wear state of particle-reinforced cast aluminium alloys under different wear durations.
2.1 Modification of Ring Block Friction and Wear Equation Based on Archard
Based on the ring-block friction model (Fig. 1), The friction ring (Ф40mm*Ф40mm*10mm) and the friction block (30mm*7mm*6mm) constitute the friction pair.
While the Archard model is commonly used to describe wear at a microscopic level, the relationship between macroscopic wear size, wear volume, load, and slip distance may not always be linear [22, 23]. In the field of wear research, the wear volume is currently expressed as an Eq. (1) that correlates with both the load W and the slip distance L.
$$V={K_W}WL$$
1
where Kw is termed the dimensional wear coefficient and is conventionally quoted in units of mm3/N m.
Consider the scenario where the friction ring is not worn and is positioned on the friction block, bearing the normal load (W) perpendicular to the friction surface. In this case, we assume that the Archard wear model applies, which states that there is a constant dimensional wear coefficient at every position on the contact surface of the friction pair during wear. If \(\Delta\)is the wear dimension, r is ring radius, \({\beta _0}\) is coincidence angle of ring and block and \(\beta\)is contact angle change during friction, then
$$\frac{\Delta }{r}=1 - \cos {\beta _0}$$
2
Therefore, the wear volume is given by
$$V=B{r^2}(\frac{{\pi {\beta _0}}}{{180}} - \sin {\beta _0}\cos {\beta _0})$$
3
And, if the Archard model is considered locally, the relationship between local pressure and local stress is as follows
$${\text{d}}W=PrB\cos \beta {\text{d}}\beta$$
4
If is defined as \(\frac{W}{{2Br}}\) and N as\(L/r\), then
$$\frac{{{\text{d}}\Delta }}{{{\text{d}}N}}{\text{d}}\beta =4\pi {K_w}R{\text{d}}P$$
5
$$d\Delta =r\sin \beta {\text{d}}\beta$$
6
$$\begin{gathered} 2\pi {K_W}PN=\int_{0}^{{{\beta _0}}} {\beta \sin \beta } {\text{ d}}\beta \\ =\frac{{{\beta _0}^{3}}}{3} \\ \end{gathered}$$
7
and thus
$$\frac{V}{{B{r^2}}}=2\pi \left( {{K_W}PN} \right)$$
8
When the angle \({\beta _0}\) is small, equations (2) and (8) reduce to
$$\frac{\Delta }{r}=3.54{\left( {{K_W}PN} \right)^{{\raise0.5ex\hbox{$\scriptstyle 2$}\kern-0.1em/\kern-0.15em\lower0.25ex\hbox{$\scriptstyle 3$}}}}$$
9
2.2 Experimental procedure
2.2.1 Material and model
The test described in this paper focuses on the friction and wear characteristics of the ZL109 (GB/T 1173–1995) matrix, as well as the random distribution of Al2O3 particles added to the ZL109 matrix. The chemical composition ratios of the ZL109 aluminium alloy are presented in Table 1.
| Composition | Si | Mn | Cu | Ni | Mg | Pb | Ti | Be | Zn | Sn | Al |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Content (wt%) | 11.95 | 0.18 | 1.03 | 0.12 | 1.13 | 0.02 | 0.11 | 0.06 | 0.18 | 0.01 | Bal. |
In the experiment, Al2O3 particles with an average diameter of 1.5µm were added to the ZL109 powder and thoroughly mixed using mechanical mixing techniques. The mixed aluminium alloy powder was then added to an extruded mold and subjected to a pressure of 20 MPa for 15 minutes. Subsequently, the uniformly mixed aluminium alloy powder was pressed into an aluminium ingot within the extruded mold.
The cast aluminium alloy sample was polished using a metallographic polishing machine, resulting in a mirror-like surface that is free of scratches. Under SEM (scanning electron microscope) observation, the distribution of Al2O3 particle-reinforced cast aluminium alloy samples appeared to be uniform. The distribution of Al2O3 particles in the cast aluminium alloy can be seen in Fig. 2. Additionally, the statistical program IPP (Image-Pro Plus) was employed to count the particles in the SEM images of the aluminium alloy sample containing 8 wt% particle concentration. The statistical analysis revealed that the fraction of Al2O3 added closely matched the actual particle content that was added.
2.2.2 Friction and wear tests
The ring on block tests were carried out without the use of any lubrication. The tribological system setup for these tests involved a ring placed on top of a block. The specific dimensions of both the friction ring and the block can be observed in Fig. 3. The material utilized for the ring is GCr15. The nominal chemical composition of the ring material is provided in Table 2.
| C | Mn | Si | P | S | Cr |
|---|---|---|---|---|---|
| 0.91 ~ 1.05 | 0.25 ~ 0.45 | 0.15 ~ 0.35 | ≤ 0.025 | ≤ 0.025 | 1.40 ~ 1.65 |
Friction and wear test settings: The linear velocity of the friction ring is set to 0.431m/s, the test duration is 30 minutes, and the rotational speed is approximately 200RPM. Prior to the test, the contact surface of the friction ring was polished using 2000-mesh abrasive paper and then cleaned with acetone. A load of 50N was applied during the test. Figure 2 illustrates that the friction coefficient of the cast aluminium alloy with nano-particles exhibits minor fluctuations during the friction and wear tests.
After the friction and wear test, the friction samples were collected in order to calculate the wear rate of the cast aluminium alloy that was reinforced with varying amounts of alumina particles.
2.3 Wear simulation analysis
The friction and wear test was conducted using the M-200 ring block testing machine. Subsequently, a finite element model was developed to simulate wear, based on the schematic diagram of the friction test displayed in Fig. 3.
2.3.1 Wear finite element model and verification
A three-dimensional finite element simulation model, depicted in Fig. 4, was created using ABAQUS CAE. This model was based on the actual dimensions (mm) of the ring block model used in the MM-200 wear tester. The Hertz contact theory was employed to simulate the established finite element model, and a comparison was made with theoretical values to validate its accuracy and reasonableness.
In order to verify the correctness of the finite element model, the elastic modulus and Poisson's ratio of the friction pair (friction ring and specimen) are taken as 200 GPa and 0.3 respectively, and normal force is 500N. The half-width of the contact area, b, and the maximum contact pressure, \({P_c}\), at the contact center, are given by Eq. (9) and Eq. (10). The Hertz contact principle diagram is shown in Fig. 5.
$${b^2}=\frac{4}{\pi }r\left( {\frac{{1 - {\mu _1}^{2}}}{{{E_1}}}+\frac{{1 - {\mu _2}^{2}}}{{{E_2}}}} \right)P$$
9
$${P_c}=\frac{6}{{\pi {r^2}}} \cdot \frac{1}{{\frac{{1 - {\mu _1}^{2}}}{{{E_1}}}+\frac{{1 - {\mu _2}^{2}}}{{{E_2}}}}}$$
10
Where P is normal force, r is radius of the friction ring, \({E_1}\) and \({E_2}\) are the elastic modulus of the two contacts, \({\mu _1}\)and \({\mu _2}\)are Poisson’s ratios.
$$\mu =\frac{M}{{F \cdot r}}$$
11
In Eq. (11), µ denotes the coefficient of friction, M is moment of sliding friction, F indicates the load between the sample and the ring of friction, r is radius of the friction ring.
The numerical calculations yield a contact half-width of 0.128mm and a maximum contact pressure of 355.346MPa. However, the finite element analysis shows a contact half-width of 0.185mm and a maximum contact pressure of 361.669MPa (Fig. 6). The maximum contact stress exhibits a difference of only 1.7% when comparing the Hertz theory with the finite element solution. This close agreement between the two methods further validates the rationality of the finite element model.
The results of finite element simulation on the maximum contact pressure are similar to Hertz solution from Fig. 7.
2.3.2 Simulation model property settings
In the simulation process, the material of the friction ring is GCr15 (Table 3), the density of the friction sample is 2.7E-09 tonne/mm3, the Young's modulus is 4.2E + 05 MPa and the Poisson's ratio is 0.33.
| Conductivity | Density (Tonne/mm3) | Young’s Modulus (MPa) | Poisson’s Ratio | Expansion Coeff (1/\(^\circ {\text{C}}\)) | Specific Heat mJ/(Tonne∙\(^\circ {\text{C}}\)) |
|---|---|---|---|---|---|
| 44 | 7.83E-9 | 219000 | 0.3 | 1.203E-05 | 460E + 06 |
Setting of contact properties between friction pairs: The tangential contact is established using a penalty function, where the friction coefficient value is specified. To ensure fast convergence of the finite element analysis model, the normal contact is set as a soft contact. The Umeshmotion subroutine is utilized to determine the offset distance of the model element node. This subroutine calls for the contact pressure and other essential information from the second surface node.
Considering the complexity of the wear process, the analysis process is divided into three steps.In the first step, the friction ring remains fully constrained and fixed, while a small displacement of 0.01 mm is applied to the friction block. This displacement allows for the establishment of a stable contact relationship between the friction pairs.Moving on to the second step, the constraints on the movement of the friction ring load direction and its rotational freedom are released. This allows for further analysis.Finally, in the third step, the rotational speed of the friction ring is set at 200 RPM. Additionally, a load of 50 N is applied to the coupling point on the upper surface of the friction block (Fig. 8).
By the Archard model, a form suitable for ABAQUS finite element analysis is obtained after modification. Therefore, the offset of the node in the wear direction can be expressed as follow.
The offset of nodes on the contact surface and the update of the friction contact surface are achieved through the mesh adaptive technology of ABAQUS software and user subroutines. The simulation flow of ring and block wear is illustrated in Fig. 9.As depicted in Fig. 9, the UMESHMOTION subroutine is utilized in ABAQUS software to update the node position information. This subroutine retrieves the contact pressure (CPRESS) and contact sliding distance (CLDISP) of the contact surface nodes of the friction pair. These values are then used to calculate the node offset in the wear direction.
3.1 Experimental results
A normal load of 50N was applied to the upper surface of the friction sample, and the test duration was 0.5h. Figure 10 presents the tribological test results of ZL109 aluminum alloy reinforced with various contents of Al2O3 particles.
The friction coefficient of the cast aluminum alloy, without the addition of Al2O3 particles, exhibits significant fluctuations during the friction process. However, the fluctuation of the friction coefficient in the presence of Al2O3 particles is noticeably reduced compared to the alloy without them. Moreover, as shown in Fig. 11, repeating the test reveals that the average friction coefficient is the lowest when the particle content is 6%. As the particle content continues to increase, the friction coefficient also increases. This suggests that the addition of particles serves as a lubricating agent between the friction pairs.
Figure 12 illustrates the variation trend of wear rate in Al2O3 particle reinforced cast aluminium alloys with different mass fractions under fixed load conditions. The graph clearly indicates that the cast aluminium alloy exhibits a higher volume wear rate in the absence of particles. This can be attributed to the softening of the metal due to frictional heat, leading to plastic deformation and the bonding of wear debris on the friction coupler ring's surface, thereby exacerbating adhesive wear. However, the wear rate is significantly reduced when alumina particles are added to the cast aluminium alloy. This can be attributed to several factors. Firstly, the addition of reinforcing particles enhances the strength and hardness of the alloy compared to alloys without particles. Additionally, the bonding force between the base metals is weakened, which reduces plastic deformation near the particles. Consequently, this prevents peeling of the aluminium alloy material and reduces adhesive wear during friction. Secondly, as the particle content increases to a certain level, a balanced state is reached between the particles and wear debris, resulting in a synergistic effect that further reduces the wear rate.
The wear rate reached its lowest point at a particle content of 2%, measuring 2.03×10− 5 mm3N− 1m− 1. During friction, the shearing force causes numerous particles to detach from the aluminium alloy substrate, resulting in the formation of pits on the frictional contact surface. Simultaneously, extrusion shearing occurs between the asperities on the friction contact surface and the dislodged particles, leading to predominantly abrasive wear. Consequently, the wear rate exhibits a noticeable increasing trend at a particle content of 6%. To achieve a low wear rate, it is recommended to control the alumina particle content within the range of 2–4%.
3.2 Numerical results
During the friction and wear experiment, various complex physical and chemical changes take place. The heat generated during the wear process easily oxidizes the surface of the cast aluminium alloy composites, leading to intensified wear and higher wear rates. It is worth noting that the simulation calculation overlooks the temperature fluctuations at the contact interface. Additionally, the heat dissipation resulting from friction has a negligible impact on the mechanical properties of the friction contact surface, considering the speed and load [24]..
Figure 13 illustrates a comparison between the experimental and simulation results at a wear time of 0.5h. The findings indicate a strong agreement between the simulation and experimental results, thereby confirming the reliability of the finite element model. However, as depicted in Fig. 14, when the wear time is extended to 1h, the simulated values of the wear scar width are consistently lower than the curve of the experimental results. This outcome aligns with the real-world scenario. Due to the presence of wear debris and other environmental factors during the experiment, the degree of wear tends to be higher than what is predicted by the simulation results.
Figure 15 displays the stress cloud diagram obtained from the wear simulation. The diagram reveals that the region influenced by stress in the friction sample is skewed towards the left, which is determined by the rotation direction of the friction ring. As a result, the metal in proximity to the left side undergoes plastic deformation due to the extrusion from the friction ring.
Figure 16 showcases the trend of wear scar width variation in the wear simulation of cast aluminium alloys with different particle contents as the wear time increases. It can be observed that the wear scar widths for particle contents of 2%, 4%, 6%, and 8% exhibit a gradual increase when the wear time reaches 5h. However, the wear scar width trend continues to rise for the 0% and 10% particle content, which can be attributed to their inherently higher wear rates
According to the present study, the following conclusions have been drawn:
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The Hertz contact verification proves the rationality of the established finite element model.
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Comparing the width of the wear scar obtained through finite element simulation with the results obtained from the wear test, it was observed that the finite element simulation results corroborated the test results. This indicates the effectiveness of the wear finite element simulation analysis method.
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In the study of particle reinforced aluminium alloys, we did not take into account the wear scar of the samples and the intricate physical and chemical reactions that transpired during the friction and wear process.
Declaration of competing interest
The authors declare that they have no known competing financial interests and personal relationships with other people or organizations that could inappropriately influence (bias) their work.
CRediT author statement
Ganlin Qin: Conceptualization, Data curation, Methodology, Software, Writing - original draft. Hongding Wang: Resources, Conceptualization, Supervision, Writing - review & editing, Funding acquisition. Zhonglei Ma: Investigation, Visualization. Hong Liu: Resources, Supervision Funding acquisition. Zhengning Li: Resources, Supervision Funding acquisition.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (52361023); Natural Science Foundation of Gansu Province (1606RJZA013) and Innovation Foundation of Education Department of Gansu Province (2021A-038).
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No competing interests reported.