1. Introduction
Due to their distinctive characteristics and versatile applications across different domains, composite materials have attracted considerable interest and attention. In this regard, researchers have explored different methods with which to fabricate advanced composite materials with enhanced properties. One of these methods is the replication of NaCl space holders, which allows precise control over the porosity and morphology of the resulting composite material [
1,
2,
3].
Researchers have conducted extensive studies on open-cell aluminum-metal-matrix composites (AMMCs) due to their remarkable properties, which have found widespread applications in various functional and structural engineering applications. For instance, AMMCs that are reinforced with graphite are used to develop wear-resistant and lightweight cylinder liners for internal combustion engines [
4]. Furthermore, AMMCs that are reinforced with SiC are employed in the manufacturing of energy-saving brake rotors used in automobiles due to their high thermal conductivity and wear resistance [
5].
The replication method is one of the most commonly employed processes for producing AMMCs due to its simplicity in the experimental part and the high level of control it offers over the size, form, and pore distribution of the final porous AMMC structure (AMMC skeleton) [
6]. This method involves the fabrication of a preform, which may be composed of various space holders, such as carbamide [
7], NaCl [
8,
9,
10,
11,
12,
13], magnesium [
14], Acrowax [
15], ammonium bicarbonate [
16], saccharose [
17], and potassium carbonate [
18]. Among these space holders, NaCl is one of the most prominent; it has been extensively studied and used in the fabrication of porous metal structures.
Various types of reinforcing phase (RP) are incorporated to enhance the hardness and wear resistance of the softer matrix in AMMCs. Ceramic materials, such as silicon carbide (SiC) [
19,
20,
21,
22], alumina (Al
2O
3) [
19,
23,
24,
25], boron carbide (B
4C) [
26], titanium carbide (TiC) [
27,
28], and graphite (Gr) [
29,
30] are some of the most commonly used RP materials due to their excellent tribological behavior. In particular, SiC is an appropriate option for reinforcing AMMCs due to its high tensile and compressive strength, hardness, stiffness, and thermal stability, as well as its excellent wear resistance.
Liquid-state processing is a widely used method for the production of AMMCs due to its cost-effectiveness and the ability it offers to distribute reinforcement effectively. This method involves mixing the metal matrix and reinforcement material in a liquid state, which allows the uniform distribution of the reinforcement material throughout the matrix. Additionally, this process enables the use of a wide range of reinforcement materials with varying properties, shapes, and sizes, which provides flexibility in the design of composites. By using the liquid-state-processing technique, it is possible to manufacture intricate geometries and near-net-shaped components, resulting in decreased material wastage and machining expenses. These advantages make liquid-state processing a popular and efficient method for the fabrication of AMMCs [
31,
32,
33].
The authors of [
34] investigated the effect of combined squeeze-casting and stir-casting processes on the microstructural, mechanical, and wear properties of Al5083 reinforced with 20–30 wt. % SiC
p. The experimental findings indicated that the combination of casting techniques enhanced the dispersion of the SiC
p, minimized the porosity, and improved the mechanical and wear properties in comparison to using only the stir-casting method. The primary wear mechanism observed in all the composites was the delamination of the friction layer. Khadijeh M. et al. [
35] were successful in synthesizing AMMCs using a process that involved the pressure-assisted melt infiltration of Al into SiC preforms. The addition of water to the process led to improved wetting and bonding between the metal and preform, resulting in AMMCs with higher compressive-strength values compared to other materials. The composites demonstrated elevated levels of hardness, with the Al-SiC (75 wt. %) composite displaying the greatest compressive strength. This strength was attributed to the Orowan strengthening mechanism. Furthermore, the development of a tribological layer on the surfaces of the Al-SiC (67 wt. %) and the Al-SiC (75 wt. %) resulted in improved wear resistance in comparison to the Al alloy. According to Jiang W et al. [
36] the addition of 2 wt. % of SiC particles as reinforcements combined with the use of squeeze casting is an effective way to improve the tensile and yield strength, as well as the hardness, of 6082 aluminum alloys due to the more uniform distribution of the reinforcement. Du Yuan and Xiong Yang [
37] reported the development of high-strength and high-toughness nano-SiCp/A356 composites using ultrasonic vibration and squeeze-casting techniques, demonstrating that the addition of 2 wt. % nano-sized SiC particles significantly improves the mechanical properties of composite materials. The study by V. Chakkravarthy et al. [
38] revealed that the addition of TiN, SiC, and Nb particles improves the microhardness and wear resistance of composite materials. Their study concludes that the microstructural changes induced by the addition of these particles are responsible for improvements in the mechanical properties and wear resistance of composite materials. The authors Navya Kota et al. [
39] reviewed the current status and applications of interpenetrating phase composites (IPCs), discussed various processing routes for fabricating open porous ceramic preforms, and evaluated different melt-infiltration techniques based on the results presented by other authors. They summarized that Al-Si/SiC IPCs can be fabricated by infiltrating SiC foams obtained via the replication method using Al-Si alloys through the squeeze-casting method. The composites showed significant improvements in hardness compared to the unreinforced Al-Si alloy, with the finer-pore-size foam composite demonstrating the greatest hardness. The SEM analysis confirmed a well-bonded interface with no intermediate reaction products.
Machine learning (ML) is an area of artificial intelligence that helps computer systems to enhance their performances through experience and without the requirement of explicit programming. It involves the development of algorithms and statistical models that can analyze and draw insights from data to make predictions or decisions. Machine learning has been widely applied in different fields for over two decades, and these applications have expanded beyond computer science. In particular, the integration of ML and material science has enabled the prediction of output measurements, such as hardness [
40], tensile strength [
41], the volume loss of AA7075-Al
2O
3 composites [
23], and the additive manufacturing of AlSi10Mg-TiCN composites with tailorable mechanical behavior [
42]. By utilizing machine-learning techniques, scientists and engineers can analyze complex data sets to make predictions about the behavior of materials, improving the efficiency and effectiveness of materials research. Furthermore, ML methods can produce encouraging outcomes with relatively small datasets, even though they necessitate substantial amounts of data. However, further elaborations on the specific applications and benefits of machine learning in various fields could help to provide a more comprehensive understanding of its utility.
The aim of this study was to investigate the tribological behavior of open-cell AlSi10Mg-SiC composites that were produced using a novel fabrication technique. The pin-on-disk method was employed to evaluate the wear parameters, including the mass wear and coefficient of friction (COF), under dry sliding-friction conditions at ambient temperature. Scanning-electron microscopy (SEM) was also conducted to investigate the wear mechanisms, while the elemental composition of the composites was examined using X-ray energy-dispersive spectroscopy (EDS). The results of this study provide valuable insights into the potential applications of AlSi10Mg-SiC composites and their tribological behavior under specific working conditions.
In addition, machine-learning algorithms were utilized to predict the COF of the AlSi10Mg-SiC composites under dry sliding conditions. Three different ML models, namely Random Forest, Decision Tree, and Extreme Gradient Boosting, were implemented to predict the wear behavior of the composites. The performances of these models were evaluated on the validation and test sets using various metrics, such as the coefficient of determination (R2), mean squared error (MSE), root mean squared Error (RMSE), and mean absolute error (MAE).
The AlSi10Mg alloy has good castability, high strength, and excellent corrosion resistance. On the other hand, SiC is recognized for its exceptional strength, hardness, and stiffness. The combination of the squeeze-casting process and the incorporation of SiC particles into the AlSi10Mg matrix produced a porous open-cell-composite test specimen with enhanced tribological and mechanical properties, making it suitable for a range of applications, such as the production of slide-contact bearings. This research builds upon previous studies in which open-cell AMMCs were produced employing the replication method [
43], and their skeletons were infiltrated with tin-based babbitt [
12,
13,
44,
45].