This paper analyses the influence of graphite reinforcement, load and sliding speed with constant sliding distance on tribological behavior of A356 aluminum matrix composites reinforced with 10 wt.% silicon carbide and graphite using the Taguchi design. Hybrid composites were produced in the compo-casting process. Tribological tests were performed on a block-on-disc tribometer where the weight percentage of graphite has three variations (0, 3, and 5), as well as load (10 N, 20 N, and 30 N) and sliding speed (0.25 m/s, 0.5 m/s, and 1 m/s), with sliding distance of 300 m. The wear of the composite is investigated under dry sliding condition. The specific wear rate was analyzed using Taguchi method with the aim of finding the optimal parameters. By applying analysis of variance, it was determined that the best tribological properties has A356/10SiC/3Gr hybrid composite. It was also found that the greatest impact on specific wear rate has load with the percentage effect of 69.163%, then sliding speed with 14.426% and the interaction between wt.% graphite and load. The dominant wear mechanism is adhesive wear as confirmed by scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS).
Purpose This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate. Design/methodology/approach These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods. Findings The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed. Originality/value The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.
The optimization of wear rate of the nanocomposites with A356 aluminium alloy matrix, reinforced with silicon carbide nanoparticles, was performed through the analysis of the following influences: wt% of the reinforcement, normal load and sliding speed. The nanocomposites were produced by the compocasting process with mechanical alloying preprocessing (ball milling). Three different amounts of SiC nanoparticles, with the same average size of 50 nm, were used as reinforcement, i.e. 0.2, 0.3 and 0.5 wt%. Tribological tests were performed on block-on-disc tribometer (line contact) under lubricated sliding conditions, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and at sliding distance of 1000 m. Analysis of variance (ANOVA) was applied to determine the influence of different parameters on wear value of tested nanocomposites. It was noticed from ANOVA analysis that normal load, with 33.39%, is the most significant factor affecting the wear rate of nanocomposites. The amount of reinforcement, with 28.90%, also has a significant influence on the wear rate, while the influence of sliding speed, with 23.82%, is smaller. It was found that the prediction of wear rate, by using regression model and Taguchi analysis, were close to the experimental values.
The development of new lightweight and strong materials and the design of new products are among the key elements for the development of new advanced construction and vehicle parts for the automotive industry. The use of composite materials in the automotive industry has been popular in recent decades due to the need to reduce vehicle weight, which directly affects fuel consumption and exhaust gases emission. In this way, the development of improved new materials with improved performance is accomplished. Nanocomposites represent a new class of materials that has excellent thermal and mechanical properties. The application of nanocomposites for development of automotive components is reflected in the improvement of the production rate, environmental and thermal stability, and the reduction in weight in the automotive industry, less wear parts, and indirectly to reduce CO 2 emissions and environmental pollution. This research paper presents a review of the application of nanocomposites (metal, ceramic and polymeric) in the automotive industry.
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