Tribological behavior of high-entropy alloy particle reinforced aluminum matrix composites and their key impacting factors

Zhang, Yun; Lei, Gang; Luo, Kaiguang; CHEN, Pinghu; Kong, Charlie; Yu, Huimin

doi:10.1016/j.triboint.2022.107868

Cited by 34 publications

(9 citation statements)

References 47 publications

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“…The enclosed Section A given in Figure 11c is magnified in Figure 11d, where the large amount of adhered buildup material at the side confirms the plastic deformation due to adhesion wear [37]. The adhesion wear is also the prevalent wear mechanism for the case of the 37 mm radius, but the creation of higher contact stresses [38] at the nearer radius (i.e., 25 mm) demonstrated more visible features of plastic deformation as depicted in Figure 11d in contrast to Figure 11b. Based on this, a wider abrasion groove is observed for the 25 mm radius in Figure 11c than the groove for the 37 mm radius given in Figure 11a.…”

Section: Wear Image Analysis Of Typical Wear Tracks On Discsmentioning

confidence: 74%

“…The enclosed Section A given in Figure 11c is magnified in Figure 11d, where the large amount of adhered buildup material at the side confirms the plastic deformation due to adhesion wear [37]. The adhesion wear is also the prevalent wear mechanism for the case of the 37 mm radius, but the creation of higher contact stresses [38] at the nearer radius The wear track attributes of the 600 m sliding distance at 30 mm and 17 mm radii with the same wear-testing conditions of WAAM samples are presented in Figure 12a-d. The SEM images shown in Figure 12a,c depict the extended widths of abrasion grooves for both tracks.…”

Section: Wear Image Analysis Of Typical Wear Tracks On Discsmentioning

confidence: 74%

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Wear Behavior Assessment of New Wire-Arc Additively Manufactured Surfaces on AA6061 and AA5086 Alloys through Multi-Walled Carbon Nanotubes and Ni Particles Inducement

Muzamil,

Iqbal,

Anwar

et al. 2024

Coatings

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This study investigates the new surface development on AA6061 and AA5086 alloys considering the wire-arc additive manufacturing technique as a direct energy deposition (DED) process of wire. Two different quantities of MWCNTs, i.e., 0.01 (low) and 0.02 (high) g, with a constant nickel (Ni) weight (0.2 g) were pre-placed in the created square patterns. ER4043 filler was used as a wire for additive deposition, and an arc was generated through a tungsten inert gas (TIG) welding source. Furthermore, hardness and pin-on-disk wear-testing methods were employed to measure the changes at the surfaces with the abovementioned inducements. This work was designed to illustrate the hardness and the offered wear resistance in terms of mass loss of the AA6061 and AA5086 aluminum alloys with the function of nano-inducements. Two sliding distance values of 500 m and 600 m were selected for the wear analysis of mass loss from tracks. A maximum increase in hardness for AA6061 and AA5086 alloys was observed in the experiments, with average values of 70.76 HRB and 74.86 HRB, respectively, at a high mass content of MWCNTs. Moreover, the tribological performance of the modified surfaces improved with the addition of MWCNTs with Ni particles in a broader sense; the modified surfaces performed exceptionally well for AA5086 compared to AA6061 with 0.02 and 0.01 g additions, respectively. The system reported a maximum of 38.46% improvement in mass loss for the AA5086 alloy with 0.02 g of MWCNTs. Moreover, the morphological analysis of the developed wear tracks and the mechanism involved was carried out using scanning electron microscope (SEM) images.

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Section: Wear Image Analysis Of Typical Wear Tracks On Discsmentioning

confidence: 74%

“…The enclosed Section A given in Figure 11c is magnified in Figure 11d, where the large amount of adhered buildup material at the side confirms the plastic deformation due to adhesion wear [37]. The adhesion wear is also the prevalent wear mechanism for the case of the 37 mm radius, but the creation of higher contact stresses [38] at the nearer radius The wear track attributes of the 600 m sliding distance at 30 mm and 17 mm radii with the same wear-testing conditions of WAAM samples are presented in Figure 12a-d. The SEM images shown in Figure 12a,c depict the extended widths of abrasion grooves for both tracks.…”

Section: Wear Image Analysis Of Typical Wear Tracks On Discsmentioning

confidence: 74%

Wear Behavior Assessment of New Wire-Arc Additively Manufactured Surfaces on AA6061 and AA5086 Alloys through Multi-Walled Carbon Nanotubes and Ni Particles Inducement

Muzamil,

Iqbal,

Anwar

et al. 2024

Coatings

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“…On the other hand, F. Ali et al [6] reported that quasi-crystalline material reinforcement can help improve the frictional energy and mechanical properties of AMCs, but the quasi-crystalline reinforced phase cannot be maintained for a long time due to its tendency to decompose in service and is limited in service temperature [7]. In addition, high entropy alloys are also a way to improve the frictional properties of AMCs, Yun Zhang et al [8] found that the addition of Al-0.5Co-Cr-Fe-Ni (1.5 wt%) high entropy alloy improved the AMCs and the coe cients of friction and wear rate were about 0.4586 and 0.32~0.4×10 -3 mm 3 •(N•m) -1 at 30 N load, which were 11.81% and 72.85% lower than that of the base material. 4586 and 0.32~0.4×10 -3 mm 3 •(N•m) -1 at 30 N load, which were 11.81% and 72.85% lower than those of the base material, but the high entropy alloys had more components and the composition and properties were di cult to adjust, resulting in the loss of the advantages of cheap and light [9].…”

Section: Introductionmentioning

confidence: 99%

Synergistic effect of Fe-amorphous and bionic microtexture in enhancing high-temperature tribological properties of Al-12Si piston materials

Wang,

Lin,

Yin

et al. 2024

Preprint

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This study designs new Fe-amorphous/Al-12Si piston composite materials. Study the effect and synergistic mechanism of the addition of Fe-amorphous and bionic micro-textured laser surface on the high-temperature friction performance of Al-12Si piston material under mixed lubrication conditions of B30 biodiesel and engine lubricating oil. The results indicate that the frictional properties of the untextured surface of the Fe-amorphous/Al-12Si composite material depend primarily on the amount of Fe-amorphous added. The 10 wt% Fe-amorphous/Al-12Si composite exhibits a dense, void-free microstructure with optimum anti-friction and anti-wear performance. It is noteworthy that the interaction between the "anchoring" effect caused by the Fe-amorphous addition and the synergistic effect of the bionic microtexture providing a stable lubricating environment further enhances the high-temperature friction properties of Al-12Si.

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“…Presently, particulate-reinforced aluminum matrix composites are receiving increasing attention due to their cost-effectiveness and beneficial isotropic properties. Aluminum alloys, due to their low weight and excellent thermal conductivity, have become a favored engineering material in industries such as automotive and aviation [13][14][15][16][17]. These industries utilize such alloys for various high-performance components in numerous applications.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

Mishra

Jatti

2023

Lubricants

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This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with various weight percentages of SiC (0%, 3%, and 5%), employing comprehensive spectroscopic analysis. The effect of SiC integration on the compositional distribution and ratio of elements within the composite is meticulously examined. In a novel move for this field of research, the study introduces and applies neurosymbolic programming as a novel computational modeling approach. The performance of this cutting-edge methodology is compared to a traditional simple artificial neural network (ANN). The neurosymbolic algorithm exhibits superior performance, providing lower mean squared error (MSE) values and higher R-squared (R2) values across both training and validation datasets. This highlights its potential for delivering more precise and resilient predictions, marking a significant development in the field. Despite the promising results, the study recognizes that the performance of the model might vary based on specific characteristics of the composite material and operational conditions. Thus, it encourages future studies to authenticate and expand these innovative findings across a wider spectrum of materials and conditions. This research represents a substantial advancement towards a more profound understanding of wear rates in Al/SiC MMCs and emphasizes the potential of the novel neurosymbolic programming in predictive modeling of complex material systems.

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Tribological behavior of high-entropy alloy particle reinforced aluminum matrix composites and their key impacting factors

Cited by 34 publications

References 47 publications

Wear Behavior Assessment of New Wire-Arc Additively Manufactured Surfaces on AA6061 and AA5086 Alloys through Multi-Walled Carbon Nanotubes and Ni Particles Inducement

Wear Behavior Assessment of New Wire-Arc Additively Manufactured Surfaces on AA6061 and AA5086 Alloys through Multi-Walled Carbon Nanotubes and Ni Particles Inducement

Synergistic effect of Fe-amorphous and bionic microtexture in enhancing high-temperature tribological properties of Al-12Si piston materials

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

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