Influence of aging &amp; varying weight fraction of Al<sub>2</sub>O<sub>3</sub> particles on the mechanical behaviour &amp; volumetric wear rate of Al 7075 alloy composite produced by liquid metallurgy route

In this study, magnesium alloy (AZ91D) matrix composites reinforced with 1 wt% of nano alumina (n-Al 2 O 3 ) were fabricated using novel Ultrasonic Treatment (UST)assisted squeeze casting method. UST was carried out at four different levels of ultrasonic power namely, 0 W (without UST), 1500 W, 2000 W and 2500 W at constant frequency and time. The composites were heat-treated at T6 condition under argon gas protected environment. Microstructural analysis was done using optical microscopy and high-resolution scanning electron microscopy. Physical, mechanical and tribological properties of the composites were evaluated. A significant refinement in grain structure and improvement in porosity was seen on an increase in UST power. Improvement was seen in microhardness, yield strength, ultimate tensile strength and % of elongation of the composite fabricated at 2500 W by 18%, 48%, 28%, and 10% respectively compared to an untreated composite. The composite fabricated at 2500 W showed less wear rate and coefficient of friction when compared to other composites at all sliding conditions. Scanning electron microscope images of the worn surface of the composite pins revealed that the wear mechanisms dominated were abrasion, adhesion, oxidation and delamination.

Section: Tribological Propertiesmentioning

confidence: 95%

Section: Worn Surface Analysismentioning

confidence: 99%

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Influence of ultrasonication power on grain refinement, mechanical properties and wear behaviour of AZ91D/nano-Al₂O₃ composites

Gnanavelbabu

Surendran

Kumar

2020

“…As GP zones expand, they convert into a semi coherent η′ phase, increasing the hardness effect of the Al7075 alloy matrix. The η′ phases evolve into incoherent phases as the aging time increases, increasing the hardness of the aged samples [39]. Even after aging in all three aging temperatures (120, 150, and 180 °C), peak hardness values of composites are much higher compared to Al7075 alloy and also as the percentage of reinforcements increases there is a significant increase in the peak hardness values of the composites.…”

Section: Methodsmentioning

confidence: 95%

Strengthening phase and mechanical property analysis of artificially aged Al7075 – Ni coated Al2024 composites

B M,

K M,

Sharma

et al. 2023

This technical paper demonstrates the possibilities of nickel (Ni) coated Al2024 powder reinforcement in an Al7075 matrix using the liquid stir casting technique. Additionally, the paper focuses on achieving stable properties by implementing artificial aging heat treatment. To apply the Ni coating, the electroless nickel plating technique was utilized, and a minimum coating thickness of 8 µm was determined to effectively prevent the dissolution of Al2024 powder reinforcements within the Al7075 matrix. Stir casting facilitated the uniform dispersion of the coated Al2024 powder up to a weight percentage of 7%. Subsequently, the Al7075 alloy and composites underwent artificial aging through solution heat treatment (SHT) at 450°C for 4 hours, followed by water quenching and aging at temperatures of 120, 150, and 180°C. Aging at 120°C was found to yield superior results compared to aging at 150 and 180°C, thus identified as the optimum aging temperature. When the Ni coating thickness was increased beyond the optimal 8 µm, the resulting enhancements in hardness for both as-cast and peak-aged specimens, as well as the tensile strength, were not significant. The improvements observed were only marginal, ranging between 2 to 3%. Fracture surface analysis revealed that the predominant fracture mode in the Al7075 alloy was ductile, characterized by dimple rupture. In the as-cast Al7075-(7%, 8 µm) Al2024 composite, a mixed fracture mode comprising both brittle and ductile characteristics was observed. In the peak-aged (120°C) Al7075-(7%, 8 µm)Al2024 composite, the overall fracture mode exhibited a dominant brittle nature. Analytical techniques including XRD, TEM, and EDS confirmed the presence of Mg2Si, MgZn2, CuAl2, and CuAl2Mg phases in the peak-aged (120°C) Al7075-(7%, 8 µm) Al2024 composite. These phases contributed to the enhancement of the properties of both the Al7075 alloy and its composites.

“…When compared to the alloy, the composites show a significant reduction in aging time. This acceleration is due to the presence of reinforcement particles, which improve the aging kinetics [37]. Higher TiB 2 and Si 3 N 4 content in composites results in a shorter aging time to attain peak hardness.…”

Section: Hardness and Ultimate Tensile Strengthmentioning

confidence: 99%

Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networks

Gowrishankar,

Doddapaneni,

Sharma

et al. 2023

This research work highlights the prediction of hardness behaviour of age-hardened LM4 and its composites fabricated using a two-stage stir casting method with TiB2 and Si3N4. MATLAB - Artificial Neural Networks is used to predict the age-hardening behaviour of LM4 and its composites. Experiments (hardness and tensile tests) are conducted to collect data for training an ANN model as well as to investigate the effect of reinforcements and age-hardening treatment on LM4 and its composites. The results show that with an increment in the reinforcement wt.%, there is an enhancement in hardness and ultimate tensile strength (UTS) values within the monolithic composites. As-cast hybrid composites display a 37 to 54% improvement in hardness compared to as-cast LM4. Heat-treated samples, specifically those treated with peak aging with MSHT and 100°C aging, perform better than as-cast samples and other heat-treated samples in terms of UTS and hardness. Compared to as-cast LM4, MSHT, and 100°C aged samples display an 85 to 202% increment in VHN. Hybrid composites perform better in terms of hardness, while composites with 3 wt.% of TiB2 (L3TB) perform better in terms of UTS, peak aged (MSHT and 100°C aging) L3TB display 68% increment in UTS when compared to as-cast LM4. ANN model is developed and trained with five inputs (wt.% of TiB2, wt.% of Si3N4, type of solutionizing, aging temperature, and aging time) and one output (VHN) using different algorithms and a different number of hidden neurons to predict the age hardening behaviour of composites. Among them, Lavenberg-Marquardt (LM) training algorithm with normalized data and 30 hidden neurons performs well and shows a least average error of 1.588364. The confirmation test confirms that the trained ANN model can predict the output with an average %error of 0.14 using unseen data.