Effect of pre-stretching and aging time on the mechanical properties and microstructure of Al-6.02Zn-1.94Mg alloy

Nie, Changchang; Huang, Yuanchun; Shao, Hongbang; Wen, Jinchuan

doi:10.1016/j.vacuum.2021.110553

Cited by 8 publications

(1 citation statement)

References 43 publications

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“…Plastic deformation before aging can generate a large number of dislocations, which is the preferential nucleation site of T 1 phase, therefore promoting the nucleation of T 1 phase and inhibiting the nucleation of other precipitates. [31][32][33][34] The T 1 phase serves as the dominant strengthening phase compared to the other precipitated phases, and the influences of heating/cooling rate on precipitation behaviors of the T 1 phase and mechanical properties were analyzed as follows. It is known that the aging process of Al alloy includes three stages: nucleation, growth, and coarsening, which are closely related to temperature and time.…”

Section: The Role Of Heating/cooling Rate On the Microstructure Of 20...mentioning

confidence: 99%

Effect of the Heating/Cooling Rate of Nonisothermal Aging on Microstructure and Mechanical Properties of 2050 Al–Li Alloy

Xu,

Tang,

et al. 2024

Adv Eng Mater

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The effect of the heating/cooling rate of non‐isothermal aging (NIA) on microstructure and mechanical properties of 2050 Al‐Li alloy were investigated. The 2050 Al‐Li alloy was first solid‐solutioned and pre‐deformation, and then heated from 130 °C to 220 °C at different rate of 20 °C/h, 30 °C/h or 40 °C/h and then cooled to 150 °C. The results show that the mechanical properties of the alloy subjected to NIA treatment could be improved by adjusting the heating and cooling rate. When the heating and cooling rate was 30 °C/h, the alloy achieved the best mechanical properties, the hardness, ultimate tensile strength (UTS), yield strength (YS), and elongation were 182.9 HV, 597 MPa, 571 MPa, and 9.6%, respectively. With the decrease of heating (or cooling) rate of NIA, the diameter and thickness of the T1 precipitate increased and the volume fraction decreased, resulting in the variation in mechanical properties. Therefore, in the present study, premier precipitation behavior and improved mechanical properties was both achieved by modifying the heating and cooling rate of NIA, the effect of the heating/cooling rate of NIA on the microstructure evolution of 2050 Al‐Li alloy was revealed, the underlying strengthening mechanism was investigated in detail.This article is protected by copyright. All rights reserved.

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Section: The Role Of Heating/cooling Rate On the Microstructure Of 20...mentioning

confidence: 99%

Effect of the Heating/Cooling Rate of Nonisothermal Aging on Microstructure and Mechanical Properties of 2050 Al–Li Alloy

Xu,

Tang,

et al. 2024

Adv Eng Mater

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Machine Learning‐Based Strength Prediction for Two‐Stage Aged 7050 Aluminum Alloy Forgings in Aircraft Main Support Joints

Liu,

Qian,

Huang

et al. 2024

Adv Eng Mater

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Aluminum alloys, widely regarded as lightweight structural materials, are extensively used in the aerospace industry. The aging process is essential for reducing residual stresses and ensuring alloys quality. Traditional methods for optimizing aging are often time‐consuming and expensive. In contrast, machine learning (ML) accelerates material design and performance prediction, significantly minimizing the need for extensive experimentation. In this study, the 7050 aluminum alloy forgings in aircraft main support joints are selected as the research object. A forward prediction model is developed using common ML algorithms, incorporating two‐stage aging process parameters and microstructural features as inputs, with yield strength (YS) and ultimate tensile strength (UTS) as outputs. The results demonstrate that the extreme gradient boosting regression model is the most effective for predicting the strength of aluminum alloys, with R2 values exceeding 0.7. By the Shapley additive explanation (SHAP) method and microscopic morphology analysis, the second‐stage aging time (t2) significantly influences YS and UTS. Hence, t2 was selected as the output for constructing the reverse classification model. The support vector machine classification model exhibits optimal performance, attaining macro‐accuracy and macro‐recall rates of 0.91 and 0.90, respectively.

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Study on the inhomogeneity of Al–Zn–Cu–Mg aluminum alloy plates with different thickness

Che

Xiao

et al. 2022

J Mater Sci

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Effect of pre-stretching and aging time on the mechanical properties and microstructure of Al-6.02Zn-1.94Mg alloy

Cited by 8 publications

References 43 publications

Effect of the Heating/Cooling Rate of Nonisothermal Aging on Microstructure and Mechanical Properties of 2050 Al–Li Alloy

Effect of the Heating/Cooling Rate of Nonisothermal Aging on Microstructure and Mechanical Properties of 2050 Al–Li Alloy

Machine Learning‐Based Strength Prediction for Two‐Stage Aged 7050 Aluminum Alloy Forgings in Aircraft Main Support Joints

Study on the inhomogeneity of Al–Zn–Cu–Mg aluminum alloy plates with different thickness

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