Enhanced high-temperature age-hardening behavior and mechanical properties of Al–Mg–Si alloys via microalloying with Cd

Lu, Gang; Sun, Bo; Wang, Jianjun; Liu, Yujie; Liu, Chunming

doi:10.1016/j.msea.2021.141910

Cited by 21 publications

(4 citation statements)

References 59 publications

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“…Many researchers have pointed out that the existence of PFZs can weaken mechanical properties. [31,32] However, the discontinuities in grain boundary precipitates and the width of PFZs are almost the same for the three specimens in this study. Therefore, it is considered that the effect of PFZs on the fatigue properties of the plate should be similar.…”

Section: Initial Microstructures Of the Platementioning

confidence: 55%

“…Moreover, it should be noted that ω is a defect-related parameter and with the increase of ω, the effect of damage localization caused by defects is amplified. [32] This phenomenon can be explained in Figure 10, which is based on the probability of the defect occurrence. When testing along the TD, the area of grain boundaries that is perpendicular to the force axis is larger compared with the test conducted along the RD.…”

Section: Positions Fatigue Limit [Mpa]mentioning

confidence: 99%

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High‐Cycle‐Fatigue Anisotropy of an Aluminum Alloy Superthick Plate

Wang,

Zhang,

Gong

et al. 2024

Adv Eng Mater

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The effect of anisotropy on high‐cycle fatigue (HCF) property along the rolling direction (RD) and the transverse direction (TD) of a 7××× aluminum alloy super thick plate is mainly investigated in this study. The results show a similar microstructure at different thicknesses, and the HCF properties are close as well. The fatigue fracture morphologies show that the fatigue crack sources are all located on the surfaces of the RD and TD specimens. By considering the differences in the HCF properties, there is only a slight anisotropy effect at the center thickness of the plate, which can be attributed to the tensile anisotropy caused by textures. Besides, the fatigue damage degree along the TD is slightly higher than that along the RD at the center thickness. The reason can be explained by two aspects: one is the higher probability of defect occurrence along the TD compared with that along the RD which is analyzed by the previously proposed Yield strength‐Tensile strength‐Fatigue strength model, and the other is the influence of the textures. The combination of hard and soft orientations may cause fatigue damage localization easily in the soft‐oriented grains which accelerates the fatigue failure.

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Section: Initial Microstructures Of the Platementioning

confidence: 55%

Section: Positions Fatigue Limit [Mpa]mentioning

confidence: 99%

High‐Cycle‐Fatigue Anisotropy of an Aluminum Alloy Superthick Plate

Wang,

Zhang,

Gong

et al. 2024

Adv Eng Mater

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“…In recent decades, it has been found that adding Cd [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ], In [ 7 , 8 ] and Sn [ 7 , 8 , 14 , 15 , 16 ] to Al-Cu alloys and adding Cu [ 17 , 18 , 19 ], Ge [ 20 ], and Ag [ 19 ] to Al-Mg-Si alloys could significantly promote the aging kinetics and improve the strength of the alloys. Microalloying improves the strength of the alloys by producing refined and homogeneous distributed strengthening phases with a higher density.…”

Section: Introductionmentioning

confidence: 99%

Effect of Cd on Mechanical Properties of Al-Si-Cu-Mg Alloys under Different Multi-Stage Solution Heat Treatment

Mao

Bai²,

Feng³

et al. 2022

Materials

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Multi-stage heat treatment is an important method to improve the mechanical properties of Al-Si-Cu-Mg aluminum alloys. In this paper, the multi-stage heat treatment was carried out for the Cd-free and Cd-containing alloys. The experimental results show that the addition of Cd promoted the precipitation of Q″ and θ″, which led to the formation of a large number of fine, dispersed precipitates and a higher yield strength (YS) and ultimate tensile strength (UTS) for the Cd-containing alloys. The addition of Cd also altered the optimal heat treatment parameters. For the Cd-free alloys, the Cu-rich phase fully dissolved after three-stage heat treatment, and the YS and UTS of the three-stage heat-treated alloys were higher than their two-stage heat-treated counterparts. For the Cd-containing alloys, the three-stage heat treatment led to the precipitation of Cd-rich low melting point phases, caused defects, and reduced the mechanical properties of the alloy. The size and volume fractions of the precipitates were significantly less than those of the alloys after two-stage heat treatment and the strength of the alloys decreased. Therefore, the solution time should be strictly controlled for Cd-containing Al-Si-Cu-Mg alloys.

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“…There are now some empirical formulas such as Formula (1) and Formula (2), the strength and conductivity can be estimated, but need many parameters (such as grain size, volume fraction, and size of precipitation phase) [20], and these parameters to obtain usually need statistics on the alloy microstructure [21][22][23]. It is difficult to obtain quickly, and the calculation is complicated.…”

Section: Introductionmentioning

confidence: 99%

Design of high strength and electrically conductive aluminium alloys by machine learning

Liang

Wang

Xue

et al. 2022

Materials Science and Technology

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Traditionally, thermodynamic modeling considers only the equilibrium conditions and one-dimensional evolution of phases. Therefore, it has difficulty in predicting the final properties of materials, especially when the functional and mechanical properties are correlated and heavily dependent on the combination of different phases which distribute in three dimensions. Recently, machine learning enabled us to establish the complex relationship between alloy compositions, processing conditions, various phases, and final properties. In this work, machine learning is coupled with thermodynamic calculations to optimise the alloy compositions, processing conditions, and the combinations of phases for improved electrical conductivity and mechanical property. Compared with previous chemistry design by machine learning for multiple inputs and single object outputs, the introduction of intermediate phases from thermodynamic calculations can improve the prediction accuracy. Combining machine learning with thermodynamic calculation is expected to optimise new alloys.

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Enhanced high-temperature age-hardening behavior and mechanical properties of Al–Mg–Si alloys via microalloying with Cd

Cited by 21 publications

References 59 publications

High‐Cycle‐Fatigue Anisotropy of an Aluminum Alloy Superthick Plate

High‐Cycle‐Fatigue Anisotropy of an Aluminum Alloy Superthick Plate

Effect of Cd on Mechanical Properties of Al-Si-Cu-Mg Alloys under Different Multi-Stage Solution Heat Treatment

Design of high strength and electrically conductive aluminium alloys by machine learning

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