On the precipitation-hardening behavior of the Al-Mg-Si-Cu alloy AA6111

Esmaeili, Shahrzad; Wang, X.; Lloyd, D. J.; Poole, Warren J.

doi:10.1007/s11661-003-0110-4

Cited by 113 publications

(141 citation statements)

References 28 publications

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“…[31][32][33]37,38] 1. Problem description With a few exceptions, there seems to be general agreement that prolonged storage at RT and up to about 343 K (70°C) will reduce the yield strength of the alloys following artificial aging.…”

Section: A Framework For Modeling Cluster Formationmentioning

confidence: 99%

“…[37,38] This is because the clusters, which initially form at low temperatures, tend to survive for a certain period of time during artificial aging and therefore temporarily tie-up solute that otherwise would have been consumed in b¢¢ formation. [11,[31][32][33] At higher storage temperatures, the situation is the opposite due to GP zone formation, since GP zones may convert directly into strengthening b¢¢ particles on subsequent artificial aging. [37,38] However, this type of aging response is not an issue here.…”

Section: A Framework For Modeling Cluster Formationmentioning

confidence: 99%

“…Appropriate default values for these parameters can be obtained from well-established sources. [21,22,[28][29][30][31][32][33]57] In addition, the new yield strength model must be flexible enough to handle local variations in the mechanical properties caused by crystallographic texture, both with respect to orientation within a given aluminum plate or extrusion as well as between different aluminum alloys exhibiting either a recrystallized or a subgrain type of microstructure. [58] In NaMo-Version 2, this is done by allowing the Taylor factor to become an input variable rather than a default parameter in the yield strength model.…”

Section: Framework For Calculating the Macroscopic Yield Strengthmentioning

confidence: 99%

“…This type of thermomechanical processing is not dealt with in Version 1, although cold deformation operations like sheet forming and stretch bending in combination with prolonged room-temperature storage are known to strongly alter the subsequent artificial aging response of Al-Mg-Si alloys. [31][32][33] Therefore, incorporation of these two new stages in NaMo will largely increase the versatility of the model by allowing simulations of integrated production processes as well of the kind shown in Figure 2. To enable this, the important reactions contributing to the nanostructure evolution at each stage must first be identified (based on what is already known in the scientific literature) and then implemented, either in the existing NaMo sub-models, or in new ones.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Extended Age-Hardening Model for Al-Mg-Si Alloys Incorporating the Room-Temperature Storage and Cold Deformation Process Stages

Myhr

Grong

Schäfer

2015

Metall Mater Trans A

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In this article, a new age-hardening model for Al-Mg-Si alloys is presented (named NaMo-Version 2), which takes into account the combined effect of cold deformation and prolonged room-temperature storage on the subsequent response to artificial aging. As a starting point, the original physical framework of NaMo-Version 1 is revived and used as a basis for the extension. This is permissible, since a more in-depth analysis of the underlying particle-dislocation interactions confirms previous expectations that the simplifying assumption of spherical precipitates is not crucial for the final outcome of the calculations, provided that the yield strength model is calibrated against experimental data. At the same time, the implementation of the Kampmann-Wagner formalism means that the different microstructure models can be linked together in a manner that enforces solute partitioning and competition between the different hardening phases which form during aging (e.g., clusters, b¢¢ and b¢). In a calibrated form, NaMo-Version 2 exhibits a high degree of predictive power, as documented by comparison with experiments, using both dedicated nanostructure and yield strength data as a basis for the validation. Hence, the model is deemed to be well-suited for simulation of thermomechanical processing of Al-Mg-Si alloys involving cold-working operations like sheet forming and stretch bending in combination with heat treatment and welding.

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Section: A Framework For Modeling Cluster Formationmentioning

confidence: 99%

Section: A Framework For Modeling Cluster Formationmentioning

confidence: 99%

Section: Framework For Calculating the Macroscopic Yield Strengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Extended Age-Hardening Model for Al-Mg-Si Alloys Incorporating the Room-Temperature Storage and Cold Deformation Process Stages

Myhr

Grong

Schäfer

2015

Metall Mater Trans A

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“…Important findings which focus on the formation of clusters and their influence on the precipitation sequence are summarized in the following. 1) Natural aging [71,72] : Usually a double cluster peak is present for the solution heat treated material, and for NA only one of the two peaks is apparent. The double peak nature is attributed to the existence of two kinds of clusters, Cluster 1 (C1) and Cluster 2 (C2).…”

Section: Differential Scanning Calorimetrymentioning

confidence: 99%

Clustering in Age‐Hardenable Aluminum Alloys

et al. 2018

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This review gives an overview of the effects of clusters in various aluminum alloys. Characterization methods are discussed in general and results for the important AlMgSi alloys are presented in detail. Indirect characterization methods, such as hardness, tensile testing, electrical resistivity, differential scanning calorimetry, and positron annihilation spectroscopy are discussed, as well as atom probe tomography for the direct measurement of clusters. A particular focus is set on atom probe tomography, where possible artifacts influencing the cluster measurements as well as different cluster finding methods are summed up. A comprehensive summary of investigated alloys and cluster algorithm parameters is given. Moreover, the findings in AlMgSi alloys regarding clusters and changes upon different heat treatments are discussed, starting from early to the latest works. Drawn conclusions are discussed and compared to give a r esum e.

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The Effect of Cu and Cr on Clustering and Precipitation in Al‐Mg‐Si Alloys

Liang

Chang

Banhart

et al. 2012

ICAA13: 13th International Conference on Aluminum Alloys

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A group of alloys based on pure ternary Al-0.4 wt.%Mg-1.0 wt.%Si are used to study the effect of Cu and Cr on clustering and precipitation in Al-Mg-Si alloys. Differential Scanning Calorimetry (DSC) is performed for samples naturally aged for different times after solution heat treatment and ice water quenching. Three clustering processes are observed in all the alloys. The fundamental clustering sequence does not change by additional elements. However, the Cu containing alloy shows less clustering and the first clustering event is hindered. Kissinger analysis reveals that the 2 nd and 3 rd processes have very similar effective activation energies. Therefore, a model incorporating only two independent reactions is used to obtain kinetic parameters. It is found that the first clustering process starts with a low effective activation energy of 50 kJ/mol and has a mechanism similar to mixed nucleation while the latter two processes are governed by a higher activation energy of 79 kJ/mol and have a mechanism similar to particle growth. During precipitation, the Cr containing alloy shows a similar precipitation heat signal in DSC as the pure ternary, thus having negligible effect on precipitation. On the other hand, the formation of " is less dominant in the Cu-containing alloy while precipitation of other phases before reaching the peak-aged condition is possible.

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On the precipitation-hardening behavior of the Al-Mg-Si-Cu alloy AA6111

Cited by 113 publications

References 28 publications

An Extended Age-Hardening Model for Al-Mg-Si Alloys Incorporating the Room-Temperature Storage and Cold Deformation Process Stages

An Extended Age-Hardening Model for Al-Mg-Si Alloys Incorporating the Room-Temperature Storage and Cold Deformation Process Stages

Clustering in Age‐Hardenable Aluminum Alloys

The Effect of Cu and Cr on Clustering and Precipitation in Al‐Mg‐Si Alloys

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