2018
DOI: 10.1016/j.jallcom.2018.01.135
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Dislocation density based model for Al-Cu-Mg alloy during quenching with considering the quench-induced precipitates

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Cited by 17 publications
(5 citation statements)
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“…The presence of fine precipitates in the matrix hinders the dislocation motion and results in the strengthening effect, which mainly depends on the size, volume fraction, characteristics, and properties of the precipitates. According to literature [28,29], the interactions between dislocations and precipitated phases can be explained by two precipitation enhancement mechanisms, namely, the by passing and by cutting mechanisms [30]. The two strengthening mechanisms may appear in the aging process.…”
Section: Mechanical Propertiesmentioning
confidence: 99%
“…The presence of fine precipitates in the matrix hinders the dislocation motion and results in the strengthening effect, which mainly depends on the size, volume fraction, characteristics, and properties of the precipitates. According to literature [28,29], the interactions between dislocations and precipitated phases can be explained by two precipitation enhancement mechanisms, namely, the by passing and by cutting mechanisms [30]. The two strengthening mechanisms may appear in the aging process.…”
Section: Mechanical Propertiesmentioning
confidence: 99%
“…Lower cooling rates reduced the tensile strength and yield strength due to a decreased amount of fine precipitates, and reduced the residual stress with the reduction of plastic strain and the degree of inhomogeneous plastic deformation. Guo [16] established a dislocation density-based model for accurate prediction of residual stress for as-quenched Al-Cu-Mg alloy, which considered the influences of precipitation, solid solution and forest dislocation. The results showed that volume fraction of precipitates decreased with the increasing temperature and cooling rate, in contrast to the increasing trend for the radius of precipitates.…”
Section: Introductionmentioning
confidence: 99%
“…The previous study tried to build the constitutive model of as-quenched alloys, e.g. the physical model, the phenomenological model and the artificial neural network model (Guo and Wu, 2018; Yang et al , 2015; Chobaut et al , 2015; Li et al , 2017; Yang et al , 2012; Li et al , 2019; Wang et al , 2016; Wang et al , 2014; Wu and Guo, 2018; Robinson et al , 2012). Guo and Wu (2018) built a dislocation based physical model, which considered the quench-induced precipitates.…”
Section: Introductionmentioning
confidence: 99%