Mechanical Properties Evaluation of Zr Addition in L12-Al3(Sc1−x Zr x ) Using First-Principles Calculation

Qian, Yi; Xue, Junmin; Wang, Zengjie; Yang, Zhenhai; Qian, Ping

doi:10.1007/s11837-016-1880-7

Cited by 12 publications

(2 citation statements)

References 38 publications

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“…This meant that Zr doping could improve stability, while the Ti/Y/Li dopings might reduce the structural stability. This was consistent with results from previous studies [23,30]. In addition, it was found that the stability of Al 24 Sc 6 Zr 2 was higher in comparison to Al 24 Sc 7 Zr.…”

Section: Resultssupporting

confidence: 93%

The Effect of Alloying Elements on the Structural Stability, and Mechanical and Electronic Properties of Al3Sc: A First-Principles Study

et al. 2019

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The first-principles methods, based on the density function theory, are performed to calculate the properties of pure and doped Al3Sc. The structural stability, and mechanical and electronic properties of L12-Al3Sc1−xMx (M = Zr, Ti, Y, and Li) have been investigated. A negative formation enthalpy for L12-Al3Sc1–xMx indicated that all doped structures were stable, and Al24Sc6Zr2 was found to be the most stable. The elastic constants, elastic moduli and Debye temperatures of Al3Sc, with different doping elements and different doping concentrations, were calculated to explore the influences of doping on the mechanical properties and Debye temperatures of Al3Sc. Furthermore, the calculated results suggested that both Al24Sc6Zr2 and Al24Sc6Ti2 could optimize the mechanical properties. Finally, the electronic properties based on the analyses of densities of states and electron density distributions, have been performed, to explain the underlying mechanisms for the structural and mechanical properties of the L12-Al3Sc1–xMx structures.

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Section: Resultssupporting

confidence: 93%

The Effect of Alloying Elements on the Structural Stability, and Mechanical and Electronic Properties of Al3Sc: A First-Principles Study

et al. 2019

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“…Immune algorithms are designed based on the immune process of the natural immune system [14]. Among them, the ICA designed by De Castro, named artificial immune network algorithm (aiNet) [13], has been widely used for data compression and clustering [6,10,12,22].…”

Section: Immune Clustering Algorithmmentioning

confidence: 99%

A Neural Network Classification Model Based on Covering and Immune Clustering Algorithm

Zhao¹

2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Inspired by the information processing mechanism of the human brain, the artificial neural network (ANN) is a classic data mining method and a powerful soft computing technique. The ANN provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. The constructive neural network (CNN) is a popular emerging neural network model suitable for processing largescale data. In this paper, a novel neural network classification model was established based on the covering algorithm (CA) and the immune clustering algorithm (ICA). The CA is highly comprehensible, and enjoys fast computing speed, and high recognition rate. However, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. To solve the problem, the ICA was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. The CA and the ICA work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. The number of hidden layer neurons equals the number of covering. In addition, the McCulloch-Pitts (M-P) neurons were adopted for the output layer. Based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. The introduction of the ICA fully solves the defect of the CA. Finally, our neural network classification model was verified through experiments on real-world datasets.

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Aluminium Scandium Alloys

Dorin

Mariappan

Vahid

et al. 2018

Fundamentals of Aluminium Metallurgy

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Mechanical Properties Evaluation of Zr Addition in L12-Al3(Sc1−x Zr x ) Using First-Principles Calculation

Cited by 12 publications

References 38 publications

The Effect of Alloying Elements on the Structural Stability, and Mechanical and Electronic Properties of Al3Sc: A First-Principles Study

The Effect of Alloying Elements on the Structural Stability, and Mechanical and Electronic Properties of Al3Sc: A First-Principles Study

A Neural Network Classification Model Based on Covering and Immune Clustering Algorithm

Aluminium Scandium Alloys

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