Yongxin Zhou, Qian Li, Zhiguo Xing, Renze Zhou, Zhenhua Huang, Yanfei Huang and Weiling Guo
This paper aims to investigate the effect of aluminum addition on the microstructure and mechanical properties of Mg-8Gd-4Y-1Zn alloy.
Abstract
Purpose
This paper aims to investigate the effect of aluminum addition on the microstructure and mechanical properties of Mg-8Gd-4Y-1Zn alloy.
Design/methodology/approach
Mg-8Gd-4Y-1Zn-xAl (x = 0, 0.5, 1.0, 1.5, 2.0 Wt.%) alloys were prepared by the conventional gravity casting technology, and then microstructures, phase composition and mechanical properties were investigated by material characterization method, systematically.
Findings
Results show that the as-cast microstructure of Mg-8Gd-4Y-1Zn alloy mainly consists of a-Mg matrix as well as Mg12REZn (18 R LPSO structure), and island-like Mg3(RE, Zn) phase is distributed at the grain boundary. The addition of a small amount of Al (0.5 Wt.%) can decrease the content of island-like Mg3(RE, Zn) phase, but significantly increase the content of long-period stacking ordered (LPSO) structure, resulting in the improvement of both tensile strength and elongation of Mg-8Gd-4Y-1Zn alloy. However, the addition of excessive Al will consume Re element and decrease the amount of LPSO structure, leading to the decrease of tensile properties. When the content of Al is 0.5 Wt.%, the tensile strength and elongation are 225 MPa and 9.0% of Mg-8Gd-4Y-1Zn alloy, which are 14% and 29% higher than that of Mg-8Gd-4Y-1Zn alloy, respectively.
Originality/value
Adding aluminum to Mg-8Gd-4Y-1Zn alloy strengthens its mechanical properties. And the effect of Al content on the alloy strengthening. The formation mechanism of LPSO structure with different aluminum content was revealed.
Details
Keywords
Renze Zhou, Zhiguo Xing, Haidou Wang, Zhongyu Piao, Yanfei Huang, Weiling Guo and Runbo Ma
With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in…
Abstract
Purpose
With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,
Design/methodology/approach
A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.
Findings
A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.
Practical implications
The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.
Originality/value
A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.