Research and Development of Wrought Magnesium Alloys in China

Pan, Fu Sheng; Yang, Mingshun; Zhang, Ding Fei; Wang, Ling Yun; Ding, Peng

doi:10.4028/0-87849-968-7.413

Cited by 4 publications

(4 citation statements)

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“…Because of their low density, magnesium alloys have attracted an increasing interest in transportation, aeronautical and aerospace industries for the past decade, but their mechanical properties and processing performances still could not meet the needs of some important parts in vehicles and other important application fields [1][2][3][4] . Many new types of magnesium alloys are being developed in the world in order to further improve the mechanical properties and processing performances of the magnesium alloys [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

An improved neural network model for prediction of mechanical properties of magnesium alloys

Tang

Liu

Pan

et al. 2009

Sci. China Ser. E-Technol. Sci.

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An improved neural network model was developed for prediction of mechanical properties in the design and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm. The results showed that the improved model apparently decreased the prediction errors, and raised the accuracy of the prediction results. Better preprocessing parameters were found to be [0.15, 0.90] for the tensile strength, [0.1, 0.9] for the yield strength, and [0.15, 0.90] for the elongation. When the above parameters were used, the relativity for predicition of strength was bigger than 0.95. By using improved ANN analysis, more reasonable process parameters and composition could be obtained in some magnesium alloys without addition of strontoum. magnesium alloys, neural network model, composition, mechanical properties BackgroundBecause of their low density, magnesium alloys have attracted an increasing interest in transportation, aeronautical and aerospace industries for the past decade, but their mechanical properties and processing performances still could not meet the needs of some important parts in vehicles and other important application fields [1][2][3][4] . Many new types of magnesium alloys are being developed in the world in order to further improve the mechanical properties and processing performances of the magnesium alloys [5][6][7] . However, conventional methods for developing new alloys need a lot of experimental work and take long time.Prediction of mechanical properties of engineering alloys is important for scientists and engineers, which can save not only cost but also time. However, due to the complex interconnections among chemical compositions and materials properties, conventional mathematical models are sometimes very complex to handle by the numerical techniques. In recent years, neural network models have been widely used in different metallurgical operations. Efforts have been made to use this technique for predicting the hot extrusion processes of AZ31 and AZ61 magnesium alloys and for investigating the influence of Y and Zn additions on the mechanical properties of Mg-Zn-Zr-Y alloys [8][9][10][11][12] . However, significant prediction errors in some cases call for further improvement of the neural network model for more accurate predictions. In the present work, an improved neural network model was developed for prediction of mechanical properties in the design and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm.

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Section: Introductionmentioning

confidence: 99%

An improved neural network model for prediction of mechanical properties of magnesium alloys

Tang

Liu

Pan

et al. 2009

Sci. China Ser. E-Technol. Sci.

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“…Therefore, many ways are being investigated in order to further improve the mechanical properties and performance of Mg-Al-based alloys (Ref [1][2][3][4]. It is well known that the grain refinement is an important method of elevating the properties and improving the formability for magnesium alloys.…”

Section: Introductionmentioning

confidence: 99%

Processing Effects on Grain Refinement of AZ31 Magnesium Alloy Treated with a Commercial Al-10Sr Master Alloy

Yang

Pan

2009

J. of Materi Eng and Perform

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The effects of Sr amount and melt holding time on the grain refinement of AZ31 magnesium alloy treated with a commercial Al-10Sr master alloy are investigated. The effects of solutionizing, rolling, and remelting of commercial Al-10Sr master alloy on the grain refinement of AZ31 magnesium alloy are also investigated. An increase in Sr amount from 0.01 to 0.1 wt.% or melt holding time from 20 to 80 min causes the grain size of AZ31 alloy treated with the commercial Al-10Sr master alloy to gradually decrease. In addition, the solutionizing, rolling, or remelting of commercial Al-10Sr master alloy can improve the refinement efficiency of the master alloy to AZ31 alloy, and the improvement resulting from the remelting is best obvious, followed by rolling and solutionizing, respectively.

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“…Recently, magnesium alloys have been widely concerned in applications for the automobile and aerospace industries as a structure material due to their low specific weight, high specific strength, excellent dimensional stability and energy consumption [1][2][3]. The Mg-Al series alloys are the most common commercial magnesium alloys, which possess the best strength to weight ratio.…”

Section: Introductionmentioning

confidence: 99%

Microstructural Evolution of Mg-4Al-2.5Ca Alloy during Solidification

Jiang

Zhang

et al. 2015

MSF

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In the present work, the microstructure evolution of Mg-4Al-2.5Ca alloy during solidification was investigated by means of scanning electron microscopy (SEM), energy dispersive spectrometer (EDS) and differential scanning calorimetry (DSC). The results showed that the as-cast microstructure was typical dendritic structure, consisting of α-Mg and the Al2Ca with a C15 structure formed at inter-dendritic regions. But during slow solidification, the sequence of phase formation of this alloy consisted of following stages: L→ L + α-Mg → L + α-Mg + (Mg, Al)2Ca → α-Mg + (Mg, Al)2Ca + Al2Ca → α-Mg + Al2Ca. Thermal analysis showed that the (Mg, Al)2Ca and Al2Ca phase separation temperature was about 520°C and 510°C respectively, which is different from thermodynamic calculation. With the temperature decreasing, the liquid (Mg, Al)2Ca phase with thick and continuous morphology transformed into lamellar-shaped eutectic phase, and parts of (Mg, Al)2Ca phase translated into Al2Ca phase with bone-shaped and needle-like.

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Research and Development of Wrought Magnesium Alloys in China

Cited by 4 publications

References 0 publications

An improved neural network model for prediction of mechanical properties of magnesium alloys

An improved neural network model for prediction of mechanical properties of magnesium alloys

Processing Effects on Grain Refinement of AZ31 Magnesium Alloy Treated with a Commercial Al-10Sr Master Alloy

Microstructural Evolution of Mg-4Al-2.5Ca Alloy during Solidification

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