Research and Development of Processing Technologies for Wrought Magnesium Alloys

Pan, Fu Sheng; Yang, Mingshun; Long, Yan; Cole, Gerald S.

doi:10.4028/www.scientific.net/msf.546-549.37

Cited by 16 publications

(7 citation statements)

References 23 publications

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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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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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“…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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“…Since the beginning of the 21st century, one can observe significant increase of the interest in magnesium alloys as applied in automotive, aerospace industry, as well as in space technology. The development of deformable magnesium alloys and plastic forming methods has been strongly limited so far [1][2][3]. Plastically worked magnesium alloys are very rarely used due to technological difficulties in plastic working and to high production costs.…”

Section: Introductionmentioning

confidence: 99%

Model of Microstructure Development in Hot Deformed Magnesium Alloy AZ31 Type

Kuc

Hadasik

2013

SSP

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The paper presents a model of microstructure changes elaborated for magnesium alloy type AZ31. In previous papers, the function of flow stress was defined on the basis of uniaxial hot compression tests. On the basis of marked relaxation curves and quantitative tests of structure the softening indicators were defined together with elaboration of equations which describe the changes in the grain size. Marked coefficients of equations were introduced in the code of simulation program. Calculations were conducted for given temperature values from 450 ÷ 250°C and strain rate from 0.01 to 10 s-1, which correspond with rolling temperature range of this alloy. Prepared model will allow the proper choice of parameters in hot rolling process of this alloy to achieve the assumed microstructure.

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Research and Development of Processing Technologies for Wrought Magnesium Alloys

Cited by 16 publications

References 23 publications

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

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

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

Model of Microstructure Development in Hot Deformed Magnesium Alloy AZ31 Type

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