Mechanical Properties and Microstructure of Magnesium-Aluminum Based Alloys Containing Strontium

Tang, Aitao; Pan, Fusheng; Yang, Ming‐Bo; Cheng, Renju

doi:10.2320/matertrans.mov2007315

Cited by 21 publications

(10 citation statements)

References 28 publications

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“…A further improvement in mechanical properties has been A.Lakshmanan Pillai, G.R.Jinu, G.Karthikeyan Fabrication and Tribological Behaviour of Mg-TiO 2 Composites required to extend the application of magnesium alloys [3,4]. Several researchers reported that the addition of hard ceramic particles to magnesium resulted in better wear resistance and improved mechanical properties and thermal stability [5][6]. Magnesium is about by one third lighter than aluminium and by two thirds than titanium.…”

Section: Introductionmentioning

confidence: 99%

Fabrication and Tribological Behaviour of Mg-TiO2 Composites

Pillai¹,

Jinu²,

Govindan³

2018

TFAMENA

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SummaryIn the present study pure magnesium was taken as a base metal which is reinforced with various weight percentages of TiO 2 (2.5, 5, 7.5, 10 % wt). This is done through a vacuum stir casting route with argon as a shielding gas to prevent oxidation. The prepared samples were machined using the Electric Discharge Machining (EDM) to achieve accurate dimensions for the study of tribological properties. A computerized pin-on-disc machine is used to study the dry sliding wear behaviour of the Mg-based composites. Wear losses were calculated using a variety of load parameters (10N, 15N, 20N, and 25N), sliding velocities (1 m/s, 1.5 m/s, and 2.0 m/s) and the weight percentage of the reinforcement. The results proved that the wear loss increased with an increase in the load, and decreased with an increase in the sliding velocity. The wear resistance is improved with an increase in the percentage of reinforcements in the matrix. The morphology and distribution of reinforcements were analysed by means of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray (EDX) spectroscopy, respectively. The results revealed a uniform distribution of TiO 2 particles throughout the magnesium matrix in the Mg-TiO 2 composites.

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Section: Introductionmentioning

confidence: 99%

Fabrication and Tribological Behaviour of Mg-TiO2 Composites

Pillai¹,

Jinu²,

Govindan³

2018

TFAMENA

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“…Mg-Al-based alloys are the most common and economic commercial magnesium alloys to which many grain refining methods have been developed, including superheating, Elfinal, carbon inoculation and addition of micro-alloying elements [4][5][6][7][8][9][10][11][12][13][14][15][16]. Among them, carbon inoculation is known to be the most effective grain refining method for operating at a low temperature and less fading with long-time holding.…”

Section: Introductionmentioning

confidence: 99%

Grain refining efficiency of a new Al–1B–0.6C master alloy on AZ63 magnesium alloy

Han

Liu

2010

Journal of Alloys and Compounds

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“…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.…”

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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Mechanical Properties and Microstructure of Magnesium-Aluminum Based Alloys Containing Strontium

Cited by 21 publications

References 28 publications

Fabrication and Tribological Behaviour of Mg-TiO2 Composites

Fabrication and Tribological Behaviour of Mg-TiO2 Composites

Grain refining efficiency of a new Al–1B–0.6C master alloy on AZ63 magnesium alloy

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

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