The study investigates the influence of different fraction of Mg 2 B 2 O5 whiskers (5, 10, 15 and 20vol.% ) on the microstructure of the hot extruded composite as well as on the mechanical properties in the same condition. The results indicate that the process is available for producing the composite, image analysis shows the whisker tends to cluster together with increasing content of reinforcement. When the content of the reinforcement is 10%, the composites exhibit the best mechanical properties, meanwhile, it demonstrate cluster is unfavorable to the improvement of properties of materials. The ductile failure of 6061Al matrix, the reinforcement fracture and the whisker-matrix interface debonding acted as the main mechanism of fracture nucleation.
Adding an anti-corrosion coating to the surface of magnesium (Mg) alloy is an effective means to improve its corrosion resistance, but it is particularly important to enhance the bonding force between the anti-corrosion coating and the Mg matrix. Epoxy resin coating is considered the most cost-effective approach from an industrial standpoint, while the hindering the wider utilization of Mg alloys with epoxy coating in the realm of engineering materials are due to their insufficient adhesion between Mg matrix and the epoxy coating. Here, we demonstrated a simple method for forming an epoxy resin/mineralized film composite coating. The mineralized products coatings were applied to the surface of Mg-3Nd alloy, via a direct reaction between Mg and 15 wt% Na2CO3 solution. Due to the inherent lamellar structure of alkaline magnesium carbonate mineralization film, the mechanical interlocking between the epoxy resin and the Mg substrate are remarkably strengthened. Therefore, this simple surface treatment method is promising to improve the adhesion between the epoxy resin and the matrix. Moreover, the effects of mineralized products coating on the corrosion resistance of Mg with epoxy coating were researched. It was observed that the corrosion resistance of Mg alloy with composite coatings are significantly enhanced. Such an investigation aims to provide a new method for developing a cost-effective and robust epoxy resin/mineralized film composite coating protection structure of Mg alloys in practical applications.
The flow stress behavior of Mg-3Sn-1Mn alloy during thermal compression deformation was systematically studied. The thermal compression simulation experiment was carried out at different deformation temperatures and different strain rates in the range of 523-673K and 0.001-1s-1, respectively. It is found that at low temperature and high strain rate, a large number of twins generated at the early stage of thermal deformation, causing an increase in the corresponding flow stress level, which makes the traditional constitutive relation model insensitive to predicting the thermal deformation behavior of Mg alloys with twinning effect. To better evaluate the rheological behavior of Mg alloys, an artificial neural network model based on feedforward and backpropagation algorithm was developed to predict the thermal deformation behavior of Mg-3Sn-1Mn alloy affected by twinning phenomena. The inputs of the model were deformation temperature, strain rate, and strain, and the output was flow stress. The comparative evaluation of the obtained results using statistical standard R2 and relative error [[EQUATION]] . The correlation coefficient of constitutive model prediction was 0.964 and 0.869 respectively at low stress and high stress, and the correlation coefficient of neural network prediction was 0.992. The result shows that the trained ANN is more accurate than the traditional constitutive relation model in predicting the thermal deformation behavior with twinning effect.
The flow stress behavior of Mg–3Sn–1Mn alloy during thermal compression deformation was systematically studied. The thermal compression simulation experiment was carried out at different deformation temperatures and different strain rates in the range of 523–673 K and 0.001-1s−1, respectively. It is found that at low temperatures and high strain rates, a large number of twins were generated at the initial stage of thermal deformation, causing an increase in the corresponding flow stress, which makes the traditional constitutive relation model insensitive to predicting the thermal deformation behavior of Magnesium (Mg) alloys with twinning effect. To better evaluate the rheological behavior of Mg alloys, an artificial neural network (ANN) model based on a feedforward and back-propagation algorithm was developed to predict the thermal deformation behavior of Mg–3Sn–1Mn alloy affected by twinning phenomena. The inputs of the model were deformation temperature, strain rate and strain, the output was the flow stress. The comparative evaluation of the obtained results using statistical standard R2 and relative error R¯. The correlation coefficients predicted by the constitutive model were 0.964 and 0.869 at low and high stress levels, respectively. And the correlation coefficient of the neural network predictive model was 0.992. The result shows that the trained ANN is more accurate than the traditional constitutive relation model in predicting the thermal deformation behavior with the twinning effect.
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