Basic Models For Magnesium Alloys Expert System Based On Data

Advances in manufactueing and processing technologies in recent years have stimulated renewed interest in magnesium alloys for applications in the automotive, communications and aerospace industries. However, there are some problems, such as prone to burning or oxidation while smelting, low strength, less resistance of creep at elevated temperature, poor corrosion resistance performance, and low plastic formability at room temperature, etc, which are major obstacles to usage of much bigger industry scale. In the present work, the available data on mechanical properties of magnesium alloys were collected from domestic and foreign literature, then data analysis was carried out. Meanwhile, a model was developed for the analysis and prediction of the correlation between the mechanical properties of magnesium alloys and compositon, processing and working condition by using artificial neural network (ANN) based on data. The input parameters of neural network (NN) model are alloy composition, cast or wrought processing parameters, heat treatment parameters and work (test) temperature. The output parameters of the model are three mechanical properties namely ultimate tensile strength (UTS), yield strength (YS) and elongation (ELO). Finally, the effect of alloying elements on mechanical properties of magnesium alloys was investigated and discussed. To find the optimum structure of the model, different options were investigated, including preprocessing method of data, hidden layer of NN model, algorithm and its main raining parameter. The results show that algorithm has the most remarkable effect, and the training parameters have the less sight effect. The Levenberg-Marquardt (LM) algorithm got the best performance than others algorithms. The others three factors mension above have some effects on model. The best preprocessing methods of UTS, YS and ELO model are [0.1, 0.8], [0.01, 0.99] and [0.01, 0.99], and best hidden layers are single hidden layer with 10, 11 and 12 neurons. The model was used to predict the mechanical properties of AZ61B and Mg-Zn-Zr-Y wrought alloys. The results show that the predicted mechanical properties coincided with the experimental data quite well, especially the performance of UTS and YS model much better. The relative errors between predicted ultimate tensile strength and experimental data are smaller than 6.5%, and these of YS model are not bigger than 16%. But the accuracy of the ELO model is not better than that of UTS and YS models. Especially the ELO model has great difficulty to predict the properties break of magnesium alloys, for an instance, the Mg-8.3Zn-0.57Zr-1.4Y alloy becomes brittleness after T6 heat treatment (500℃/3hours+170℃/24hours), but the model can not predict this. Moreover, the mechanical properties of Mg-Al-Zn as-cast alloys was predicted using the optimum model. Available results were obtained and were verificated by microstructure analysis. The model was also used to predict effects of alloying elements on the mechanical properties of Mg-Zn-Zr-Y alloys, Mg-Al-Zn-Mn alloys and Mg-Al-Si alloys. The results are as follows: (i) Zinc can improve the ultimate tensile strength and yield strength of Mg-Zn-Zr-Y wrought alloys at room temperature, and reduce their elongation. These predicting results very close to that in literature. The high content of zirconium is unfavourable, compare to the alloys with Zr content of 0.15%, 0.35% and 0.55%, the alloys with 0.75% Zr have rather lower tensile strenght, yield strenght and elongation. (ii) With the increase of aluminium content, the ultimate tensile strength of as-cast Mg-Al-Zn-Mn as-cast alloys at room temperature went up firstly and then went down. While Al content is 3.0%, the maximum tensile strength was obtained. After that, it decreased slowly. The yield strength has the same results, but elongation has not. And zinc has sligh effect on the mechanical properties. (iii) With silicon content increase, ultimate tensile strength of as-cast Mg-Al-Si alloys at room temperature decrease, and elongation have the same result, but yield strength have opposing result. The correlation between alloy composition, processing parameters and final properties of magnesium alloys is of importance and complexity. In the present work, modeling of them by using ANN has good performance, which indicate that the method is feasible and effective. However, some problems exist. For examples: (i) ANN has poor capability to get the best result which exceed its range; (ii) experimental data of magnesium alloys are still limit compared to the data in aluminium alloys and steels. Some other methods have been suggested to resolve to the first problem. As to the second prolem, simple model was developed to predit the mechanical properties of Mg-Al-Zn as-cast alloys. Compare to the results of unsimple model, better preforance was achieved. In addition, grain size of Mg-Al-Zn as-cast alloys was predicted by using ANN.The results indicate that with the increase of aluminium content, the grain size of as-cast Mg-Al-Zn becomes more finer that can be verificated by the microstructure analysis...