Prediction And Verification Of The Influence Of Texture Characteristics On The Mechanical Properties Of AZ31 Magnesium Alloy Sheet

Magnesium alloys have become promising lightweight structural materials due to their low density,high specific strength,excellent electromagnetic shielding and damping performance.However,the hexagonal close-packed crystal structure results in fewer independent slip systems that can be activated at room temperature,and strong basal texture would be readily formed during forming,which has an important impact on the mechanical properties of magnesium alloys.The mapping relationship between texture characteristics and mechanical properties of magnesium alloys is relatively complex.At present,the magnitude of pole density is mainly used to value the strength of basal texture,in this way a simple correspondence between basal texture and mechanical properties can be obtained.However,this method ignores the influence of the degree of texture dispersion and polar axis deflection,resulting in that the texture characteristics cannot be well correlated with the mechanical properties.Therefore,this study takes commercial AZ31 magnesium alloy sheet as the research object,introduces machine learning method to conduct in-depth analysis of AZ31 characteristic parameters such as maximum polar density,texture distribution,polar axis deflection,and establishes the inherent relationship between texture characteristic parameters and mechanical properties to obtain a more accurate quantitative correspondence,thus providing theoretical support and process guidance for the development of high-performance magnesium alloy materials based on texture modification.In this study,with combination of the machine learning method and experimental research,a series of texture characteristic parameters of AZ31 sheets were quantitatively extracted by self-programming,such as maximum pole density and the distribution of texture（the degree of texture dispersion,polar axis deflection）.The artificial neural network model between the texture characteristics and room-temperature tensile properties of the AZ31 magnesium alloy sheet was established.The reliability of the model was verified through the characterization and analysis of the mechanical properties with different texture characteristics.Furthermore,the constructed model was used to explore the influence of different texture characteristic parameters on the mechanical properties of AZ31 magnesium alloy sheets.The main contents and results are as follows:（1）The artificial neural network model was established based on the characteristic parameters of the texture of AZ31 magnesium alloy sheet that were quantitatively extracted.Based on the basal texture pole figures of the AZ31 sheets in extensive literatures,the texture characteristic parameters extracted from pole figure included the maximum pole density(I_max),the degree of texture dispersion(the ratio of the radius of the envelope circle to the maximum pole density,r/I_max),ED polar axis deflection(P_ED)and TD polar axis deflection(P_TD).The study based on Pearson correlation coefficient and random forest method showed that the degree of texture dispersion and polar axis deflection had important effects on the ultimate tensile strength and yield strength of AZ31 magnesium alloy sheets,while the elongation was mainly related to the maximum extreme density and degree of texture dispersion.（2）The artificial neural network model of the texture characteristics and mechanical properties of AZ31 magnesium alloy sheets was established,which included hidden layer,input layer and output layer,and the number of neurons in the hidden layer was optimized to be 10.Based on the 121 groups of available data,the Levenberg-Marquardt algorithm was used for training optimization,and the fitted regression correlation coefficients R of the training set,validation set,and test set of the obtained artificial neural network model were 0.98,0.98,and 0.97,respectively,showing high reliability.（3）Six kinds of AZ31 magnesium alloy sheets with different texture characteristics were fabricated,and then the evolution of their mechanical properties with the texture characteristics was investigated,and the reliability of the established artificial neural network model was further verified.By comparing the experimental and the predicted results,it was found that the constructed artificial neural network model could accurately predict the mechanical properties of AZ31 sheets under the six different texture characteristics.The average prediction error of yield strength and elongation was about20%,and the average prediction error for ultimate tensile strength was about 25%.It was worth pointing out that the model could accurately predict the mechanical properties of extruded sheets with partial TD deflection texture characteristics,and the prediction errors of yield strength,ultimate tensile strength,and elongation were all below 5%,which presented a relatively high accuracy.（4）Based on the constructed artificial neural network model,the effects of different texture characteristic parameters on the tensile mechanical properties of AZ31magnesium alloy sheets at room temperature were systematically studied.With the increase of the I_max,the activating stress of basal slip increased,thus the ultimate tensile strength and yield strength increased,while the elongation decreased;with the increase of degree of dispersion r/I_max,the ultimate tensile strength and yield strength decreased,while the elongation increased;with the increase of P_ED,the yield strength first decreased and then increased,while the elongation first increased and then decreased,showing a nonlinear change that was mainly related to the activation and transformation of different deformation modes;with the increase of P_TD,the yield strength increased,the ultimate tensile strength first increased and then decreased,and the elongation decreased.