TC17钛合金片层组织动态球化的神经网络预测模型
发布人:上海艾荔艾合金股份有限公司www.shailiai.cn
更新时间:2016-01-15
在Gleeble−1500热模拟试验机上通过热压缩试验研究具有初始片层组织的TC17钛合金在变形温度为780~860 ℃、应变速率为0.001~10 s−1、变形量为15%~75%范围内的组织演变,定量分析热变形参数对片层组织动态球化过程的影响。采用结合贝叶斯归一化算法的BP人工神经网络,建立TC17钛合金片层组织动态球化演变的预测模型,误差分析表明模型精度较好。
TC17钛合金片层组织动态球化的神经网络预测模型ANN model for prediction of dynamic globularization in TC17 titanium alloy在Gleeble−1500热模拟试验机上通过热压缩试验研究具有初始片层组织的TC17钛合金在变形温度为780~860 ℃、应变速率为0.001~10 s−1、变形量为15%~75%范围内的组织演变,定量分析热变形参数对片层组织动态球化过程的影响。采用结合贝叶斯归一化算法的BP人工神经网络,建立TC17钛合金片层组织动态球化演变的预测模型,误差分析表明模型精度较好。
The isothermal hot compression on Gleeble−1500 system in the temperature range of 780−860 ℃ and strain rate range of 0.001−10 s−1, was used to study the kinetics of microstructural evolution of TC17 titanium alloy with initial lamellar microstructure. The quantitative results show that dynamic globularization kinetics and kinetics rate are sensitive to deformation conditions. A model for predicting volume fraction of dynamic globularization was built based on the above quantitative results and BP artificial neural network (ANN) combined with Bayesian regularization. The well coincidence of the predicted results with measured ones shows the feasibility of the model.
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