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| 神经网络预测搅拌摩擦加工TC4超塑性行为 |
| Neural Network Prediction of Superplastic Behavior of Friction Stir Processing TC4 |
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| DOI：10.3969/j.issn.1674-6457.2022.06.008 |
| 中文关键词: Ti–6Al–4V钛合金 BP人工神经网络 超塑性变形 搅拌摩擦加工 |
| 英文关键词: Ti-6Al-4V alloy back-propagation (BP) artificial neural network superplastic deformation friction stir processing (FSP) |
| 基金项目:国家自然科学基金(51805335) |
| Author Name | Affiliation | | MEN Yue | School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, China | | WANG Xin | School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, China | | ZHANG Hao-yu | School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, China | | ZHOU Ge | School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, China | | CHEN Li-jia | School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, China | | LIU Hai-jian | Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China |
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| 中文摘要: |
| 目的 研究搅拌摩擦加工工艺改性的Ti–6Al–4V双相钛合金的超塑性变形行为。方法 对360 r/min、30 mm/min工艺条件下搅拌摩擦加工处理的TC4钛合金在不同的变形条件下进行超塑性拉伸实验，在实验数据的基础上构建以变形温度、应变速率和晶粒尺寸为输入参数且以峰值应力为输出参数的3–16–1结构的BP人工神经网络模型。应用所构建的BP人工神经网络模型对不同变形条件的Ti–6Al–4V钛合金的超塑性行为进行预测。结果 BP人工神经网络预测的精准度较高，实验应力值与预测应力值吻合度较高，相关系数R=0.991 3，相对误差为1.91%~12.48%，平均相对误差为5.92%。结论 该模型预测的准确性较高，能够客观真实地描述Ti–6Al–4V合金的超塑性变形行为。 |
| 英文摘要: |
| The paper aims to study the superplastic deformation behavior of Ti-6Al-4V dual-phase titanium alloy modifies by friction stir processing. The superplastic tensile test of TC4 titanium alloy after friction stir processing at 360 r/min, 30 mm/min was carried out under different deformation conditions. Based on the experimental date, a BP artificial neural network model of 3-16-1 structure with deformation temperature, strain rate and grain size as input parameters and peak stresses as output parameters was constructed. The established BP neural network model was used to predict the superplastic behavior of Ti-6Al-4V titanium alloy under different deformation conditions. BP neural network prediction accuracy was high. The experimental stress value was in good agreement with the predicted stress value, and for the correlation coefficient R=0.991 3, the relative error range was between 1.91%-12.48%, and the average relative error was 5.92%. The model has high prediction accuracy and can objectively and truly describe the superplastic deformation behavior of Ti-6Al-4V alloy. |
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