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首页> 外文期刊>Journal of Materials Engineering and Performance >Prediction of Tensile Property of Hydrogenated Ti600 Titanium Alloy Using Artificial Neural Network
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Prediction of Tensile Property of Hydrogenated Ti600 Titanium Alloy Using Artificial Neural Network

机译：人工神经网络预测氢化Ti600钛合金的拉伸性能。

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摘要

An artificial neural network (ANN) model has been developed to analyze and predict the correlation between tensile property and hydrogenation temperature and hydrogen content of hydrogenated Ti600 titanium alloy. The input parameters of the neural network model are hydrogenation temperature and hydrogen content. The output is ultimate tensile strength. The accuracy of ANN model was tested by the testing data samples. The prediction capability of ANN model was compared with the multiple linear regression approach and response surface method. The combined influence of inputs on the tensile property is also simulated using ANN model. It is found that excellent performance of the ANN model was achieved, and the results showed good agreement with experimental data. Moreover, the developed ANN model can be used as a tool to control the tensile property of titanium alloys.
机译：建立了人工神经网络（ANN）模型，以分析和预测氢化Ti600钛合金的拉伸性能和氢化温度与氢含量之间的关系。神经网络模型的输入参数是氢化温度和氢含量。输出是极限抗拉强度。通过测试数据样本测试了人工神经网络模型的准确性。将神经网络模型的预测能力与多元线性回归方法和响应面方法进行了比较。还使用ANN模型模拟了输入对拉伸性能的综合影响。结果表明，人工神经网络模型具有良好的性能，与实验数据吻合良好。此外，开发的人工神经网络模型可以用作控制钛合金拉伸性能的工具。

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