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基于BP神经网络的理论和算法,建立了BP神经网络模型;通过拉伸实验测定了不同淬火温度下TC16合金的力学性能,使用建立的BP神经网络模型,对实验数据进行了训练和仿真,研究了TC16钛合金在不同淬火温度下的变形行为。结果表明,使用该BP神经网络模型可以得到很高的计算精度,预测误差在5%,该方法适用于TC16合金的进一步研究。
Based on the theory and algorithm of BP neural network, a BP neural network model was established. The mechanical properties of TC16 alloy were measured by tensile test at different quenching temperature. The BP neural network model was established to train and simulate the experimental data. The deformation behavior of TC16 titanium alloy at different quenching temperatures. The results show that this BP neural network model can obtain high computational accuracy with a prediction error of 5%. This method is suitable for further study of TC16 alloy.