锻压技术

基于PSO-BP模型的5083铝合金力学性能预测

英文标题：Prediction of mechanical properties for 5083 aluminum alloy based on PSO-BP model

作者：崔鑫张建平张能辉

分类号：TG146.2; TP183

出版年,卷(期):页码：2019,44(6):183-187

摘要：

为准确预测5083铝合金热力学参数与其流变应力之间的关系，基于PSO优化BP神经网络方法提出了PSO-BP铝合金性能预测模型，并对BP模型和PSO-BP模型预测结果进行了对比与分析。结果表明：PSO-BP模型预测值与试验值的吻合度高于BP模型，更能准确地反映铝合金在不同工艺条件下流变应力的变化规律；PSO-BP预测模型具有更快的收敛速度，达到BP预测模型的10倍以上；与传统BP模型相比，PSO-BP模型预测值的平均相对误差不到BP模型的50%，在低温和高温时更明显，且4种应变速率下其预测值与试验值的线性回归决定系数更接近于1，证明了PSO-BP模型对5083铝合金力学性能具有更高的预测精度。

In order to accurately predict the relationship between thermodynamic parameters and rheological stress of 5083 aluminium alloy, based on the method of BP neural network optimized by PSO, the performance prediction model PSO-BP of aluminum alloy was proposed, and the prediction results of BP model and PSO-BP model were compared and analyzed. The results indicate that the coincidence degree between the predicted value of PSO-BP model and the experimental value is higher than that of BP model, and the PSO-BP model can more accurately reflect the rheological stress change of aluminium alloy under different process conditions. Furthermore, the PSO-BP prediction model has the faster convergence speed, which is more than 10 times that of the BP prediction model. Compared with the traditional BP model, the average relative error of the predicted value of PSO-BP model is less than 50% that of BP model, it is more obvious at low temperature and high temperature, and its linear regression determinant coefficients between predicted value and experimental value under four strain rates are closer to 1. Thus, it is proved that the PSO-BP model has higher prediction accuracy for mechanical properties of 5083 aluminum alloy.

基金项目：

国家自然科学基金资助项目（11572187）；上海市科学技术委员会项目（18DZ1202105, 18DZ1202302）

崔鑫（1993-），男，硕士研究生 E-mail：cxin777@163.com 通讯作者：张建平（1972-），男，博士后，教授 E-mail：jpzhanglzu@163.com

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