基于铝合金粉末成形本构模型的BP神经网络参数预测

Parameters Prediction of BP Neural Network Based on Constitutive Model of Aluminum Alloy Powder Forming

建立准确的本构模型是进一步开展金属粉末成形研究的基础。基于修正的Drucker-Prager Cap模型和遗传算法优化的BP神经网络,对铝合金粉末压制成形过程进行了数值模拟,实现了本构模型参数的预测。对于铝合金粉末成形研究,首先确定了修正的Drucker-Prager Cap模型参数取值范围,基于ABAQUS及子程序USDFLD的有限元分析平台实现了压制过程的数值模拟。然后,以数值模拟的压制力数据为输入,修正的Drucker-Prager Cap模型参数为输出,建立了遗传算法优化的BP神经网络模型。对模压试验的本构模型参数进行了反演预测。结果表明,参数反演后数值模拟结果和模压试验数据之间的平均绝对百分比误差(MAPE)仅为5.10%,该BP神经网络模型能实现对本构模型参数的快速、有效和准确地预测。

The establishment of an accurate constitutive model was the basis for further research on metal powder forming.Based on the modified Drucker-Prager Cap model and a BP neural network optimized by genetic algorithm,the numerical simulation of aluminum alloy powder forming process was carried out,and the constitutive model parameters were predicted.For the research of aluminum alloy powder forming,firstly,the range of the Drucker-Prager Cap model parameters were determined,and the numerical simulation of the forming process was realized based on the finite element analysis platform of ABAQUS and its subroutine USDFLD.Then,a BP neural network model optimized by genetic algorithm was established by taking the suppression force data of numerical simulation as input and the Drucker-Prager Cap model parameters as output.Finally,the constitutive model parameters of the powder die compaction tests were predicted by inversion.The results show that the average absolute percentage error(MAPE)between the numerical simulation results and the die compaction tests data after parameters inversion is only 5.10%.The BP neural network model can predict the parameters of the constitutive model quickly,effectively and accurately.