基于PSO-BP网络模型的模压时效炉金属温度软测量方法研究

Research on Soft Sensor for Metal Temperature of Molded Aging Oven Based on PSO-BP Network Model

铝合金产品在密闭的时效炉中进行热处理时,实时获取时效金属产品的温度成为工业生产过程中的一大难点。根据可直接检测的时效炉工作室壁温度,建立了基于BP神经网络的模压制品时效炉金属温度预测模型,该模型能够满足一般民用铝合金产品的时效温度预测要求;再通过PSO算法代替梯度向量法对BP网络模型的参数进行优化训练,仿真实验结果表明,PSO-BP网络温度预测模型的精度和泛化能力显著提高,能够满足特种铝合金产品时效处理温度预测的需要。

In the process of heat treatment of aluminum alloy products,it is a difficult problem to ob-tain the temperature of aging products in real time.According to the aging furnace wall temperaturecapab-le of being detected directly,prediction model of metal temperature of aging furnace for molding products based on BP neural network is established. This model can meet the requirements of the aging temperature prediction for the general civil aluminum alloy products.Then the PSO algorithm is used instead of thegr-adient vector method to optimize the parameters of the BP network model.The simulation results showth-at the precision and generalization ability of the PSO-BP network temperature prediction model are impro-ved significantly,and it can meet the needs of the aging temperature prediction for the special aluminuma-lloy products.