摘要
为了进行新型建筑耐候钢铸造性能优化,本文以合金元素、合金元素添加量、熔炼温度、静置时间和浇注温度5个神经单元为输入层参数、以腐蚀电位为输出层参数,以tansig函数为隐含层传递函数、purelin函数为输出层传递函数,构建了5×30×6×1四层拓扑结构的新型建筑耐候钢铸态性能神经网络优化模型,并进行了模型的学习训练与预测验证。结果表明:模型具有较佳的预测能力和较高的预测精度,模型相对预测误差介于3.57%与5.02%之间,平均相对预测误差4.24%。模型优化出的新型建筑耐候钢是在09MnCuPTi钢中添加0.3%Ce,熔炼温度是1630℃、静置时间是30 min、浇注温度是1600℃。与09MnCuPTi建筑耐候钢相比,优化的新型建筑耐候钢的腐蚀电位从-676 mV正移到-543 mV,正移133 mV,耐腐蚀性能得到明显提高。
In order to optimize the casting performance of a new building weathering steel, taking alloy elements, alloy element addition, melting temperature, standing time and pouring temperature as input layer parameters, and corrosion potential as output,tansig function as hidden layer transfer function and purelin function as output layer transfer function, a neural network optimization model for the casting performance of the new building weathering steel with a four-layer topology of 5×30×6×1 was constructed and learning training and prediction verification of the model were carried out. The results show that the model has better prediction ability and higher prediction accuracy, the relative prediction error of the model is between 3.57% and 5.02%, and the average relative prediction error is 4.24%. The new building weathering steel optimized by the model is 09MnCuPTi steel with 0.3% Ce, melting temperature of 1630℃, standing time of 30 min and pouring temperature of 1600℃. Compared with that of 09MnCuPTi building weathering steel, the corrosion potential of the optimized new building weathering steel moves from -676 mV to -543 mV, and shifts positively by 133 mV, and the corrosion resistance is significantly improved.
作者
汤东
王兵
刘松林
TANG Dong;WANG Bing;LIU Songlin(Chongqing Chemical Industry Vocational College,Chongqing 401220,China;Chongqing Institute of Building Science Co.,Ltd.,Chongqing 400016,China;School of Materials Science and Engineering,Chongqing University,Chongqing 400045,China)
出处
《热加工工艺》
北大核心
2023年第1期78-81,共4页
Hot Working Technology
基金
重庆市长寿区科技局项目(CS2020041)。
