摘要
针对川东造船厂化学品船建造中使用特种钢——2205双相不锈钢(DSS),在其焊接试验的基础上分别建立了双相不锈钢焊接应力和收缩变形的BP神经网络预测模型,并与多元线性回归方法进行比较,较好地模拟了焊接残余应力和变形与板厚、焊接电流、电弧电压、焊接速度等工艺参数之间的非线性关系。预测结果表明,BP神经网络比多元线性回归在预测精度和泛化能力上都有很大的提高。
With regard to the 2205 duplex stainless steel which is used for special shipbuilding in Chuandong Shipyard,BP neural network forecast models on welding residual stress and shrinkage deformation of 2205 duplex stainless steel（DSS） have been respectively established,on the basis of the welding experiment of 2205 duplex stainless steel.The proposed forecast model is compared with the multiple linear regression method.The comparison indicates that the proposed forecast model can better simulate the non-linear relationships of welding residual stress and deformation between the process parameters,such as thickness,welding current,arc voltage and welding speed.The forecast results show that BP neural network is more advantageous both in the prediction accuracy and generalization capability than the multiple linear regression method.
出处
《重庆交通大学学报（自然科学版）》
CAS
北大核心
2010年第5期832-836,共5页
Journal of Chongqing Jiaotong University(Natural Science)
基金
重庆市科委重大科技攻关项目(CSTC2008AB3033)
关键词
神经网络
双相不锈钢
焊接
残余应力
预测
neural network
duplex stainless steel
welding
residual stress
forecast
