基于组合神经网络的钨极氩弧焊环境负荷预测  

Prediction for emission of environmental burden in GTAW based on combined neural network

作　　者：蒋伟琪 黄海鸿[1,2] 刘赟[1,2] 李磊[1,2] 刘志峰[1,2] JIANG Weiqi;HUANG Haihong;LIU Yun;LI Lei;LIU Zhifeng(Hefei University of Technology,Key Laboratoryof Green Design and Manufacturing of Mechanical Industry,Hefei,230009;Hefei University of Technology,Hefei 230009)

机构地区：[1]合肥工业大学,机械工业绿色设计与制造重点实验室,合肥230009 [2]合肥工业大学,合肥230009

出　　处：《焊接学报》2022年第10期77-85,I0008,共10页Transactions of The China Welding Institution

基　　金：国家重点研发计划资助项目(2018YFB2002103)。

摘　　要：以钨极氩弧焊为例,搭建焊接环境负荷定量预测模型.通过正交试验法确定GTAW环境负荷排放的关键影响因素为焊接电流、喷嘴高度和焊接时间;建立基于RBF-BP(Radial Basis Function-Back Propagation)组合神经网络的GTAW环境负荷排放模型,对不同焊接参数下的环境负荷进行预测.结果表明,RBF-BP组合神经网络模型的预测结果(平均误差6.63%)与实际值拟合程度高;焊接电流、喷嘴高度、焊接时间与各环境负荷产生量均呈正相关趋势.建立的预测模型可为降低焊接环境负荷排放和制定合理焊接工艺路线提供数据支持.The model is established for quantitative predicting the generation of environmental burdens in welding.The key factors for influencing the emissions in GTAW are determined using Taguchi method,including welding current,nozzle height,and welding time.Moreover,the emission model based on RBF-BP neural network was established.It can be predicting the emissions of environmental burden in GTAW when different welding parameters are adapted.The results show that the average error is 6.63% for predicting emissions of environmental burden using RBF-BP combination neural network model.The welding current,nozzle height and welding time are positively correlated with the generation of environmental burdens.That can provide support for reducing the emission of welding environmental burden and formulating reasonable welding process route.

关 键 词：钨极氩弧焊 焊接排放 正交试验 神经网络 预测模型 

分 类 号：TG457.1[金属学及工艺—焊接]