基于神经网络的药芯焊丝GMAW发尘率预测

Prediction for fume formation rate of flux cored wire GMAW based on neural network

焊接工艺对焊接发尘率有直接的影响,建立基于相关焊接工艺参数的焊接发尘率预测模型,预测特定焊接工艺的发尘率对控制和降低焊接烟尘的排放具有重要意义。鉴于焊接发尘率影响因素复杂,存在高度非线性特征,提出了基于神经网络的熔化极气体保护焊(GMAW)焊接发尘率的预测模型。通过药芯焊丝E501T-1发尘率实测数据,分别建立了BP和Elman神经网络模型,并采用遗传算法(GA)对2种神经网络进行了优化。基于15组实测数据的验证,结果表明,采用遗传算法优化后,BP和Elman神经网络模型的预测合格率分别提升了6.7%和13.4%,遗传算法优化的BP神经网络模型(GA-BP)的均方误差为586.21,平均绝对百分比误差为3.01%,均为4个模型中最小,其预测结果更为准确可靠。基于GA-BP模型所预测数据,对不同焊接电流和电弧电压的发尘率进行预测,在一定的焊接速度和保护气流量条件下,焊接电流约为170 A,电弧电压约为26 V时,焊接发尘率最小。

Welding process has a direct effect on welding fume formation rate.Establishing a prediction model of fume formation rate related to welding parameters and predicting fume formation rate of a particular welding process is of great significance to control and reduce emission of welding fumes.Due to complex factors affecting fume formation rate and their highly nonlinear characteristics,a predication model of fume formation rate of gas metal arc welding(GMAW)based on neural network was put forward.Through experimental data of fume formation rate of flux-cored wire E501T-1,back propagation(BP)neural network and Elman neural network models were established respectively,and genetic algorithm(GA)was used to optimize the two neural networks.Through verification of 15 groups of measured data,the calculated results showed that after genetic algorithm optimization,qualified prediction values of BP neural network model and Elman neural network model were increased by 6.7%and 13.4%,respectively.The mean square error of GA-BP model was about 586.21,and average absolute percentage error was about 3.01%.Both of them were the smallest among the four models,and prediction results of GA-BP were more accurate and reliable.Based on date predicted by GA-BP model,fume formation rate of different welding current and arc voltage was predicted.Under a certain welding speed and shielding gas flow rate,the minimum welding fume formation rate was obtained when welding current was about 170 A and arc voltage was about 26 V.