基于RBF神经网络的FeO-MnO渣系ZGMn13堆焊焊条的设计

Design of FeO-MnO Slag System Hardfacing Electrode About ZGMn13 Based on RBF Neural Networks

为设计一种ZGMn13堆焊焊条,提出了一种基于RBF神经网络的FeO-MnO渣系焊条配方优化设计方法。利用试验采集的数据对网络进行训练,以加工硬化后的硬度为优化目标,得到最优的焊条配方。试验结果表明:优化后熔敷金属的动载加工硬化性能和静载加工硬化性能良好。

To design a kind of hardfacing electrode about ZGMnl3, a method of optimization design about electrode formulation with FeO-MnO slag system based on RBF neural networks was proposed. The networks was trained with the data collected l^om experiments. Optimal formulation of electrode was achieved when the hardness is considered after work hardening as the optimal target. The results of experiment show that the performances of dynamic load hardening and static load hardening are good.