基于神经网络模型的P-GMAW焊缝成形过程仿真
Simulation of welding shape process in P-GMAW based on neural network models
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摘要: 脉冲熔化极气体保护焊(P-GMAW)是一种高效、适应性强的焊接方法之一,在工业生产中得到了广泛的应用.文中以低碳钢P-GMAW为对象,研究其焊缝成形过程的建模与仿真方法.文中首先利用BP神经网络,建立了该过程的动态模型,然后利用该模型的稳态与动态仿真揭示了P-GMAW过程的成形规律.同时,文中提出了一种利用神经网络模型考察正面熔池特征参量与反面熔池宽度之间关系的方法,利用该方法验证了熔池特征参量的有效性与可靠性.这些建模与仿真的方法及结果为焊缝成形规律的探究和焊接过程控制器的设计提供了条件.Abstract: As one of efficient and good-adaptability welding methods,pulsed gas metal arc welding(P-GMAW) has been applied in industrial production widely.In this paper,the modeling and simulation methods in P-GMAW shape process of low carbon steel were studied.Firstly,a series of BP neural network dynamic models were established for P-GMAW shape process;then stable-state and dynamic simulations were implemented with these modes to reveal the welding form rules in P-GMAW.Meanwhile,this paper proposes a method that using the neural network model to investigate the relationship between the top side weld pool characterized parameters and the backside weld pool width.With the proposed methods,the validity and reliability of the topside weld pool characteristic parameters were verified.The methods and results of these modeling and simulation provide the conditions for exploring the welding shape rules and designing the welding process controllers.