基于神经网络的焊接过程建模及控制研究

Modeling and Controlling of Welding Process Based on Neural Networks

采用人工神经网络方法对焊接过程进行建模及控制．并以ＧＴＡＷ作为对象加以实施。用误差反传网络（ＢＰ网）建立了静态过程ＧＴＡＷ熔透情况下表征其质量的正面熔宽、背面熔宽与施焊参数之间的关系，即静态模型，并从质和量两个方面验证了该模型的可行性。提出了采用两个网络对ＧＴＡＷ动态过程建模及控制的方法，并通过实验验证了所提方案的正确性。文中所提的方法从根本上不同于以往的以固定结构数学模型为基础的建模及控制的方法，无需对过程作任何假设，所建模型与实际过程符合较好。实验证明，该方法具有容错性好、通用性强和抗干扰能力强等优点，为焊接过程的建模及智能化控制找出了一条新途径。

elding process is modeled and controlled by artificial neural networks. Taking GTAW as the object, it is realized in the process.The relationship between face bead width,back bead width, which is the expression of the quality,and welding parameters is established. It is static model. The feasibility is tested from both the quality and quantity aspects.The modeling and controlling method for the GTAW dynamic process is put forward by two networks. Experiments show the validity of the method. This method is totally different from the traditional one which is based on fixed structure mathematical model in which it does not need make any assumption.The established model fits well with the reality.Experiments show that the method has the advantage of good fault tolerance,generality and disturbance resistance etc.It finds a new way for the modeling and intelligent controlling of the welding process.