GTAW神经网络-模糊控制技术的研究

研究神经网络与模糊控制融合技术，构成钨极气体保护电弧焊ＧＴＡＷ神经网络＝模糊控制系统。重点论述神经网络和模糊逻辑在熔深建模和控制以地缝跟踪方面的应用。通过视觉传感ＣＣＤ获取电弧区图像和熔池表面的应用。种描述深的神经焊缝间隙是量来精确估算熔的精度，同时结合模糊逻辑提高熔深的控制精度。针对弧焊过程非线性以焊炬伺服系统动态过程难以用角的数学模型来表达的问题，设计焊缝跟踪自调整模糊控制器，通过自适应共振理

Study on the Technique of Neural Network and Fuzzy Control for GTAW

An intelligent system including both neural network and fuzzy controller for the gas tungsten arc welding(GTAW) was presented in this paper.The discussion was mainly focused on the application of neural network and fuzzy logic in modeling and controlling the penetration depth as well as the seam tracking.A visual sensor CCD was used to obtain the image of the molten pool.A neural network model was established to estimate the penetration depth based on the welding current,pool width and seam gap.Also,the fuzzy logic technique was combined to promote the control accuracy of penetration depth.It was demonstrated that the proposed neural network could produce highly complex nonlinear multi variable model of the GTAW process and thus it offered the accurate prediction of welding penetration depth.It was difficult to obtain the accurate models of the actuators,in that the torch drivers were extremely complex systems which had highly nonlinearity.In order to resolve this problem,a self adjusting fuzzy controller to control the torch motion was proposed,which was used for seam tracking.The self organizing artificial neural network algorithm was used to detect the weld position.The control parameters were adjusted on line automatically according to the tracking errors so that the tracking errors could be decreased sharply.The experimental results showed that the proposed system yielded conspicuously controlling performance and provided an efficient approach to realize the intelligence of GTAW process.