针对铝合金钨极惰性气体保护电弧焊(tungsten inert gas arc welding，TIG焊)过程中，焊接工艺参数的实时状态与焊缝熔池三维尺寸间的非线性对应关系，研究建立一种基于信息物理融合的多传感器TIG焊过程熔池状态协同感知计算方法. 首先，构建由红外温度传感器、电弧形态传感器、电弧能量传感器和焊接位置传感器组成的TIG焊过程熔池状态信息物理融合系统架构. 其次，考虑焊接过程中焊枪电弧的运动特性和测量噪声影响，设计基于温度、位置、能量传感器信息交互的熔池长宽深三维参数状态感知策略，并基于多传感器数据的异步和异构特性，提出了基于无迹卡尔曼滤波的焊接过程中熔池状态的多传感器数据协同感知算法. 针对7075超硬铝合金TIG焊过程进行熔池参数在线测量与辨识试验，结果表明，所提算法能够根据TIG焊过程多传感器数据实时计算熔池参数结果，焊缝宽度和焊缝高度计算结果误差基本上控制在10%以内，该算法响应时间基本控制在0.3 s内，能够较为准确地评估焊接过程中熔池的实时状态.
Aiming at the non-linear correspondence between the real-time state of welding process parameters and the three-dimensional size of the weld pool in the tungsten inert gas arc (TIG) welding process of aluminum alloy, a multi-sensor TIG welding process based on cyber-physical fusion is studied to establish a multi-sensor TIG welding process collaborative sensing calculation method. First, build a TIG welding process molten pool state information physical fusion system architecture consisting of infrared temperature sensors, arc shape sensors, arc energy sensors and welding position sensors. Then, considering the influence of the environment and measurement noise in the welding process, a three-dimensional parameter state sensing strategy of the length, width and depth of the molten pool based on the exchange of temperature, position, and energy sensor information is designed. Based on the asynchronous and heterogeneous characteristics of multi-sensor data, a new method based on Multi-sensor data collaborative sensing algorithm for the state of the molten pool in the welding process based on trace Kalman filtering. Finally, taking the TIG welding process of 7075 super-hard aluminum alloy as the test object, the test results show that the proposed algorithm can calculate the welding pool parameter results in real time according to the motion characteristics of the welding torch and the arc in the welding seam of the TIG welding process. The error of the calculation results of the weld width and weld height can be controlled within 10%, and the response time of the algorithm is within 0.3 s, which can accurately evaluate the real-time state of the weld pool.
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