Iterative Local ANFIS-Based Human Welder Intelligence Modeling and Control in Pipe GTAW Process: A Data-Driven Approach
Iterative Local ANFIS-Based Human Welder Intelligence Modeling and Control in Pipe GTAW Process: A Data-Driven Approach
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DOI：
10.1109/tmech.2014.2363050
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发表时间：
2015-06
期刊：
影响因子：
--
通讯作者：
Yukang Liu;Yuming Zhang
中科院分区：
文献类型：
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作者：
Yukang Liu;Yuming Zhang
将人工焊工（具有智能和通用性）与自动化焊接系统（具有精度和一致性）相结合，可以实现智能焊接系统。本文旨在提出一种数据驱动的方法来模拟人类焊工的智能，并利用所得到的模型来控制自动气体钨极弧焊过程。为此，设计了一种创新的人机协作虚拟焊接平台，远程操作进行训练实验。焊接电流随机变化，产生波动的焊池表面，人类焊工根据观察到的实时焊池反馈/图像，尝试调整自己的手臂运动（焊接速度），叠加一个辅助的视觉信号，指示焊工增加/减少速度。首先从实验数据中识别出将焊工对焊接速度的调整与三维熔池表面相关联的线性模型，然后提出一种全局自适应神经模糊推理系统（ANFIS）模型来提高模型的精度。为了更好地提取人类焊工的详细行为，在输入空间上执行K均值聚类，以便识别局部ANFIS模型。为了进一步提高精度，进行了迭代计算。与线性、全局和局部ANFIS模型相比，迭代局部ANFIS模型具有更好的建模性能，并能更详细地揭示人类焊工所具有的智能。为了证明该模型作为一种有效的智能控制器的有效性，进行了自动控制实验。实验结果验证了该控制器在不同焊接电流和焊接速度扰动下的鲁棒性。
Combining human welder (with intelligence and versatility) and automated welding systems (with precision and consistency) can lead to intelligent welding systems. This paper aims to present a data-driven approach to model human welder intelligence and use the resultant model to control automated gas tungsten arc welding process. To this end, an innovative machine-human cooperative virtualized welding platform is teleoperated to conduct training experiments. The welding current is randomly changed to generate fluctuating weld pool surface and the human welder tries to adjust his arm movement (welding speed) based on his observation on the real-time weld pool feedback/image superimposed with an auxiliary visual signal which instructs the welder to increase/reduce the speed. Linear model is first identified from the experimental data to correlate welder's adjustment on the welding speed to the 3-D weld pool surface and a global adaptive neuro-fuzzy inference system (ANFIS) model is then proposed to improve the model accuracy. To better distill the detailed behavior of the human welder, K -means clustering is performed on the input space such that a local ANFIS model is identified. To further improve the accuracy, an iterative procedure has been performed. Compared to the linear, global and local ANFIS model, the iterative local ANFIS model provides better modeling performance and reveals more detailed intelligence human welders possess. To demonstrate the effectiveness of the proposed model as an effective intelligent controller, automated control experiments have been conducted. Experimental results verified that the controller is robust under different welding currents and welding speed disturbance.