Application of neural network to arc sensor
Application of neural network to arc sensor
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DOI：
10.1179/136217199101537950
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发表时间：
1999-12
影响因子：
3.3
通讯作者：
K. Eguchi;S. Yamane;H. Sugi;T. Kubota;K. Oshima
中科院分区：
文献类型：
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作者：
K. Eguchi;S. Yamane;H. Sugi;T. Kubota;K. Oshima
在单面多层焊件的第一层中，熔池的全熔透控制对于获得良好的焊缝质量是重要的。为此，提出了一种新的方法，曲折焊接方法，以实现稳定的背面焊道。焊枪不仅沿着坡口穿行，而且前后移动。此外，神经网络（NN）电弧传感器，提出了估计的电线延伸和电弧长度，通过使用测量的电压和电流。此外，从神经网络的输出，估计间隙和焊炬的振荡中心与槽中心的误差（偏差）的差距。训练数据的构建实验结果，并使用测试数据的NN电弧传感器的性能进行检查。利用神经网络电弧传感器的输出进行焊缝跟踪，取得了良好的跟踪效果。
Full penetration control of the weld pool in the first layer of a single side multilayer weldment is important to obtain a good quality weld. For this purpose, a new method, the switchback welding method, is proposed to achieve a stable back bead. A welding torch not only weaves along the groove, but also moves back and forth. Also, a neural network (NN) arc sensor is proposed that estimates the wire extension and the arc length by using measurements of both voltage and current. Moreover, from the output of the NN, the gap and the error (deviation) of the oscillation centre of the torch from the groove centre are estimated. Training data are constructed from experimental results, and performance of the NN arc sensor is examined using test data. Seam tracking is carried out via the output of the NN arc sensors: a good tracking result is obtained.