Neural network and fuzzy control of weld pool with welding robot

Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting Pub Date : 1993-10-02 DOI:10.1109/IAS.1993.299169

S. Yamane, Y. Kaneko, N. Kitahara, K. Ohshima, M. Yamamoto

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引用次数: 38

Abstract

The sensing and control of weld pool depth in robotic welding are discussed. A neural network-based method for measuring the depth is proposed, since the depth cannot be directly measured in real time. The weld pool depth is estimated by using the information obtained from the welding side. The surface shape of the weld pool and the width of the groove gap can be measured during the welding. The weld pool depth can also be measured after the welding. Training data were constructed from these numerical data. When the width of the groove gap changes, the weld pool depth changes too. The feedforward control system for the variation of the groove gap width just under the electrode can be constructed by observing the groove gap width before the electrode. The feedback control system was constructed in order to keep the output of the neural network constant. The fuzzy control system was constructed from the feedback control part and the feedforward control part. The validity of a neuro-fuzzy controller was verified by welding experiments.<>

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焊接机器人熔池的神经网络模糊控制

讨论了机器人焊接中熔池深度的感知与控制。针对无法直接实时测量深度的问题，提出了一种基于神经网络的深度测量方法。利用从焊接侧获得的信息估计熔池深度。焊接时可以测量焊池的表面形状和坡口间隙的宽度。焊接后也可测量熔池深度。训练数据由这些数值数据构建。当坡口宽度变化时，熔池深度也会发生变化。通过观察电极前的槽隙宽度，可以构建电极正下方槽隙宽度变化的前馈控制系统。为了使神经网络的输出保持恒定，构造了反馈控制系统。模糊控制系统由反馈控制部分和前馈控制部分组成。通过焊接实验验证了神经模糊控制器的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting

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