Intelligent methodology for sensing, modeling and control of pulsed GTAW : Part 2: Butt joint welding
Intelligent methodology for sensing, modeling and control of pulsed GTAW : Part 2: Butt joint welding
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DOI：
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发表时间：
2000
期刊：
影响因子：
2.2
通讯作者：
S. B. Chen;D. Zhao;L. Wu;Y. Lou
中科院分区：
文献类型：
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作者：
S. B. Chen;D. Zhao;L. Wu;Y. Lou
本文研究了对接脉冲钨极氩弧焊过程质量控制的智能技术，是文献[1]的发展。由于对接焊和板上焊存在一些重要差异，文献1中的建模和控制方案并不完全适合对接焊。本文研究了两者的区别，并采用熔池形状和尺寸参数来描述熔池的几何形状。提出了一种新的尺寸和形状帕拉的实时算法。建立了尺寸形状神经网络模型（SSNNM），对最大背宽进行了预测.验证了模型的准确性。设计了一种自学习模糊神经网络控制器（FNNC），对最大后宽进行控制，并在线修改模糊规则。基于模糊神经网络控制器，结合专家系统，开发了双输入双输出（DIDO）智能控制器，用于控制最大背宽和熔池形状。实验结果表明，DIDO智能控制器能形成较好的对接焊缝。
This paper addresses intelligent techniques for the quality control of the pulsed gas tungsten arc welding process for butt joints, and it is a development to Ref. 1. Because there exist some important differences in butt joint welding and bead-on-plate welding, the modeling and control scheme in Ref. 1 does not completely fit for butt joint welding. In this paper, the differences be tween the two were investigated, The shape and size parameters for the weld pool were used to describe the weld pool geometry. A new real-time algorithm was developed for the size and shape para meters. A size and shape neural network model (SSNNM) was established to predict the maximum backside width. The model accuracy was verified. Furthermore, a self-learning fuzzy neural net work controller (FNNC) was designed for control of the maximum backside width and the fuzzy rules were modified online. Based on the FNNC, and combined with an expert system, a double-input and double-output (DIDO) intelligent controller was developed for controlling the maximum backside width and the shape of the weld pool. Experiment results showed the DIDO intelligent controller could form a better butt joint weld.