Weld penetration identification for VPPAW based on keyhole features and extreme learning machine
Weld penetration identification for VPPAW based on keyhole features and extreme learning machine
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DOI：
10.1109/arso.2016.7736263
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Di Wu;Huabin Chen;Yiming Huang;Yinshui He;Shanben Chen
中科院分区：
文献类型：
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作者：
Di Wu;Huabin Chen;Yiming Huang;Yinshui He;Shanben Chen
变极性等离子弧焊接作为一种先进的制造技术，由于其能量密度高，已成功地应用于工业生产。在VPPAW工艺中，对焊缝熔深控制的需求仍然是一个长期的关注点。本文建立了一个简单灵活的视觉系统，采集了一系列小孔图像，并基于零件树模型提取了小孔的几何形貌，包括小孔的宽度和面积。然后利用所获得的小孔特征，采用一种新的极端学习机模型对焊缝熔深进行预测。研究表明，ELM模型能够较好地预测变极性等离子弧焊熔透状态，实现焊接质量的真实的实时监测。
Variable polarity plasma arc welding, as an advanced manufacturing technology, has been successfully used in industrial production due to high energy density. The need for the control of the weld penetration remains of a long term interest in VPPAW process. In this study, a simple-flexible vision system was established to acquire a series of keyhole images, and the geometrical appearance of keyhole including the keyhole width and area are extracted based on part-based tree model. Then the acquired keyhole features are used to predict the weld penetration by using a novel extreme learning machine model. The research shows that ELM model can predict the penetration state of variable polarity plasma arc welding credibly and achieve real time monitoring for welding quality.