| 基于多阈值与神经网络的旋转电弧图像飞溅分析 |
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| 引用本文： | 汤泉,石志新,毛志伟.基于多阈值与神经网络的旋转电弧图像飞溅分析[J].焊接学报,2022,43(12):41-46. |
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| 作者姓名： | 汤泉 石志新 毛志伟 |
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| 作者单位： | 南昌大学, 南昌, 330031 |
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| 基金项目： | 国家自然科学基金资助项目(51265036) |
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| 摘 要： | 为探究旋转电弧飞溅产生原因及规律，针对高速相机采集的旋转电弧平堆焊的焊接图像，提出了一种基于掩膜的多阈值与BP(back propagation)神经网络组合方法识别焊接飞溅．多阈值法获取飞溅位置及其轮廓，再通过建立5特征值的BP神经网络模型识别飞溅．结果表明，对于具有灰度分布范围大、背景复杂的旋转电弧飞溅图像，该组合方法的识别准确率可达95.76%．同时，通过飞溅与焊丝位置的相位分析，飞溅最大数量相位均值为241.4°，即焊丝末端进入熔池后约0.14周期位置，主要是由焊丝末端熔滴与熔池接触导致电流激增，电流抑制不充分造成，该研究结果为旋转电弧焊接飞溅控制提供了依据．
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| 关 键 词： | 旋转电弧 焊接飞溅 多阈值处理 BP神经网络 图像处理 |
| 收稿时间： | 2021-12-19 |
Spatter analysis of rotating arc image based on multi threshold and neural network |
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| Affiliation: | Nanchang University, Nanchang, 330031, China |
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| Abstract: | To exploring the causes and rules of rotary arc spatter, a combination method of multi threshold and BP neural network based on mask was proposed to identify welding spatter in accordance with the welding images of rotary arc flat surfacing collected by high-speed camera. The multi threshold method was used to obtain the spatter position and contour, and then the spatter was identified by establishing a BP neural network model with five characteristic values The recognition accuracy of this combined method can reach 95.76% for rotating arc spatter images with complex background. At the same time, through the phase analysis of spatter and welding wire position, the average phase of the maximum number of spatters is 241.4°, that is, about 0.14 cycle position after the end of welding wire enters the molten pool. This is mainly due to the current surge caused by the contact between the droplet at the end of welding wire and the molten pool, and the insufficient current suppression, The research results provide a basis for controlling spatter in rotating arc welding. |
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