| 基于动态时空规整的系泊链闪光焊接在线质量评估 |
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| 引用本文： | 李俏,苏世杰,陈赟,王海荣,唐文献.基于动态时空规整的系泊链闪光焊接在线质量评估[J].焊接学报,2019,40(3):52-58. |
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| 作者姓名： | 李俏 苏世杰 陈赟 王海荣 唐文献 |
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| 作者单位： | 1. 江苏科技大学 机械工程学院，镇江 212003; |
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| 基金项目： | 国家自然科学基金青年科学基金(51705214)；江苏省自然科学基金青年基金(BK20170582)；浙江省质监系统质量技术基础建设项目(NQI)；船舶与海工装备安全性能评价关键技术研究(20180129) |
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| 摘 要： | 闪光焊接系泊链已经成为现阶段造船业高质量系泊链市场的主流. 针对闪光焊接在线质量评估问题，首先采集实际焊接过程中各种传感器信号；其次，提出了一种新颖的时空规整算法来量化闪光焊接过程中电流和电极位置信号的时空不相似度；然后，将得到的不相似距离矩阵嵌入到低维空间的特征向量中，并保留信号之间的原始不相似距离；最后，提出了KNN分类算法并引入K折交叉验证，对嵌入的特征向量(低维空间中的坐标)进行分类. 结果表明，文中提出的方法不仅能够用采集的传感器信号进行实时的焊接质量评估，而且有效地揭示了闪光焊接过程的质量差异.
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| 关 键 词： | 闪光焊接 动态时空规整 特征嵌入 质量评估 统计过程控制 |
| 收稿时间： | 2018/9/25 0:00:00 |
On-line quality assessment of mooring chain flash welding based on dynamic spatiotemporal warping |
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| LI Qiao,SU Shijie,CHEN Yun,WANG Hairong,TANG Wenxian.On-line quality assessment of mooring chain flash welding based on dynamic spatiotemporal warping[J].Transactions of The China Welding Institution,2019,40(3):52-58. |
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| Authors: | LI Qiao SU Shijie CHEN Yun WANG Hairong TANG Wenxian |
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| Affiliation: | 1. College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China;2. Jiangsu Key Laboratory of Advanced Manufacturing of Ship and Ocean Machinery Equipment, Zhenjiang 212003, China;3. Zhoushan Institute of Calibration and Testing for Qualitative and Technical Supervision, Zhoushan 316000, China |
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| Abstract: | The flash welding mooring chain has become the mainstream of the high-quality mooring chain market in the shipbuilding industry. In this present work, we proposed a new methodology for the online quality assessment of flash welding. First, the various sensor signals in the real-world welding process are collected in this work. Second, a novel spatiotemporal warping algorithm is proposed to quantify the spatiotemporal dissimilarity of current and electrode position signals during flash welding processes. Third, the dissimilar distance matrix is embedded into the feature vector of the low dimensional space while the original dissimilar distance between the signals were preserved. Finally, the KNN classification algorithm and K-fold cross-validation are proposed to classify the embedded feature vectors (coordinates in low-dimensional space). Experimental results show that the proposed method in this paper can not only use the collected sensor signals for real-time welding quality assessment, but also effectively reveal the difference between the flash welding processes. |
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| Keywords: | flash welding|dynamic time warping|feature embedding|quality assessment|statistical process |
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