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A new online quality monitoring method of chain resistance upset butt welding based on Isolation Forest and Local Outlier Factor
Journal of Manufacturing Processes ( IF 6.2 ) Pub Date : 2021-06-18 , DOI: 10.1016/j.jmapro.2021.06.005
Lei Zhou , Tianyi Zhang , Zhongdian Zhang , Zhenglong Lei , Shiliang Zhu

Small chain resistance butt welding often has unstable welding quality due to high production frequency and short welding time. However, there is still a lack of research on online monitoring of resistance upset butt welding. This study proposes to collect the state information in the welding process and combine with the unsupervised learning method to predict the final welding quality. The collected state information includes the dynamic resistance curve, electrode displacement curve, and upset pressure curve. And state information of 308,274 welding joints was collected. Because of the high production frequency of chains, it is difficult to obtain the quality label by the destructive detection method. The commonly used supervised learning is no longer applicable. In view of this challenge, the unsupervised learning methods, Isolation Forest and Local Outlier Factor are proposed to predict the welding quality online for the first time. Another critical problem is that the unsupervised learning model lacks evaluation criteria. This paper presented the concept of separation degree between anomalous data set and normal data set to solve this problem. The classification performance of the model is judged by comparing the separation degree. According to the separation degree calculation results, the Isolation Forest's classification performance is better than the Local Outlier Factor. Finally, according to the classification results of Isolation Forest, the correlation between state information and welding quality is analyzed. It is found that the upset pressure and dynamic resistance can reflect the change of welding quality, but the correlation between electrode displacement and welding quality is not significant.



中文翻译:

一种基于隔离森林和局部异常因子的链条电阻镦粗对接焊在线质量监测新方法

小链阻对焊由于生产频率高、焊接时间短,往往焊接质量不稳定。然而,目前还缺乏对电阻镦锻对焊在线监测的研究。本研究提出收集焊接过程中的状态信息,并结合无监督学习方法预测最终焊接质量。采集的状态信息包括动态电阻曲线、电极位移曲线和镦粗压力曲线。并收集了308274个焊接接头的状态信息。由于链条生产频率高,用破坏性检测方法很难获得质量标签。常用的监督学习不再适用。针对这一挑战,无监督学习方法,首次提出了隔离森林和局部异常因子在线预测焊接质量。另一个关键问题是无监督学习模型缺乏评估标准。本文提出了异常数据集与正常数据集分离度的概念来解决这个问题。通过比较分离度来判断模型的分类性能。根据分离度计算结果,Isolation Forest的分类性能优于Local Outlier Factor。最后,根据Isolation Forest的分类结果,分析了状态信息与焊接质量的相关性。发现镦锻压力和动态阻力可以反映焊接质量的变化,

更新日期:2021-06-18
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