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Erschienen in: Journal of Intelligent Manufacturing 1/2022

29.08.2020

Online monitoring of resistance spot welding electrode wear state based on dynamic resistance

verfasst von: Lei Zhou, Tianjian Li, Wenjia Zheng, Zhongdian Zhang, Zhenglong Lei, Laijun Wu, Shiliang Zhu, Wenming Wang

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2022

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Abstract

The electrode state is an important factor affecting the welding quality in the process of resistance spot welding, but there is still lack of an effective method to monitor the electrode wear state. In this paper, a novel online monitoring method of electrode wear state is proposed by exploring the variation pattern of dynamic resistance. The evaluation method of time series similarity is important for dynamic resistance data processing, and two types of evaluation methods including static evaluation method and dynamic evaluation method are proposed in this paper. The static evaluation methods include shape change factor, dynamic resistance decrease ratio and peak time delay, and the dynamic evaluation method refers to the trend change factor. The welding process parameters and the welding material are kept unchanged during the experiment, the newly polished electrode is used to weld 1300 times continuously, and dynamic resistance is collected. According to the results of data processing, the change of the electrode state can be divided into three stages: The electrode state is stable when the welding number is less than 360. When the welding number is in the range of 360–800, the electrode state is in the transition stage, and the electrode state rapidly deteriorates with the increase of the welding number. When the welding number is more than 800, the electrode state is completely deteriorated, and the electrode needs to be dressed. This study may pave the way for online monitoring of electrode wear state.

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Metadaten
Titel
Online monitoring of resistance spot welding electrode wear state based on dynamic resistance
verfasst von
Lei Zhou
Tianjian Li
Wenjia Zheng
Zhongdian Zhang
Zhenglong Lei
Laijun Wu
Shiliang Zhu
Wenming Wang
Publikationsdatum
29.08.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 1/2022
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-020-01650-6

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