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

Zhou, Lei; Li, Tianjian; Zheng, Wentian; Zhang, Zhongdian; Lei, Zhenglong; Wu, Laijun; Zhu, Shi-Liang; Wen-ming, Wang

doi:10.1007/s10845-020-01650-6

Cited by 16 publications

(23 citation statements)

References 17 publications

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“…Finally, Zhou et al [25] presented a method based on the analysis of dynamic resistance during welding to determine wear. The main disadvantage of this study is that it assumed that the dynamic resistance trend depends only on the wear of the electrodes when, actually, this variation can depend on different factors, such as the final quality of the welding point, as stated by Zhao et al [26], or the temperature and pressure, as stated by Whang et al [27], among many other factors.…”

Section: Electrode Wear and Millingmentioning

confidence: 99%

An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process

Ibañez

García²,

Soret

et al. 2022

Sensors

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Resistance spot welding is one of the most widely used metal joining processes in the manufacturing industry, used for structural body manufacturing, railway vehicle construction, electronics manufacturing, battery manufacturing, etc. Due to its wide use, the quality of welded joints is of great importance to the manufacturing industry, as it is critical for ensuring the integrity of finished products, such as car bodies, that withstand high levels of stress. The quality of the welding is influenced both by the programming of the welding and by the good condition of the mechanical part that carries out the welding. These mechanical factors, such as electrode geometry and wear, degrade over time. As the welding points are made, the geometry and properties of the electrodes change, so they undergo a milling process to remove impurities and return them to their initial geometry. Sometimes the milling is deficient, and the electrode continues to wear, causing welding problems such as loose spots and metal spatter. This article presents a method for condition monitoring of the milling process and weld wear based on existing data in real production lines. The use of unsupervised clustering methods is proposed to perform a check by which, using current and resistance data, the electrode wear is grouped. Specifically, a method using multidimensional k-means for the condition monitoring of electrode wear is established. This research gives a real and applicable solution for reducing the quality problems caused by milling defects and electrode wear in the production lines of high-production manufacturing industries, presenting a system for sending alarms based on the behavior of welding variables.

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Section: Electrode Wear and Millingmentioning

confidence: 99%

An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process

Ibañez

García²,

Soret

et al. 2022

Sensors

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“…There have been a number of research studies carried out to investigate the effect of these parameters on nugget size and spot quality [4][5][6][7][8], as well as to optimize the parameters [9,10] and monitor the process [11][12][13][14] in order to improve spot quality. An increase of welding current and heat input increases the nugget size and joint strength for materials like stainless steel, aluminium, magnesium [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Detection of electrode misalignment and its effect on joint quality in resistance spot welding: a low-cost computer vision-based approach

Bhavsar,

Dutta,

Pal

2024

Meas. Sci. Technol.

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Electrode misalignment in resistance spot welding can be caused by poor fitting or deformation of electrode with continuous usage. This leads to asymmetric weld nugget formation, porosity and expulsion. This paper presents a novel low-cost real-time inspection system for angular misalignment using an image processing approach. The proposed solution can effectively segment the electrode tips even from the image captured at noisy industrial background such as automotive assembly line, by using a regional convolutional neural network based object identification method. The trained model has a mean average precision and recall of 99.01% and 96.6%, respectively. A series of image processing tools and mathematical operations were used to identify the edge line contours of electrode tips accurately from the detection mask, and determine the angular misalignment with a maximum deviation of less than 0.06˚. Experimental results showed that the weld nuggets exhibited porosity, shrinkage voids, and cracks when performed under angular misalignment conditions.

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“…The frequency for tip dressing is based on experience and does not reflect the actual state of the electrode. Furthermore, a generic number for the amount of spot welds between tip dressing is set for several welding machines even though the electrode state is affected by various factors, such as the welding material or the welding equipment, and fluctuates greatly even with the same welding parameters [4,5]. Therefore, the frequency of tip dressing is chosen in a preventive and pessimistic manner.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Inline Monitoring of the Electrode State in Resistance Spot Welding

Durnagöz

2024

IJEETC

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Resistance Spot Welding (RSW) is a key technology for joining car body parts. During the process, the upper and lower electrodes of the welding gun are subject to wear, which affects the electric current flow and thus, the quality of the spot weld. To counteract this effect, the electrode tip is dressed after a predefined number of spot welds. The frequency for tip dressing is based on experience and does currently not directly reflect the actual state of the electrode. This work provides an approach to inline electrode state monitoring to determine the ideal and demand-based point in time for electrode tip dressing based on process data. We avoid costly and time-intensive experimental labeling of electrode states and utilize changes in the dynamic electrical resistance between dressing cycles to represent the electrode wear state. To describe the changes in the dynamic electrical resistance curve, new features such as the peak time delay are calculated and visualized. We evaluate our approach with a real-world data set that stems from a dynamic and complex environment out of a series production line, which, in contrast to laboratory data, ensures a successful application of the proposed methods in an industrial setting.

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

Cited by 16 publications

References 17 publications

An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process

An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process

Detection of electrode misalignment and its effect on joint quality in resistance spot welding: a low-cost computer vision-based approach

An Approach to Inline Monitoring of the Electrode State in Resistance Spot Welding

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