Monitoring and intelligent control of electrode wear based on a measured electrode displacement curve in resistance spot welding

Zhang, Y S; Wang, H; Chen, G L; Zhang, X Q

doi:10.1088/0957-0233/18/3/040

Cited by 25 publications

(16 citation statements)

References 22 publications

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“…On the other hand, Zhang et al [24] proposed the use of electrode displacement to determine electrode wear; the discussed method provides a convincing solution but can only be carried out in those welding guns that have an integrated measurement system for electrode displacement, something that is usually lacking in pneumatic welding guns.…”

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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“…Electrode displacement (ED) has been extensively used to characterise the weld quality in a range of existing studies, via the techniques of image-process [12][13][14], non-contact laser displacement sensor [15,16] and contact-type displacement sensor [17,18]. ED curve describes the thermal expansion of sheets in the weld stage, where a range of factors affect the features of curves, such as surface conditions of the sheets [19], welding parameters [20] and electrode degradation [21]. Jou revealed the relationship between the profile features, such as peak value and ED velocity, and nugget diameter in an array of steels [20].…”

Section: Figure 1 Schematic Diagram Of Single Shuntingmentioning

confidence: 99%

Characteristics of shunting effect in resistance spot welding in mild steel based on electrode displacement

2018

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Shunting effect of resistance spot welding is evaluated based on the electrode displacement signals. The shunted welds in mild steel with different weld spacing were produced. The results showed that the weld spacing and nugget diameter were polynomial-correlated, and the minimum welding spacing of 20 mm can be derived from the results. Both the peak value and gradient of electrode displacement in the weld stage indicated strong correlations with the nugget diameters of shunted welds. Additional shunt path was found to further aggregate the shunting, suggesting the decline in the values of profile features. Furthermore, it is found that the shunting effect led to the decline of the dynamic resistance curves, which is contradictive to the trends between acceptable-sized and undersized welds claimed based on the single weld study. The paper shows that electrode displacement curves of shunting can be incorporated into existing quality monitoring system.

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“…Although many works have investigated electrode wear and its effect on spot weld quality [5,20,21,23], only some have focused on the online prediction of electrode wear. Statistical process control techniques have been developed based on the electrode displacement signals for monitoring the welding process during the electrode wear [24]. The electrode displacement signal was also used to monitor the electrode wear through a moving range chart [25] or to feed an arti cial neural network to predict the electrode contact area, commonly used as a wear indicator [26].…”

Section: Introductionmentioning

confidence: 99%

Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds

Panza

Bruno²,

György³

et al. 2023

Preprint

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The quality of resistance spot welding (RSW) joints is strongly affected by the electrode condition. Therefore, the capacity to automatically assess the effect of electrode deterioration on weld quality is crucial to ensure welds compliant with manufacturer quality requirements. This work develops a machine learning-based tool to automatically assess the influence of electrode wear on the quality of RSW welds. In this way, manufacturers can reduce the number of redundant spot welds due to electrode wear and increase competitiveness. Two different experimental campaigns were performed to evaluate the effect of electrode wear on the mechanical strength of spot welds. The resulting failure load of the joints have been used to build the weld quality classes of the machine learning tool, whereas data from electrode displacement and electrode force sensors, embedded in the welding machine, have been processed to identify the predictors of the tool. Five machine learning algorithms have been applied with a classification approach to predict whether the spot weld complies with the welding quality requirements. As a result, the artificial neural network is the most performing model with an accuracy of about 90%. This work paves the way for new maintenance strategies based on the real-time predictions provided by the machine learning algorithms and contributes to making the car-assembly process leaner and more sustainable.

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Monitoring and intelligent control of electrode wear based on a measured electrode displacement curve in resistance spot welding

Cited by 25 publications

References 22 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

Characteristics of shunting effect in resistance spot welding in mild steel based on electrode displacement

Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds

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