Monitoring of resistance spot welding expulsion based on machine learning

Zhou, Lei; Zhang, Tianyi; Zhang, Zhongdian; Lei, Zhenglong; Zhu, Shi-Liang

doi:10.1080/13621718.2022.2051408

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…On the other hand, for [56,61], as the monitoring setup includes pressure sensors, the feasibility may be limited to stationary applications, but this depends on the design of the welding machine. The majority of the available studies for expulsion's online assessment [81][82][83][84] based their success on the classification of features extracted from the dynamic signals of the current, voltage, and pressure. Finally, another aspect that makes online expulsion assurance a real thing is the available studies concerning cases that involve different materials and welding infrastructure.…”

Section: A Realistic Scenario For Online Quality Assessment In Produc...mentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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Resistance spot welding is a process with high variability regarding the quality of the produced joints. This means that key performance indicators (KPIs) such as geometrical and mechanical features as well as failure modes can deviate from the initial design even if the same process parameters are used. The industry has developed quality assurance programs and quality control methods for tracking these KPIs; however, most of them are based on offline or/and destructive practices. On the other hand, state-of-the-art approaches have made online quality assessment feasible and proved its necessity if a 100% quality rate is required. However, limited attention has been given to “closing the loop” and providing feedback for preventing and correcting process anomalies that cause quality variations in real time. In this study, the main gaps between the state of practice and the state of the art are discussed in the context of quality assurance for resistance spot welding. Finally, the role and importance of digital twins by taking into consideration the entire welding ecosystem in quality assurance are discussed in order to form the prospects for the road ahead.

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Section: A Realistic Scenario For Online Quality Assessment In Produc...mentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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“…Our increasing Internet of Things (IoT) data enables optimization where conventional methods reach their limitations. Data-based approaches allow non-destructive online monitoring of complex processes and their components [6]. Monitoring the electrode state increases process reliability and stability, and once implemented, the methodology can be transferred to hundreds of industrial robots of a comparable type and even to other use cases and production plants.…”

Section: Related Workmentioning

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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“…The performance of the machine learning model closely depends on the hyperparameter [27]. In the ROCKET algorithm, the hyperparameter is the number of random kernels, so that the performance of the models with the number of random kernels from 5000 to 15,000 was compared.…”

Section: Hyperparameter Optimisationmentioning

confidence: 99%

Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform

Jang,

Lee,

Lee

et al. 2023

Science and Technology of Welding and Joining

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A monitoring technique for detecting changes in the root gap of butt joints during the flux-cored arc welding (FCAW) was proposed. FCAW experiments were conducted for both increasing and decreasing root gap conditions, and current and voltage were measured during the root-pass welding. The measured time series signals were used as input data for training Random Convolution Kernel Transform (ROCKET) algorithm, which consists of a feature extractor with multiple random kernels, and a linear classifier. A univariate model using current and voltage, respectively, and a multivariate model using both were compared, and the multivariate model showed the highest classification accuracy of 96.2%. Moreover, the classification errors were investigated by correlating the geometry of the root bead with the measured signals.

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Monitoring of resistance spot welding expulsion based on machine learning

Cited by 14 publications

References 19 publications

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

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

Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform

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