Comparative study on resistance and displacement based adaptive output tracking control strategies for resistance spot welding

Zhou, Lang; Xia, Yu-Jun; Yan, Shaofeng; Haselhuhn, Amberlee S.; Wegner, Diana; Li, Yong Bing; Carlson, Blair E.

doi:10.1016/j.jmapro.2020.03.061

Cited by 24 publications

(5 citation statements)

References 21 publications

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“…In recent years, the use of various nondestructive testing methods based on dynamic signals for the detection and evaluation of welding quality [7][8][9][10][11][12][13] has attracted the attention of numerous scholars. For example, Zhao et al [14] analyzed the relationship between the dynamic changes in the power curve and the nugget diameter of titanium alloy during RSW and developed a predictive model.…”

Section: Instructionmentioning

confidence: 99%

Online evaluation of resistance spot welding quality and defect classification

Yang

Xiang-dong

P.gao

2023

Meas. Sci. Technol.

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Resistance Spot Welding (RSW) is widely used in industrial product manufacturing, and weld quality is critical to ensuring the safety and reliability of mechanical structures. In order to improve the product quality, online monitoring of weld quality has become a priority. Therefore, a method based on online evaluation of mild steel RSW quality and automatic defect classification is proposed. First of all, the optimal welding parameters for DC01 mild steel RSW are determined, and the welding parameters are slightly adjusted, resulting in expulsion and incomplete fusion. By acquiring welding data in real time, a new dynamic resistance signal filtering method is used to extract features in the time domain from dynamic resistance signals, and then analyze the performance of the extracted features in the classifier. A new method that can accurately classify the quality of welding spots is established to improve the classification accuracy of normal and expulsion welding spots. The classification performance test results of the new method show that the online quality evaluation results are extremely consistent with the actual situation. This research can reliably evaluate the quality of DC01 the spots online and automatically classify the welding defects of RSW.

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Section: Instructionmentioning

confidence: 99%

Online evaluation of resistance spot welding quality and defect classification

Yang

Xiang-dong

P.gao

2023

Meas. Sci. Technol.

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“…Both cold and burn-through welds have a limited strength under static and/or dynamic loading conditions. Generally, the main causes leading to non-compliant spot welds can be referred to [1,[4][5][6][7]: electrodes (electrode wear, electrode misalignment, inappropriate electrode shape, dimensions, and material); process parameters (improper welding time, current, and electrode force); materials (incompatible metals in dissimilar joining, resistance shunting, sheet gap, edge proximity, and non-orthogonal electrode-sheet contact).…”

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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“…11,12) For decades, these two signals were always medium process variables to accomplish the nondestructive test (NDT) task 13,14) or design a tracking control strategy. 15) However, there were few works concerning obtaining these two signals by means of numerical calculation, so the works cannot be closely related to the actual engineering requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, numerical calculation using the FE method was conducted for a medium frequency DC RSW process, and the parent metal sheet used DP590 steel sheets, which is commonly employed in modern lightweight car design and manufacturing. 15) Unlike exploring the material characteristics only through the comparisons of the post-processing results, the characteristics of intermediate process variables, such as dynamic resistance and electrode displacement, were explored in depth, which can seriously study the characteristic variation in real time during different stages of the welding process. Additionally, the final nugget pattern and other features were considered.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Process Signals of Resistance Spot Welding for DP590 Steel Using Numerical Calculation

Zhou

Wang

et al. 2022

ISIJ Int.

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Although the finite element (FE) numerical calculation model has been extensively employed in analyzing metal characteristics, very few works use it to analyze features of the main process signals during the resistance spot welding (RSW) process. Hence, its generality was seriously restricted. In this work, a 2D FE model, which coupled thermal, electrical and mechanical fields, was established with reasonable meshing and parameters and boundary conditions. The parent metal used dual phase (DP) 590 steel, which is commonly employed in automobile lightweight manufacturing process. An actual experiment with the same conditions as the numerical calculation was conducted. The dynamic resistance and electrode displacement obtained from numerical calculation and actual experiment were seriously compared and analyzed. Many important characteristics of process signals have been reasonably explained by combining the results obtained from numerical calculation and experiments. The reasonability and accuracy of the FE numerical calculation were not only verified after the welding process but also examined in depth during the intermediate welding process. Hence, the work can effectively enlarge the applications of the FE model and strengthen the correlation between control strategy design, online quality estimation using main process signals and FE numerical calculation during the RSW process.

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Comparative study on resistance and displacement based adaptive output tracking control strategies for resistance spot welding

Cited by 24 publications

References 21 publications

Online evaluation of resistance spot welding quality and defect classification

Online evaluation of resistance spot welding quality and defect classification

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

Analysis of Process Signals of Resistance Spot Welding for DP590 Steel Using Numerical Calculation

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