Analysis of resistance welding processes and expulsion of liquid metal from the weld nugget

Mikno, Zygmunt; Pilarczyk, A.; Korzeniowski, Marcin; Kustroń, Paweł; Ambroziak, Andrzej

doi:10.1016/j.acme.2017.08.003

Cited by 18 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a statistical method, regression analysis is used to explain the functional relationship between a dependent variable and one or more independent variables. [6][7][8] According to the number of variables involved, regression analysis can be divided into univariate and multiple regression. 9) According to the different relationship between the dependent variable and the independent variable, regression analysis is divided into linear regression and nonlinear regression.…”

Section: Regression Model and Analysis Of Welding Parameters 331 Regr...mentioning

confidence: 99%

Optimization and Improvement of the Projection Welding of Nut Based on Regression Analysis

Xing

Zhang

et al. 2023

ISIJ Int.

View full text Add to dashboard Cite

Since it has the advantages of high production efficiency, no shunting effects, and the ability to arrange solder joints on narrow locations without the limitation of point distance, projection welding of nut has been widely used in the automotive industry. This paper presents a new welding quality prediction and analysis framework by using regression analysis techniques. The relationship between the welding parameters (welding time, welding current and electrode force) and the Pull-out load of the M6 welded square nut and the 1.5 mm thick pickled hot-rolled steel was obtained by multiple nonlinear regression analysis. The influence and interaction of welding parameters on the Pull-out load are discussed. Using this regression model, a better combination of process parameters can be obtained. The mechanical properties, microstructure and microhardness of welded joints were also investigated.

show abstract

Section: Regression Model and Analysis Of Welding Parameters 331 Regr...mentioning

confidence: 99%

Optimization and Improvement of the Projection Welding of Nut Based on Regression Analysis

Xing

Zhang

et al. 2023

ISIJ Int.

View full text Add to dashboard Cite

show abstract

“…The metal temperature around the nugget is lower than the melting point, and the weldment is in a plastic state, usually called a plastic ring [3]. When anomalous conditions occur, such as excessive welding current [4] and insufficient electrode pressure, the plastic ring cannot surround the molten metal, which will cause expulsion in the welding joint [5]. During expulsion, the ejection of molten metal greatly influences the welded joint quality [6].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of resistance spot welding expulsion based on machine learning

Zhou

Zhang

et al. 2022

Science and Technology of Welding and Joining

View full text Add to dashboard Cite

There is still a lack of effective monitoring methods for resistance spot welding expulsion in manufacturing sites. In this study, more than 63,766 dynamic resistance curves of welding points were collected in manufacturing sites, and the machine learning method is applied to monitor the resistance spot welding expulsion. Generally, the model's generalisation ability is poor due to the complex conditions of the manufacturing site. This study solves this problem by improving the data preprocessing and model selection method. The experimental results show that when the process parameters do not change, the accuracy of expulsion recognition can easily reach 95%. When the process parameters change, it can reach 90% by selecting the appropriate data preprocessing method and model selection.

show abstract

“…Welding parameters are important indicators in online quality monitoring. Scholars have undertaken many studies in recognition of expulsion defects from different characteristic parameters, including the welding voltage, welding current, dynamic resistance, electrode displacement, electrode force, and so on [4][5][6][7]. Chen [8] has proposed an evaluation method based on the online monitoring of the weld quality of spot welded titanium alloys.…”

Section: Introductionmentioning

confidence: 99%

Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks

Chen

Xiao

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to identify the expulsion from electrode force sensing signals. The rapid step impulse and resultant damping vibration of electrode force was determined as a robust indication of expulsion, and this feature was extracted from the electrode force waveform by seven-layer wavelet decomposition with Daubechies5 wavelets. Then, the energy distribution proportion of the decomposed detail signals were calculated, and the highest-energy one was selected as the target signal. Two statistical indexes were introduced in this paper to measure the target signal in overall situation and volatility. The bigger the peak-to-peak amplitude is, the more violent the fluctuation is. Moreover, the higher the kurtosis index is, the stronger the impact is, and the lower the dispersion degree of the data is. Experimental analysis showed that neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect individually, because of the early signal fluctuation, likely affected by the work-piece clamping, work-piece clearance, or the oxide film thickness. Therefore, the BP neural networks were introduced to identify the expulsion defects, which is a mature and stable non-linear pattern recognition method. Testing experiments presented good results with the trained networks and improved the evaluable accuracy effectively in the quality assessment of the resistance spot welding.

show abstract

Analysis of resistance welding processes and expulsion of liquid metal from the weld nugget

Cited by 18 publications

References 5 publications

Optimization and Improvement of the Projection Welding of Nut Based on Regression Analysis

Optimization and Improvement of the Projection Welding of Nut Based on Regression Analysis

Monitoring of resistance spot welding expulsion based on machine learning

Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks

Contact Info

Product

Resources

About