Online quantitative evaluation of expulsion in resistance spot welding

Xia, Yu-Jun; Su, Ze-Wei; Li, Yong Bing; Zhou, Lang; Yan, Shaofeng

doi:10.1016/j.jmapro.2019.08.004

Cited by 46 publications

(15 citation statements)

References 18 publications

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“…The strength of resistance spot welds depends on the welding parameters and the type of materials. The parameters of resistance spot welding are the welding time, the welding current and the pressing force of the electrodes [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Joining the High-Strength Steel Sheets Used in Car Body Production

Kaščák¹,

Cmorej²,

Spišák³

et al. 2021

Adv. Sci. Technol. Res. J.

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Nowadays, there are several important reasons for using high-strength sheets in the manufacturing of car bodies. Car manufacturers choose the steel with good formability, fatigue resistance and ability to absorb impact energy. Microalloyed steels and dual-phase steels are the materials which fulfil the above-mentioned criteria. The application of high-strength sheets has led to the development of new materials joining techniques. Mechanical joining, such as clinching, is the innovative technique to join these progressive materials. Materials of different thicknesses can be joined by clinching. The paper focuses on the comparison of the properties of the joints made by clinching and resistance spot welding. The application of resistance spot welding is still the most used joining method in car body production. These properties were investigated by tensile test and metallographic observation. The HCT600X+ZF, HCT600X+Z and HX420LAD+Z steel sheets were used for the experiments. The results of tensile test show that the values of load-bearing capacity of clinched joints reached from 3900 N to 5900 N and the resistance spot welded joints reached the values of load-bearing capacity from 12000 N to 19500 N. In comparison to the resistance spot welded joints, the clinched joints reached from 32 to 48% of load-bearing capacity.

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Section: Introductionmentioning

confidence: 99%

Joining the High-Strength Steel Sheets Used in Car Body Production

Kaščák¹,

Cmorej²,

Spišák³

et al. 2021

Adv. Sci. Technol. Res. J.

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“…The deposition of the metal coating on the copper electrodes generates a change in the properties of the electrode and, therefore, the wear of the tips [19]. In addition to the reduction of the size of the nugget, the wear of the electrode is of great importance in the presence of weld ejections and other quality defects and can be the cause of 60% of quality problems [20].…”

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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“…For each scenario, two contamination models, referred to as Out E and Out P, with three increasing contamination levels, referred to as C1, C2 and C3, are considered. The former mimics a splash weld (expulsion), caused by excessive welding current, while the latter resembles phase shift of the peak time caused by an increased electrode force (Xia et al, 2019). Moreover, we consider also a scenario, referred to as Scenario 0, representing settings where the Phase I sample is not contaminated.…”

Section: Simulation Studymentioning

confidence: 99%

Robust Multivariate Functional Control Charts

Capezza¹,

Centofanti²,

Lepore³

et al. 2022

Preprint

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Profile monitoring assesses the stability over time of one or multiple functional quality characteristics to quickly detect special causes of variation that act on a process. In modern Industry 4.0 applications, a huge amount of data is acquired during manufacturing processes that are often contaminated with anomalous observations in the form of both casewise and cellwise outliers. Because anomalous observations can seriously affect the monitoring performance, profile monitoring methods that are able to successfully deal with outliers are needed. To this aim, we propose a new framework, referred to as robust multivariate functional control charts (RoMFCC), that is able to monitor multivariate functional data while being robust to both functional casewise and cellwise outliers. The RoMFCC relies on four main elements: (I) a univariate filter to identify functional cellwise outliers to be replaced by missing components; (II) a robust functional data imputation method of missing values; (III) a casewise robust dimensionality reduction; (IV) a monitoring strategy for the multivariate functional quality characteristic. An extensive Monte Carlo simulation study is performed to quantify the monitoring performance of the RoMFCC by comparing it with some competing methods already available in the literature. Finally, a motivating real-case study is presented where the proposed framework is used to monitor a resistance spot welding process in the automotive industry.

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Online quantitative evaluation of expulsion in resistance spot welding

Cited by 46 publications

References 18 publications

Joining the High-Strength Steel Sheets Used in Car Body Production

Joining the High-Strength Steel Sheets Used in Car Body Production

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

Robust Multivariate Functional Control Charts

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