A holistic approach for an intelligent laser beam welding architecture using machine learning for the welding of metallic bipolar plates for polymer electrolyte membrane fuel cells

Weiss, Tony; Kick, Michael K.; Grabmann, Sophie; Geiger, Christian; Mayr, Lukas; Wudy, Katrin; Zaeh, Michael F.

doi:10.1016/j.procir.2022.08.089

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Cited by 4 publications

References 16 publications

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Laser Cutting, Drilling, and Piercing

Walia,

Varshney,

Singh

2024

Laser‐Assisted Machining

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Laser Cutting, Drilling, and Piercing

Walia,

Varshney,

Singh

2024

Laser‐Assisted Machining

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Analysis of photodiode signals for monitoring the laser beam welding process of cell-internal contacts in lithium-ion batteries

Grabmann,

Harst,

Bernauer

et al. 2024

Optics & Laser Technology

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Inline failure detection in laser beam welding of battery cells: Acoustic and spectral emission analysis for quality monitoring

Heilmeier,

Kick,

Grabmann

et al. 2024

Journal of Laser Applications

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The mobility sector is considered a major contributor to global greenhouse gas emissions and air pollution. As a result, many countries have initiated the transition from fossil fuel-powered to electrified powertrains. This transformation of the powertrain concept will lead to a rapid increase in the production of electric vehicles and, therefore, to a high demand for so-called traction batteries. As a production step of the traction batteries, a connection between the cell connector and the terminal of the battery cell has to be manufactured. For this purpose, laser beam welding is a reliable and efficient joining technique. In order to ensure continuous quality of the welding process during production and to detect defects in real time, reliable process monitoring is required. In this study, spectral and acoustic emissions during laser beam welding were recorded using a laser welding monitor and an optical microphone. For determining possible correlations between the signals and weld defects, various failure cases were generated by the systematic placement of disturbance elements. These elements included a contaminated surface, a gap between the cell connector and the battery cell, and a misalignment of the cell connector. Based on the recorded signals, statistical metrics were calculated. Finally, weld seams with and without defects were compared to assess the capability of both sensor systems for detecting the weld defects.

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A holistic approach for an intelligent laser beam welding architecture using machine learning for the welding of metallic bipolar plates for polymer electrolyte membrane fuel cells

Cited by 4 publications

References 16 publications

Laser Cutting, Drilling, and Piercing

Laser Cutting, Drilling, and Piercing

Analysis of photodiode signals for monitoring the laser beam welding process of cell-internal contacts in lithium-ion batteries

Inline failure detection in laser beam welding of battery cells: Acoustic and spectral emission analysis for quality monitoring

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