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

Johannes Heilmeier, Michael K. Kick, Sophie Grabmann, Tatek Muschol, Franz Schlicht, Felix von Hundelshausen, Hans Georg von Ribbeck, Tony Weiss, Michael F. Zaeh

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number022007
JournalJournal of Laser Applications
Volume36
Issue number2
DOIs
StatePublished - 1 May 2024

Keywords

  • acoustic emissions
  • battery cells
  • laser beam welding
  • process monitoring
  • sensor data fusion
  • spectral and emissions
  • weld seam quality

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