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Amidst the advent of Industry 4.0, the manufacturing industry is exploring AI methodologies and other data-driven approaches for the understanding and optimization of gas metal arc welding (GMAW) processes. Various data sources such as process data logs and image data are available to the users of modern welding systems. However, to make good use of the data for machine learning, data sets of different quality and information density have to be fused. In this paper, we propose strategies for improving the dataset quality of time series process data and image data from the GMAW process. We explore resampling strategies to ensure the harmonization of time series data. Additionally, ideas for improving image quality from welding process cameras are discussed.

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Data Quality Strategies in Gas Metal Arc Welding Production for Machine Learning Applications

  • 会议论文

  • 发表日期：

    2025年

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  • Jorge Rodríguez-Echeverría,
  • Evans Doe Ocansey,
  • Roxana-Maria Holom,
  • Tomasz Piotr Michno,
  • Hannes Hinterbichler,
  • Pauline Meyer-Heye,
  • José Neves,
  • Martin Schnall,
  • Matthias Hartmann,
  • Sidharta Gautama

摘要

Amidst the advent of Industry 4.0, the manufacturing industry is exploring AI methodologies and other data-driven approaches for the understanding and optimization of gas metal arc welding (GMAW) processes. Various data sources such as process data logs and image data are available to the users of modern welding systems. However, to make good use of the data for machine learning, data sets of different quality and information density have to be fused. In this paper, we propose strategies for improving the dataset quality of time series process data and image data from the GMAW process. We explore resampling strategies to ensure the harmonization of time series data. Additionally, ideas for improving image quality from welding process cameras are discussed.