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Flux-cored arc welding (FCAWFlux-Cored Arc Welding (FCAW)) has become a popular welding technique in developing country shipyards because of its high productivity. The purpose of this study is to monitor the welder performanceWelder performances during the FCAWFlux-Cored Arc Welding (FCAW) process, which applies different electrode movements compared with shielded metal arc welding (SMAW) as previously explored. The inertia measurementMeasurement units (IMU) sensors were used to capture the wrist-hand motions of a welder, and the support vector machine (SVMSupport Vector Machine (SVM)) method was used to evaluate the welder's performance while welding. The first step involved collecting hand motions of FCAWFlux-Cored Arc Welding (FCAW) butt joint welding activities in three positions of 1G, 2G, and 3G, each of different welder skills (6 classes), using the IMU wearable sensorsWearable sensors. The resulting comma-separated value (csv) data recorded was extracted using features such as the root mean square, correlation index, spectral peaks, and spectral power. Finally, the SVMSupport Vector Machine (SVM) method classified and identified typical hand motions of various welder performanceWelder performances types. The outcomes demonstrated that the SVMSupport Vector Machine (SVM) method could recognize the welding performances of the FCAWFlux-Cored Arc Welding (FCAW) welders with an accuracy of 85%.

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Monitoring Welders Performance in Flux-Cored Arc Welding Using Hand Motion Wearable Sensors and Support Vector Machine

  • 章节

  • 发表日期：

    2026年

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  • Triwilaswandio Wuruk Pribadi,
  • Takeshi Shinoda,
  • Fransisco Juan Sunandar,
  • Vialdo Muhammad Virmansyah

摘要

Flux-cored arc welding (FCAWFlux-Cored Arc Welding (FCAW)) has become a popular welding technique in developing country shipyards because of its high productivity. The purpose of this study is to monitor the welder performanceWelder performances during the FCAWFlux-Cored Arc Welding (FCAW) process, which applies different electrode movements compared with shielded metal arc welding (SMAW) as previously explored. The inertia measurementMeasurement units (IMU) sensors were used to capture the wrist-hand motions of a welder, and the support vector machine (SVMSupport Vector Machine (SVM)) method was used to evaluate the welder's performance while welding. The first step involved collecting hand motions of FCAWFlux-Cored Arc Welding (FCAW) butt joint welding activities in three positions of 1G, 2G, and 3G, each of different welder skills (6 classes), using the IMU wearable sensorsWearable sensors. The resulting comma-separated value (csv) data recorded was extracted using features such as the root mean square, correlation index, spectral peaks, and spectral power. Finally, the SVMSupport Vector Machine (SVM) method classified and identified typical hand motions of various welder performanceWelder performances types. The outcomes demonstrated that the SVMSupport Vector Machine (SVM) method could recognize the welding performances of the FCAWFlux-Cored Arc Welding (FCAW) welders with an accuracy of 85%.