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Development of Anomaly Detection Methods for a Collaborative Robot in Chemical Drum Assembly

  • Sung-Hun Jeong (Dept. of Mechanical Convergence Engineering, Kyungnam University) ;
  • Gi-Seong Kim (Dept. of Mechanical Convergence Engineering, Kyungnam University) ;
  • Shi-Baek Park (Infra Equipment Automation T/F, Samsung Electronics) ;
  • Han-Sung Kim (Dept. of Mechanical Engineering, Kyungnam University)
  • 투고 : 2024.07.29
  • 심사 : 2024.09.19
  • 발행 : 2024.10.31

초록

In this paper, anomaly detection methods for a collaborative robot during the chemical drum assembly process in the semiconductor industry are presented. The manual assembly of chemical drums has been automated using robots to prevent industrial accidents. However, the automation may increase downtime due to anomalies or failures in the robot manipulator tasks. To prevent this issue in advance, the methods to diagnose anomalous behaviors and conditions in the robotic automation workflow and subsequently resume tasks are proposed. To detect and diagnose anomalies in the tasks, the Random Forest classification method was utilized. Using this Random Forest classification, the collaborative robot anomaly detection model achieved an accuracy of 98.91%, successfully detecting all anomalies in the assembly process.

키워드

과제정보

This research was partially supported by Samsung Electronics-University Cooperation Research Project.

참고문헌

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