In the production sites of small and medium sized manufacturing enterprises, the increasing proportion of foreign workers has led to frequent difficulties in responding promptly to process defects and equipment setting errors during night and weekend shifts due to the absence of Korean supervisors. If such issues are not addressed in a timely manner, they can lead to large scale defects and reduced production efficiency. In this study, we developed an AI-based defect prediction and prevention system for the bearing machining process to overcome these on site management limitations. Real time machining data, equipment information, and quality inspection results were collected from the production lines of the target company, and the prediction accuracy of three models, RNN(Recurrent Neural Network), LSTM(Long Short-Term Memory), and GRU(Gated Recurrent Unit), was compared. As a result, the LSTM model demonstrated the best performance. The developed system visualizes real time defect prediction results in the form of a dashboard, enabling workers to immediately detect anomalies and adjust the process accordingly. Particularly in bearing machining processes where mass production occurs in short periods, the risk of lot level defects is high, while this system can contribute to improved production quality and efficiency by enabling early defect prediction and immediate response.

1. 서 론
2. 관련 연구
3. 문제 정의
4. 시스템 설계 및 현장 적용
    4.1 주요 변수 도출
    4.2 데이터 수집
    4.3 예측 모델 비교 분석
    4.4 불량 예측 시스템 현장 적용 및 운영 체계
    4.5 현장 적용 결과
5. 결 론
References

• Dae-Youn Kim(Department of Smart Manufacturing Convergence Systems Engineering, Dong-A University) | 김대연 (동아대학교 스마트생산융합시스템공학과)
• Dongwoo Go(Department of Industrial Management Engineering, Dong-A University) | 고동우 (동아대학교 산업경영공학과)
• Seunghoon Lee(Department of Industrial Management Engineering, Dong-A University) | 이승훈 (동아대학교 산업경영공학과) Corresponding author