This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

1. 연구 배경
2. 관련 연구
3. 스핀들 부하 데이터 수집 및 전처리
    3.1 절삭 공정 및 데이터 수집
    3.2 데이터 전처리
4. 수명 예측 모델 개발
    4.1 데이터 기초 분석
    4.2 모델 학습
    4.3 종합 분석 결과
5. 공구 수명 예측
6. 결 론
References

• Su A Shin(VMS Solutions) | 신수아 (브이엠에스 솔루션스)
• Inho Yi(Department of Industrial and Systems Engineering, GyeongSang National University) | 이인호 (경상국립대학교 산업시스템공학부)
• SungMoon Bae(Department of Industrial and Systems Engineering, GyeongSang National University) | 배성문 (경상국립대학교 산업시스템공학부) Corresponding author