The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

1. 서 론
2. 문헌 연구
3. 적용된 데이터 마이닝 기법
    3.1 분류 예측을 위한 기존의 기법
    3.2 분류 예측을 위한 제안하는 기법
4. 실험 및 결과
    4.1 로지스틱 회귀분석
    4.2 Random Forest
    4.3 다층퍼셉트론(MLP)
    4.4 SA + Random Forest
    4.5 SA + MLP
    4.6 결과
5. 결론 및 연구과제

• Sang-Hyun Ko(Myungha Tech) | 고상현 (명하테크)
• Dongju Lee(Department of Industrial & Systems Engineering, Kongju National University) | 이동주 (공주대학교 산업시스템공학과) Corresponding author