Grease consistency is a critical quality factor in industrial lubrication processes, as it significantly affects mechanical performance, operational stability, and product durability. In grease manufacturing, fluctuations in process variables such as feed temperature, evaporation time, flow rate, and environmental conditions can cause inconsistencies in quality, which may lead to operational defects or increased maintenance costs. To address this challenge, this study proposes a predictive modeling approach for forecasting grease consistency with the aim of enhancing process quality. Real manufacturing process data were collected from a grease production facility, and irrelevant or highly correlated variables were eliminated through multicollinearity analysis and dimensionality reduction. Multiple machine learning regression techniques were applied and evaluated to identify the most effective model for predicting grease consistency. Through systematic comparison, the final predictive model was developed to provide accurate consistency estimation based on selected process variables. The proposed model enables proactive quality control by allowing consistency deviations to be detected early, thereby supporting process optimization and decision-making in manufacturing environments. This research demonstrates the applicability of data-driven predictive modeling in the grease industry and contributes to the development of intelligent quality management strategies in modern manufacturing. The findings suggest that machine learning-based consistency prediction can play a key role in improving production efficiency and ensuring stable product performance.

1. 서 론
2. 이론적 배경
3. 데이터 구성 및 전처리
4. 모델링 전략
5. 실험 결과
6. 결론 및 시사점
References

• Donghyun Kim(Department of Industrial Engineering, Kongju National University) | 김동현 (공주대학교 산업공학과)
• Dongju Lee(Department of Industrial Engineering, Kongju National University) | 이동주 (공주대학교 산업공학과) Corresponding author