Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

1. 서 론
2. 선행 연구
3. 데이터 및 연구 방법
    3.1 패키징 생산 공정 작업방식
    3.2 사용 데이터셋
    3.3 데이터 EDA(Exploratory Data Analysis)
    3.4 머신러닝 모델
    3.5 데이터 모델링
4. 결과 및 고찰
5. 요약 및 향후 연구
Acknowledgement
References

• Se Wook Kwon(Department of Digital Healthcare Research Korea Institute of Industrial Technology) | 권세욱 (한국생산기술연구원 디지털헬스케어연구부문)
• Young Joo Hyun(Department of Digital Healthcare Research Korea Institute of Industrial Technology) | 현영주 (한국생산기술연구원 디지털헬스케어연구부문)
• Hyun Chul Tae(Department of Digital Healthcare Research Korea Institute of Industrial Technology) | 태현철 (한국생산기술연구원 디지털헬스케어연구부문) Corresponding Author