머신러닝을 통한 잉크 필요량 예측 알고리즘

• 영문명: Machine Learning Algorithm for Estimating Ink Usage
• 발행기관: 한국산업경영시스템학회
• 저자명: 권세욱 현영주 태현철
• 간행물 정보: 『산업경영시스템학회지』제46권 제1호, 23~31쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.03.31

영문 초록

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.

목차

1. 서론

2. 선행 연구

3. 데이터 및 연구 방법

4. 결과 및 고찰

5. 요약 및 향후 연구

키워드

Machine Learning XGBoost Ink Usage Regression