Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Machine Learning Algorithm for Estimating Ink Usage

머신러닝을 통한 잉크 필요량 예측 알고리즘

  • Se Wook Kwon (Department of Digital Healthcare Research Korea Institute of Industrial Technology) ;
  • Young Joo Hyun (Department of Digital Healthcare Research Korea Institute of Industrial Technology) ;
  • Hyun Chul Tae (Department of Digital Healthcare Research Korea Institute of Industrial Technology)
  • 권세욱 (한국생산기술연구원 디지털헬스케어연구부문) ;
  • 현영주 (한국생산기술연구원 디지털헬스케어연구부문) ;
  • 태현철 (한국생산기술연구원 디지털헬스케어연구부문)
  • Received : 2022.11.21
  • Accepted : 2022.12.27
  • Published : 2023.03.31
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Abstract

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.

Keywords

Acknowledgement

This research was a part of the project titled 'forest science-technology R&D program (2021383A00-2223-0101)', funded by the Korea Forestry Promotion Institute (Korea National Arboretum), Korea. This study has been funded by the following project of KITECH (Korea Institute of Industrial Technology), which is "Development of AI-based packaging manufacturing cost estimation system".

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