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

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Quality Prediction Model for Manufacturing Process of Free-Machining 303-series Stainless Steel Small Rolling Wire Rods

쾌삭 303계 스테인리스강 소형 압연 선재 제조 공정의 생산품질 예측 모형

  • Seo, Seokjun (Department of Smart Manufacturing Engineering, Changwon National University) ;
  • Kim, Heungseob (Department of Smart Manufacturing Engineering, Changwon National University)
  • 서석준 (창원대학교 스마트제조융합협동과정) ;
  • 김흥섭 (창원대학교 스마트제조융합협동과정)
  • Received : 2021.10.06
  • Accepted : 2021.12.06
  • Published : 2021.12.31
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Abstract

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

Keywords

Acknowledgement

This results was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (MOE) (2021RIS-003)

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