This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

1. 서 론
2. 데이터 정의 및 전처리
    2.1 데이터 정의
3. 차원축소
    3.1 상관관계 분석
    3.2 변수 중요도
4. 기계학습 모델링
    4.1 품질 예측 모델
    4.2 성능 평가
5. 결론 및 향후 연구방향
References

• Seokjun Seo(창원대학교 스마트제조융합협동과정) | 서석준
• Donghyeon Ahn(창원대학교 스마트제조융합협동과정) | 안동현
• Seongil Heo(창원대학교 스마트제조융합협동과정) | 허성일
• Heungseob Kim(창원대학교 스마트제조융합협동과정/산업시스템공학과) | 김흥섭 Corresponding Author