CNC 가공 공정 불량 예측 및 변수 영향력 분석 (Defect Prediction and Variable Impact Analysis in CNC Machining Process)

서지정보

· 발행기관 : 한국품질경영학회
· 수록지 정보 : 품질경영학회지 / 52권 / 2호 / 185 ~ 199페이지
· 저자명 : 홍지수, 정영진, 강성우

초록

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of
process management. Controlling key variables within the process is essential for enhancing the quality of
the produced items. In this study, we aim to identify key variables influencing product defects and facilitate
quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations)
Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM,
XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test
data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent
Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC
machining process.
Results: By comparing the performances of different models, the selected CatBoost model demonstrated an
Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively
of negatively impact the dependent variable(good/defective product). We identify variables with relatively
low importance, suggesting variables that should be prioritized for management.
Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional
machine learning techniques. This study holds significance in identifying key variables that should be prioritized
for management in CNC machining process. It is expected to contribute to enhancing the production
quality of the CNC machining process.

영어초록

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of
process management. Controlling key variables within the process is essential for enhancing the quality of
the produced items. In this study, we aim to identify key variables influencing product defects and facilitate
quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations)
Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM,
XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test
data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent
Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC
machining process.
Results: By comparing the performances of different models, the selected CatBoost model demonstrated an
Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively
of negatively impact the dependent variable(good/defective product). We identify variables with relatively
low importance, suggesting variables that should be prioritized for management.
Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional
machine learning techniques. This study holds significance in identifying key variables that should be prioritized
for management in CNC machining process. It is expected to contribute to enhancing the production
quality of the CNC machining process.

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