Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Defect Prediction and Variable Impact Analysis in CNC Machining Process

CNC 가공 공정 불량 예측 및 변수 영향력 분석

  • Hong, Ji Soo (Department of Industrial Engineering, Inha University) ;
  • Jung, Young Jin (Department of Industrial Engineering, Inha University) ;
  • Kang, Sung Woo (Department of Industrial Engineering, Inha University)
  • 홍지수 (인하대학교 산업경영공학과) ;
  • 정영진 (인하대학교 산업경영공학과) ;
  • 강성우 (인하대학교 산업경영공학과)
  • Received : 2024.02.02
  • Accepted : 2024.03.19
  • Published : 2024.06.30
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Abstract

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.

Keywords

Acknowledgement

본 논문은 인하대학교의 지원에 의해 연구되었습니다.

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