AE-XGBoost: A Novel Approach for Machine Tool Machining Size Prediction Combining XGBoost, AE and SHAP

Mu Gu, Shuimiao Kang, Zishuo Xu (), Lin Lin and Zhihui Zhang
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Mu Gu: College of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150006, China
Shuimiao Kang: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
Zishuo Xu: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
Lin Lin: College of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150006, China
Zhihui Zhang: Beijing Aerospace Intelligent Manufacturing Technology Development Co., Ltd., Beijing 100028, China

Mathematics, 2025, vol. 13, issue 5, 1-24

Abstract: To achieve intelligent manufacturing and improve the machining quality of machine tools, this paper proposes an interpretable machining size prediction model combining eXtreme Gradient Boosting (XGBoost), autoencoder (AE), and Shapley additive explanation (SHAP) analysis. In this study, XGBoost was used to establish an evaluation system for the actual machining size of computer numerical control (CNC) machine tools. The XGBoost model was combined with SHAP approximation to effectively capture local and global features in the data using autoencoders and transform the preprocessed data into more representative feature vectors. Grey correlation analysis (GRA) and principal component analysis (PCA) were used to reduce the dimensions of the original data features, and the synthetic minority overstimulation technique of the Gaussian noise regression (SMOGN) method was used to deal with the problem of data imbalance. Taking the actual size of the machine tool as the response parameter, based on the size parameters in the milling process of the CNC machine tool, the effectiveness of the model is verified. The experimental results show that the proposed AE-XGBoost model is superior to the traditional XGBoost method, and the prediction accuracy of the model is 7.11% higher than that of the traditional method. The subsequent SHAP analysis reveals the importance and interrelationship of features and provides a reliable decision support system for machine tool processing personnel, helping to improve processing quality and achieve intelligent manufacturing.

Keywords: machine tool processing; dimension prediction; XGBoost; autoencoder; SHAP (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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