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

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

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...

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Bibliographic Details
Main Authors: Mu Gu, Shuimiao Kang, Zishuo Xu, Lin Lin, Zhihui Zhang
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Mathematics
Subjects:
machine tool processing
dimension prediction
XGBoost
autoencoder
SHAP
Online Access:https://www.mdpi.com/2227-7390/13/5/835
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