Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

Thomas Heitz, Ning He (), Addi Ait-Mlouk (), Daniel Bachrathy (), Ni Chen (), Guolong Zhao () and Liang Li ()
Additional contact information
Thomas Heitz: Nanjing University of Aeronautics and Astronautics
Ning He: Nanjing University of Aeronautics and Astronautics
Addi Ait-Mlouk: University of Skövde
Daniel Bachrathy: Budapest University of Technology and Economics
Ni Chen: Nanjing University of Aeronautics and Astronautics
Guolong Zhao: Nanjing University of Aeronautics and Astronautics
Liang Li: Nanjing University of Aeronautics and Astronautics

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 15, 285-301

Abstract: Abstract Accurate prediction of cutting forces is critical in milling operations, with implications for cost reduction and improved manufacturing efficiency. While traditional mechanistic models provide high accuracy, their reliance on extensive milling data for force coefficient fitting poses challenges. The eXtreme Gradient Boosting algorithm offers a potential solution with reduced data requirements, yet the optimal utilization of eXtreme Gradient Boosting remains unexplored. This study investigates its effectiveness in predicting cutting forces during down-milling of Al2024. A novel framework is proposed optimizing its precision, efficiency, and user-friendliness. The model training incorporates the mechanistic force model in both time and frequency domains as new features. Through rigorous experimentation, various aspects of the eXtreme Gradient Boosting configuration are explored, including identifying the optimal number of periods for the training dataset, determining the best normalization and scaling technique, and assessing the hyperparameters’ impact on model performance in terms of accuracy and computational time. The results show the remarkable effectiveness of the eXtreme Gradient Boosting model with an average normalized root mean square error of 14.7%, surpassing the 21.9% obtained by the mechanistic force model. Additionally, the machine learning model could capture the runout effect. These findings enable optimized milling operations regarding cost, accuracy and computation time.

Keywords: Cutting force prediction; Machine learning; Milling; Optimization; XGBoost (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02243-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02243-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02243-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().