Robust tool wear modeling based on differential wear signature analysis
Journal
International Journal of Advanced Manufacturing Technology
ISSN
02683768
Date Issued
2025
Author(s)
Abstract
Accurate prediction of tool wear is essential for ensuring machining stability, product quality, and process efficiency in high-speed milling. However, traditional time-based wear models require extensive full-life testing under each parameter set, making them impractical for modern multi-condition machining. This study develops an enhanced and generalizable tool-wear modeling framework based on Differential Wear Signature (DWS) analysis, which formulates the wear rate as a function of cutting parameters and instantaneous wear state. By leveraging the tool runout effect, the model captures multiple effective feed rates within a single experiment, thereby improving data efficiency and reducing experimental effort. The enhanced DWS framework incorporates a broader range of cutting parameters, integrates Remaining Useful Life (RUL) estimation, and is benchmarked against conventional offline models including empirical regression, GPR, and ANN-based approaches. Experimental validation confirms that the proposed model accurately reconstructs wear trajectories and predicts RUL under both single- and multi-step cutting scenarios, achieving a root-mean-square error of 0.008 mm, compared to 0.028 mm in the prior DWS model. The results demonstrate that the proposed framework provides a robust, data-efficient, and transferable solution for offline tool-life prediction and process planning in CNC milling.
Subjects
CNC milling process
Differential wear signature analysis
Remaining useful life estimation
Tool wear modeling
Tool wear rate
Publisher
Springer Science and Business Media Deutschland GmbH
Type
journal article