Wear behavior simulation and prediction of TiAlSiN-coated tools for high-speed milling of titanium alloys

Coated tools exhibit exceptional wear resistance under high-speed machining conditions by virtue of their significantly enhanced hardness, reduced friction coefficient, and low thermal conductivity, thus finding extensive applications in aerospace, energyy equipment, and other high-end manufacturing sectors. Especially under extreme interfacial conditions involving high speeds and elevated temperatures, the tool and workpiece are subjected to more severe mechanical and thermal loads at the interface, leading to a sharp increase in the complexity of the contact region. This renders tool wear a highly time-varying and nonlinear characteristic, making accurate wear prediction rather challenging. To address this issue, this paper proposes and establishes a tool wear prediction method based on the integration of wear modeling and finite element simulation data iteration. By integrating the classical Usui wear rate model with the influence of tool geometric features, a wear rate model for flank wear width was developed. To further simulate the wear process, stress and temperature distributions in the stable contact region between the tool and workpiece were extracted from cutting simulations. Based on the established wear rate model, the flank wear amount was calculated, and the tool geometric profile was iteratively updated accordingly. The accumulated wear was fed back into subsequent cutting simulations, establishing a closed-loop analysis of cutting simulation, wear calculation, and geometry updating. This enabled a mechanistic analysis of the evolution of tool morphology over time. Furthermore, the simulated tool wear results were compared with experimental data, showing that the prediction error of this model could be controlled within 17%.

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Publication Details

Journal
Journal of Materials Research and Technology
Published
2026-04-15
DOI
https://doi.org/10.1016/j.jmrt.2026.04.077
Primary Topic
Advanced machining processes and optimization
Type
article
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article

Wear behavior simulation and prediction of TiAlSiN-coated tools for high-speed milling of titanium alloys

Steven Y. Liang, Zunyan Ma, Haotuo Liu, Zhijia Yu et al.
Journal of Materials Research and Technology
Advanced machining processes and optimization
article

Wear behavior simulation and prediction of TiAlSiN-coated tools for high-speed milling of titanium alloys

Steven Y. Liang, Zunyan Ma, Haotuo Liu, Zhijia Yu, Desheng Hu, Guanhui Ren, Caixu Yue, Zhipeng Jiang
article en

Abstract

Coated tools exhibit exceptional wear resistance under high-speed machining conditions by virtue of their significantly enhanced hardness, reduced friction coefficient, and low thermal conductivity, thus finding extensive applications in aerospace, energyy equipment, and other high-end manufacturing sectors. Especially under extreme interfacial conditions involving high speeds and elevated temperatures, the tool and workpiece are subjected to more severe mechanical and thermal loads at the interface, leading to a sharp increase in the complexity of the contact region. This renders tool wear a highly time-varying and nonlinear characteristic, making accurate wear prediction rather challenging. To address this issue, this paper proposes and establishes a tool wear prediction method based on the integration of wear modeling and finite element simulation data iteration. By integrating the classical Usui wear rate model with the influence of tool geometric features, a wear rate model for flank wear width was developed. To further simulate the wear process, stress and temperature distributions in the stable contact region between the tool and workpiece were extracted from cutting simulations. Based on the established wear rate model, the flank wear amount was calculated, and the tool geometric profile was iteratively updated accordingly. The accumulated wear was fed back into subsequent cutting simulations, establishing a closed-loop analysis of cutting simulation, wear calculation, and geometry updating. This enabled a mechanistic analysis of the evolution of tool morphology over time. Furthermore, the simulated tool wear results were compared with experimental data, showing that the prediction error of this model could be controlled within 17%.

Journal of Materials Research and TechnologyVol. 42
Harbin University of Science and Technology (CN), Georgia Institute of Technology (US), MD Precision (Canada) (CA)
Openalex Percentile: Top 18%
Advanced machining processes and optimization
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