Prediction of the TiAlN-Coated Tool Wear Evolution in Cutting Inconel 718 Based on Nonlinear Wear Theory with the Thermal-Mechanical Coupling Effect

In this study, a three-dimensional thermo-mechanical coupled finite element framework for orthogonal cutting with coated tools was established in ABAQUS/Explicit. The framework incorporated the Johnson-Cook constitutive model and a temperature-dependent modified Coulomb friction model. The transient mechanical and thermal responses of uncoated WC-Co, TiN-coated, and TiAlN-coated tools were evaluated at cutting speeds of 50, 100, and 150 m/min. Long-term wear evolution was then predicted by integrating the Usui wear equation with three-stage nonlinear wear theory. Prediction errors for cutting force and cutting temperature remained within 10% across the tested cutting speeds. The TiAlN coating reduced friction and restricted heat conduction into the tool substrate under high strain-rate conditions. The near-field tool temperature decreased by 4.0% to 9.5%, with an 8.2% reduction at 150 m/min. Compared with uncoated WC-Co, TiAlN reduced the simulated main cutting force by 6.0%, 5.6%, and 4.4% at 50, 100, and 150 m/min, respectively. Cutting experiments yielded force reductions of 5.0% to 7.1%. After 30 min of dry cutting at 150 m/min, the model predicted flank wear depths of 0.398 mm for uncoated WC-Co, 0.261 mm for TiN, and 0.235 mm for TiAlN. Relative errors against the measured flank wear depths ranged from 3.4% to 4.7%. The small error between the predicted wear evolution and the experimental data demonstrated that the established model achieved a high level of predictive accuracy.

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Publication Details

Journal
Coatings
Published
2026-10-30
DOI
https://doi.org/10.3390/coatings16111289
Primary Topic
Advanced machining processes and optimization
Type
article
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article

Prediction of the TiAlN-Coated Tool Wear Evolution in Cutting Inconel 718 Based on Nonlinear Wear Theory with the Thermal-Mechanical Coupling Effect

Aijun Tang, Zhonglei Wang, Guangchao Hao, Yongquan Jing et al.
3 links
Coatings
Advanced machining processes and optimization
article

Prediction of the TiAlN-Coated Tool Wear Evolution in Cutting Inconel 718 Based on Nonlinear Wear Theory with the Thermal-Mechanical Coupling Effect

Aijun Tang, Zhonglei Wang, Guangchao Hao, Yongquan Jing, Lei Huang, Laixiao Lu, Yanfa Zhao, Rui Wang, Shuo Zhang
article en

Abstract

In this study, a three-dimensional thermo-mechanical coupled finite element framework for orthogonal cutting with coated tools was established in ABAQUS/Explicit. The framework incorporated the Johnson-Cook constitutive model and a temperature-dependent modified Coulomb friction model. The transient mechanical and thermal responses of uncoated WC-Co, TiN-coated, and TiAlN-coated tools were evaluated at cutting speeds of 50, 100, and 150 m/min. Long-term wear evolution was then predicted by integrating the Usui wear equation with three-stage nonlinear wear theory. Prediction errors for cutting force and cutting temperature remained within 10% across the tested cutting speeds. The TiAlN coating reduced friction and restricted heat conduction into the tool substrate under high strain-rate conditions. The near-field tool temperature decreased by 4.0% to 9.5%, with an 8.2% reduction at 150 m/min. Compared with uncoated WC-Co, TiAlN reduced the simulated main cutting force by 6.0%, 5.6%, and 4.4% at 50, 100, and 150 m/min, respectively. Cutting experiments yielded force reductions of 5.0% to 7.1%. After 30 min of dry cutting at 150 m/min, the model predicted flank wear depths of 0.398 mm for uncoated WC-Co, 0.261 mm for TiN, and 0.235 mm for TiAlN. Relative errors against the measured flank wear depths ranged from 3.4% to 4.7%. The small error between the predicted wear evolution and the experimental data demonstrated that the established model achieved a high level of predictive accuracy.

CoatingsVol. 16(11)
University of Illinois Urbana-Champaign (US), Shandong Jianzhu University (CN), Zhejiang University (CN)
Natural Science Foundation of Shandong Province
Openalex Percentile: Top 23%
Advanced machining processes and optimization
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