The plastic strain energy density-based fatigue life prediction method incorporating geometric-mechanical-microstructural characteristic parameters of machined surface integrity

Surface integrity critically influences the fatigue life of load-bearing components, directly determining their safe service performance. To investigate the quantitative relationship between surface integrity and fatigue life, a representative volume element (RVE) model incorporating geometric-mechanical-microstructural surface integrity characteristics is established using crystal plasticity finite element method (CPFEM). Based on this model, the plastic strain energy density of machined surfaces is computed for fatigue life prediction. A surface plastic strain energy density-based (SPSED) fatigue life prediction method was proposed and validated by tensile fatigue and torsional fatigue experiment. For tensile fatigue, the prediction error was 51.46 % using the average plastic strain energy density (APSED) method, compared to 21.84 % when employing the SPSED approach. For torsional fatigue, the SPSED method achieved a prediction error of 18.64 %. Fractographic analysis of torsional fatigue specimens combined with stress intensity factor calculations revealed the fatigue process and surface integrity effects. Results indicate that specimens with anti-fatigue surface integrity exhibit slower circumferential crack propagation and enhance Mode III cracking, which lead to shear- dominated smooth surfaces in the instantaneous fracture zone. Conversely, specimens with shorter fatigue lives demonstrate greater influence of normal stresses, hence showing cleavage steps and dimples in the instantaneous fracture zone.

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Publication Details

Journal
Results in Engineering
Published
2025-09-18
DOI
https://doi.org/10.1016/j.rineng.2025.107299
Primary Topic
Advanced machining processes and optimization
Type
article
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article

The plastic strain energy density-based fatigue life prediction method incorporating geometric-mechanical-microstructural characteristic parameters of machined surface integrity

Xiaochao Liu, Shuyao Liu, Xibin Wang, Zhibing Liu et al.
Results in Engineering
Advanced machining processes and optimization
article

The plastic strain energy density-based fatigue life prediction method incorporating geometric-mechanical-microstructural characteristic parameters of machined surface integrity

Xiaochao Liu, Shuyao Liu, Xibin Wang, Zhibing Liu, Yong Wang, Hongtao Chen, Pai Wang
article en

Abstract

Surface integrity critically influences the fatigue life of load-bearing components, directly determining their safe service performance. To investigate the quantitative relationship between surface integrity and fatigue life, a representative volume element (RVE) model incorporating geometric-mechanical-microstructural surface integrity characteristics is established using crystal plasticity finite element method (CPFEM). Based on this model, the plastic strain energy density of machined surfaces is computed for fatigue life prediction. A surface plastic strain energy density-based (SPSED) fatigue life prediction method was proposed and validated by tensile fatigue and torsional fatigue experiment. For tensile fatigue, the prediction error was 51.46 % using the average plastic strain energy density (APSED) method, compared to 21.84 % when employing the SPSED approach. For torsional fatigue, the SPSED method achieved a prediction error of 18.64 %. Fractographic analysis of torsional fatigue specimens combined with stress intensity factor calculations revealed the fatigue process and surface integrity effects. Results indicate that specimens with anti-fatigue surface integrity exhibit slower circumferential crack propagation and enhance Mode III cracking, which lead to shear- dominated smooth surfaces in the instantaneous fracture zone. Conversely, specimens with shorter fatigue lives demonstrate greater influence of normal stresses, hence showing cleavage steps and dimples in the instantaneous fracture zone.

Results in EngineeringVol. 28
Openalex Percentile: Top 74%
Advanced machining processes and optimization
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