Notch fatigue life prediction of micro-shot peened 25CrMo4 alloy steel: A comparison between fracture mechanics and machine learning methods

Li, Hang; Zhang, Jiwang; Hu, Liukui; Su, Kaixin

doi:10.1016/j.engfracmech.2022.108992

Cited by 14 publications

(5 citation statements)

References 54 publications

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“…Therefore, the fatigue life prediction of the short crack growth phase is extremely complex. To address this issue, the Equivalent Initial Flaw Size (EIFS) method has been applied to predict the short crack growth life (Li et al 2023).…”

Section: Modeling Of Crack Growth Lifementioning

confidence: 99%

“…However, the applicability of the NASGRO equation can be extended to the short crack stage by employing the EIFS method. The key idea of the EIFS method is to equate the life of the short crack growth stage to that of part of the long crack growth stage, thus avoiding the need for life prediction during the short crack growth phase (Li et al 2023). As illustrated in the schematic of the EIFS method (Fig.…”

Section: Modeling Of Crack Growth Lifementioning

confidence: 99%

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The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

Zhang,

Hu,

et al. 2024

Preprint

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Three-dimensional characterization of internal defects in Ti-6Al-4V alloy fabricated by Laser Engineered Net Shaping (LENS) was conducted by utilizing synchrotron X-ray imaging technology. Subsequently, the statistical analysis of defect size, quantity, and morphology characteristics was performed. Additionally, high cycle fatigue tests were conducted to analyze the high cycle fatigue performance of LENS Ti-6Al-4V alloy and elucidate the causes of its anisotropic behavior. Furthermore, based on the multi-stage crack growth model, the high cycle fatigue life of LENS Ti-6Al-4V alloy was predicted. The results showed that the quantity and size of internal defects were small, with defects predominantly spherical pores and no lack of fusion defects detected. Longitudinal specimens exhibited significantly higher fatigue life at high stress levels compared to transverse specimens. The anisotropic behavior of high cycle fatigue performance of LENS Ti-6Al-4V alloy at high stress levels was mainly attributed to the anisotropic distribution of its microstructure. As stress levels decreased, the fatigue life of both types of specimens approached each other, with fatigue strengths of 650 MPa and 656 MPa at 2×106 cycles for longitudinal and transverse specimens respectively, showing minimal difference. In addition, the predictions from the multi-stage crack growth model aligned well with experimental results, effectively predicting the high cycle fatigue life of LENS Ti-6Al-4V alloy.

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Section: Modeling Of Crack Growth Lifementioning

confidence: 99%

Section: Modeling Of Crack Growth Lifementioning

confidence: 99%

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

Zhang,

Hu,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Their investigation revealed that the prediction results of the GA-BP-ANN algorithm (Genetic Algorithm Optimized Backpropagation Artificial Neural Network algorithm) closely aligned with FEM simulation results in terms of SP-induced residual stresses, equivalent plastic strains, grain refinement, and surface roughness. Li et al [16] predicted the fatigue life of 25CrMo4 alloy steel notches by micro-shot peening and used a BP neural network to predict the fatigue results, which were within ± 2 error. However, the projectiles in the shot peening model used by M. Daoud and Haiquan Huang were not randomly generated, and they simulated the shot peening process by using the correlation function or by fixing the position and height of the projectile in advance, which is a big gap with the actual shot peening process.…”

Section: Introductionmentioning

confidence: 99%

Optimization of parameters for the best shot peening effect based on surface response and neural network model

Wang,

Kim

2024

Mater. Res. Express

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To optimize the peening effect of different shot peening process parameters on metal surfaces, the mapping relationship between different shot peening process parameters and metal surface integrity was obtained. In this paper, ABAQUS software was used to establish a DE-FE (Discrete element-Finite element) random multi-shot analysis model to simulate shot peening, then optimize the shot peening process parameters based on the surface response method(RSM), and finally validate it through experiments and BP(back propagation) neural network model. The result shows that when the shot velocity is 70 m/s, the impact angle of shot is 61.45°, and the shot diameter is 0.78 mm, the shot peening effect is the best, the surface roughness value is reduced by 101.84%, and the arc height value is increased by 54.66%; the error between the predicted results of BP neural network and the results of numerical analysis is less than 8%. Therefore, the optimized process parameters significantly improve the shot peening effect, but also shows that the BP neural network prediction model can more accurately predict the mapping relationship between the input parameters of shot velocity, shot diameter, and impact angle of shot and the output parameters of roughness value and arc height value.

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“…Parameters influencing notch fatigue life are divided into three categories: (i) material properties, (ii) geometrical features, and (iii) loading conditions. [29] proposed a ML method to predict high cycle fatigue 25CrMo4 alloy steel containing artificial notches, considering the influence of surface condition, loading level and notch size. Considering additive manufacturing processes, [30] proposed a ML approach based on a continuum damage mechanics model to assess the fatigue life of V-notched bars made of 300M-AerMet100 steel.…”

Section: Introductionmentioning

confidence: 99%

Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation

Farhadi,

Tatullo,

Ferrian

2024

Preprint

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Efficient prediction of fatigue life in structural components is crucial for ensur- ing their integrity and reliability, especially considering the dominant occurrence of fatigue failure in metallic structures within the industrial sectors. Conven- tional fatigue assessment methods, although theoretically established, are often time-consuming and exhibit limitations due to the intricate nature of the fatigue mechanism. Machine learning models have demonstrated significant potential for enhancing the efficiency of predictions in fatigue life. This research explores the effectiveness of ensemble learning models—boosting, stacking, and bag- ging—compared to linear regression and K-Nearest Neighbors as benchmarks. Fatigue life prediction is conducted across different notched scenarios using Incre- mental Energy Release Rate (IERR) measures in addition to the more standard stress/strain field measures. To assess the performance of the proposed models, a comprehensive set of evaluation metrics was performed, including mean square error (MSE), mean squared logarithmic error (MSLE), symmetric mean abso- lute percentage (SMAPE), and Tweedie score. The findings reveal that ensemble learning models, particularly the ensemble neural networks, stands out as a superior approach for fatigue life cycle assessment compared to other methods. Moreover, the integration of IERR in predicting fatigue life for notched-shape components indicates a promising approach for enhancing the reliability and efficiency of fatigue life predictions in real-world industrial applications.

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Notch fatigue life prediction of micro-shot peened 25CrMo4 alloy steel: A comparison between fracture mechanics and machine learning methods

Cited by 14 publications

References 54 publications

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

Optimization of parameters for the best shot peening effect based on surface response and neural network model

Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation

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