Prediction of remaining fatigue life of metal specimens using data-driven method based on acoustic emission signal

Li, Jialin; Cao, Xuan; Chen, Renxiang; Zhao, Chengying; Li, Yuxiong; Huang, Xianzhen

doi:10.1016/j.apacoust.2023.109571

Cited by 4 publications

(1 citation statement)

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“…In particular, in the case of parts manufactured from thin plates, bending deformation due to repeated loading occurs relatively easily and has a negative effect on fatigue properties. For this reason, shot peening treatment is necessary to enhance fatigue resistance and extend the service life of metal parts [4][5][6]. There is still limited research on the effect of impact velocity on fatigue strength when both side of SAE1070 steel plate are shot peened simultaneously.…”

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Computational fatigue analysis of the Almen strip treated with double-sided shot peening and its experimental verification

Wang,

Kim

2024

Preprint

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The purpose is to research the effect of different shot peening parameters on the fatigue life of double-sided shot peening of SAE1070 steel. The method that in this paper, establishes a DE-FE (Discrete element-finite element) random multi-shot analysis mode is firstly used to simulate the processing of double-sided shot peening on SAE1070 steel using different shot peening parameters respectively, and then the shot peened parts are imported into fe-safe software for fatigue simulation test, and the same conditions are used to conduct double-sided shot peening test on SAE1070 specimen, and then the shot peened parts are subjected to fatigue life test. Finally, the simulation results are compared with the test results. Thus, the results of the simulation and experimental analyses are relatively consistent. In conclusion, shot peening treatment can effectively enhance the overall mechanical properties of SAE1070 alloy steel and increase its fatigue life. Significant differences in the shot peening effects at various intensities were observed, and optimal shot peening parameters can lead to the best shot peening results, resulting in relatively accurate predictions of fatigue outcomes.

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confidence: 99%