Fatigue strength model based on microstructures and damage mechanism of compacted graphite iron

Yu, Qin; Pang, J.C.; Zou, C.L.; Zhang, M.X.; Li, S.X.; Zhang, Z.F.

doi:10.1016/j.msea.2018.03.110

Cited by 29 publications

(17 citation statements)

References 24 publications

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“…Therefore, the FGHAZ showed low fatigue life ( Figure 5A) because fatigue life is strongly dependent on strength. 27,28 Although the strength of the ICHAZ was lower than that of the CGHAZ, the ICHAZ had the highest fatigue life. The change of mechanical properties in sub-HAZs of the welded joint could be ascribed to differences of microstructure (Figures 7-10) due to the effects of peak temperature on phase transformation during the welding heat cycle.…”

Section: Discussionmentioning

confidence: 99%

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Research on the fatigue properties of sub‐heat‐affected zones in X80 pipe

Qiao

Wang

et al. 2020

Fatigue Fract Eng Mat Struct

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Pipeline transmission is an essential means of oil and gas transportation, but its safety is threatened by fatigue fracture that generally occurs in the heat-affected zone (HAZ) adjacent to welded joints of the pipeline. Therefore, it is important to determine the fatigue properties of HAZ. In this work, the microstructure and mechanical properties in the HAZ of a X80 pipe were accurately simulated by thermal simulation. The fatigue life and crack growth rate of typical sub-HAZ were tested. The results showed that fine grain HAZ exhibited the lowest strength and fatigue life. In contrast, coarse grain HAZ had the highest strength, but its fatigue life was lower than that of the intercritical HAZ. The microstructure of each sub-HAZ and the effect of this structure on fatigue properties were analysed and discussed in detail. The results provide insight into the effects of microstructure and mechanical properties on the service safety of pipeline transportation.

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Section: Discussionmentioning

confidence: 99%

“…The fatigue resistance of steel is related to its strength. 27,28 Figures 1A and 2A show the different hardness levels in the sub-HAZs, which correlate to different strengths that may affect overall fatigue life. The tensile properties of the three sub-HAZs of X80 steel were next measured.…”

Section: Tensile and Fatigue Propertiesmentioning

confidence: 99%

Research on the fatigue properties of sub‐heat‐affected zones in X80 pipe

Qiao

Wang

et al. 2020

Fatigue Fract Eng Mat Struct

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“…Therefore, the region showed the low fatigue life (Fig. 5a ) because the fatigue life strongly depended on the strength 27,28 . Although the strength of Region 3 (ICHAZ) was lower than that of Region 1 (CGHAZ), the former had the highest fatigue life.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, all factors that affect the fatigue crack initiation and propagation will affect the fatigue life. Considerable research reports that the fatigue resistance of the metals relates to the strength 27,28 . The fatigue cracks generally generate on the surface of specimens.…”

Section: Discussionmentioning

confidence: 99%

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Research on the fatigue properties of sub-HAZs in the X80 pipe

Xu¹,

Qiao²,

Wang³

et al.

Authorea

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Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning

Teng

Pang

Liu

et al. 2023

Acta Metall. Sin. (Engl. Lett.)

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Conventional fatigue tests on complex components are difficult to sample, time-consuming and expensive. To avoid such problems, several popular machine learning (ML) algorithms were used and compared to predict fatigue life of gray cast iron (GCI) with the complex microstructures. The feature analysis shows that the fatigue life of GCI is mainly influenced by the external environment such as the stress amplitude, and the internal microstructure parameters such as the percentage of graphite, graphite length, stress concentration factor at the graphite tip, matrix microhardness and Brinell hardness. For simplicity, collected datasets with some of the above features were used to train ML models including back-propagation neural network (BPNN), random forest (RF) and eXtreme gradient boosting (XGBoost). The comparison results suggest that the three models could predict the fatigue lives of GCI, while the implemented RF algorithm is the best performing model. Moreover, the S-N curves fitted by the Basquin relation in the predicted data have a mean relative error of 15% compared to the measured data. The results have demonstrated the advantages of ML, which provides a generic way to predict the fatigue life of GCI for reducing time and cost. KeywordsGray cast iron • Microstructure feature • Machine learning • High-cycle fatigue life * Jianchao Pang

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Fatigue strength model based on microstructures and damage mechanism of compacted graphite iron

Cited by 29 publications

References 24 publications

Research on the fatigue properties of sub‐heat‐affected zones in X80 pipe

Research on the fatigue properties of sub‐heat‐affected zones in X80 pipe

Research on the fatigue properties of sub-HAZs in the X80 pipe

Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning

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