Larson–Miller Constant of Heat-Resistant Steel

Tamura, Manabu; Abe, Fujio; Shiba, K.; Sakasegawa, Hideo; Tanigawa, Hiroyasu

doi:10.1007/s11661-013-1631-0

Cited by 54 publications

(37 citation statements)

References 28 publications

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“…Assuming a thermal-activating process for dislocation glide and subsequent climb to overcome obstacles, the time to rupture is expressed as [16] t r ¼ t r0 exp Q À rV ð Þ =RT ½ ; ½1…”

Section: Analysis Methodsmentioning

confidence: 99%

“…[1] and [2] are explained in previous work. [16] However, Q is usually called simply the activation energy or the activation energy for creep. Here, we call Q the activation energy, for simplicity.…”

Section: Analysis Methodsmentioning

confidence: 99%

“…Even if the temperature-compensated time is used, a linear relationship between log r and the temperature-compensated time hardly holds. Therefore, this study employs the cubic equation: [16] P ¼ T log t r þ C ð Þ¼a 0 þ a 1 log r þ a 2 log 2 r þ a 3 log 3 r; ½4…”

Section: Analysis Methodsmentioning

confidence: 99%

“…III shown in Figure 8 can be recognized within 10,000 hours like the variation in time to 0.5 pct strain shown in Figure 7, and therefore, variation in the activation energy can be used as a sign for the long-term drop in strength. The Larson-Miller constant, C, is strongly correlated with Q [16] , and the description concerning C is thus omitted. Figure 9 shows the correlation between the activation volume and creep time.…”

Section: A Variations In Q and V During Creep Deformationmentioning

confidence: 99%

“…Under these test conditions, the considerably high activation energy ranges from about 650 to 1000 kJ/mol as shown in Figure 8, although the activation energy of several carbon steels, low-alloy steels, and austenitic stainless steels are approximately 400 kJ/mol. [16,17] Extremely high values of the activation energy shown in Figure 8 are considered to denote increasing resistance to moving dislocations.…”

Section: A Creep Mechanisms For Grs I-iii¢mentioning

confidence: 99%

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Method of Estimating the Long-term Rupture Strength of 11Cr-2W-0.4Mo-1Cu-Nb-V Steel

Tamura

2015

Metall Mater Trans A

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Long-term rupture data of 11Cr-2W-0.4Mo-1Cu-Nb-V steel were analyzed using an exponential equation for stress regarding time to rupture as a thermal activation process. The fitness was compared with the usually employed method assuming power-law creep. In the exponential method, rupture data are classified into several groups according to the thermal activation process; the activation energy, Q; the activation volume, V; then, the Larson-Miller constant, C, values are calculated, and a regression equation is obtained for each data group. The fitness level of the equation was satisfactorily high for each group. The values of Q, V, and C were unusually small for a data group where an unexpected drop in rupture strength was observed. The critical issue is how to comprehend signs of degradation within the short term. We can observe several signs at a creep time of approximately one-tenth of the times of the degradation events. The small values of Q and V indicate that completely softened regions form and creep locally, which is consistent with previous observations. From both metallurgical considerations and the variations of Q and V, it is suggested that the rate of the unexpected drop in strength is mitigated after further long-term creep.

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“…Assuming a thermal-activating process for dislocation glide and subsequent climb to overcome obstacles, the time to rupture is expressed as [16] t r ¼ t r0 exp Q À rV ð Þ =RT ½ ; ½1…”

Section: Analysis Methodsmentioning

confidence: 99%

Section: Analysis Methodsmentioning

confidence: 99%

Section: Analysis Methodsmentioning

confidence: 99%

Section: A Variations In Q and V During Creep Deformationmentioning

confidence: 99%

Section: A Creep Mechanisms For Grs I-iii¢mentioning

confidence: 99%

See 3 more Smart Citations

Method of Estimating the Long-term Rupture Strength of 11Cr-2W-0.4Mo-1Cu-Nb-V Steel

Tamura

2015

Metall Mater Trans A

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Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks

Zong,

Li,

Zhou

et al. 2024

Materials Genome Engineering Advances

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Prediction of creep rupture life of high‐temperature titanium alloys is crucial for their practical applications. The efficient representations (features) of the information encoded in the data are essential to achieve an accurate prediction model. Here, using convolutional neural networks (CNN) enhanced features, we obtain largely improved prediction models for creep rupture life. Comparison of CNN‐based features with the original features in describing different samples reveals that the former, by assigning more individualized labels, outperforms the latter and underpins improved prediction models. This work suggests that beyond images, CNN is also suitable for numerical data to obtain enhanced features and surrogate models.

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Mechanical Behavior of Solids

Perez

2024

Materials Science: Theory and Engineering

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Larson–Miller Constant of Heat-Resistant Steel

Cited by 54 publications

References 28 publications

Method of Estimating the Long-term Rupture Strength of 11Cr-2W-0.4Mo-1Cu-Nb-V Steel

Method of Estimating the Long-term Rupture Strength of 11Cr-2W-0.4Mo-1Cu-Nb-V Steel

Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks

Mechanical Behavior of Solids

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