Some studies on P91 steel and their weldments

Pandey, Chandan; Mahapatra, Manas Mohan; Kumar, Pradeep; Saini, Nitin

doi:10.1016/j.jallcom.2018.01.120

Cited by 220 publications

(74 citation statements)

References 160 publications

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“…For the martensitic steel welded joint used in thermal power unit, the service life mainly depended on its creep performance and type IV crack in HAZ was a common creep fracture mode which led to premature failure of the welded joint [11][12][13][14][15][16][17][18][19][20][21]. As they moved away from fusion line, the sub-zones of HAZ of conventional martensitic heat resistant steel, i.e., P91, were classified into coarse-grained HAZ (CGHAZ, also known as overheated sub-zone of HAZ), fine grained HAZ (FGHAZ, also known as normalized sub-zone of HAZ), inter-critical HAZ (ICHAZ) and over-tempered base metal (OTBM), which were distinguished with the different peak temperature and prior austenite grain size [22], as shown in Figure 1 [23]. The formation of sub-zones of HAZ was closely related to the austenitization process during welding.…”

Section: Introductionmentioning

confidence: 99%

A Review of Austenite Memory Effect in HAZ of B Containing 9% Cr Martensitic Heat Resistant Steel

Cai

et al. 2019

Metals

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During the welding process of B containing 9% Cr martensitic heat resistant steel (9Cr-B steel), austenite memory effect (referred to that the prior austenite grains in the heat affected zone (HAZ) after welding inherit the shape and size of prior austenite grains before welding) occurs in its normalized sub-zone of HAZ and the grain refinement is suppressed, which can effectively prevent type IV crack, and improve the service life of the welded joint at high temperatures. In the present article, α/γ reverse transformation behavior in the normalized sub-zone of 9Cr-B steel HAZ is reviewed. Austenite memory effect of 9Cr-B steel is derived from B addition. The main mechanisms of austenite memory effect during α/γ reverse transformation are discussed. Various models of boron causing austenite memory effect are discussed in detail. Matrix microstructure also plays an important role in austenite memory effect. Effects of heating rate, peak temperature, and holding time at peak temperature on austenite memory effect are also discussed.

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Section: Introductionmentioning

confidence: 99%

A Review of Austenite Memory Effect in HAZ of B Containing 9% Cr Martensitic Heat Resistant Steel

Cai

et al. 2019

Metals

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“…Hosoi et al found a relatively large loss of toughness of 9Cr‐2Mo heat resistant steel after aged for 1000 hours at 873 K. The embrittlement of 9Cr‐2Mo heat resistant steel was considered to be caused by the precipitation of M 6 C, M 23 C 6 ((Cr, Fe) 23 C 6 ) particle, and the Laves phase. The damage in the toughness was also observed in 9Cr‐1Mo series heat resistant steel . Miyamura et al considered that Laves phase, which was formed only after long‐term aging around 873 K, affected toughness deterioration of 9Cr‐1Mo‐V steel.…”

Section: Introductionmentioning

confidence: 99%

“…Miyamura et al considered that Laves phase, which was formed only after long‐term aging around 873 K, affected toughness deterioration of 9Cr‐1Mo‐V steel. Pandey et al indicated that Laves phase reduced the toughness of P91 steel. In 9Cr‐0.5Mo‐1.8WVNbN steel, Laves phase was considered as the main factor to embrittlement after aging .…”

Section: Introductionmentioning

confidence: 99%

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Effect of Laves phase on the toughness of P92 weld metals

Wang

Liu

et al. 2018

Fatigue Fract Eng Mat Struct

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In this investigation, Laves phase was found to gradually precipitate from the tempered martensitic matrix of the two P92 weld metals (WMs) during the aging treatment. The particle size of Laves phase in the two WMs was ranged from 0.18 to 0.24 μm and was larger than the M23C6 particle size (ranged from 0.09 to 0.13 μm). The dislocation density of the two WMs was stable during aging. The hardness of the two WMs was stable during aging treatment, as the loss of solid solution strengthening was continuously compensated by the precipitation strengthening of Laves phase. The impact toughness of the two WMs declined gradually as the extending of aging time. The crack initiation zone and shear lip zone of the fracture surface were ductile fracture mode which consumed more fracture energy during impacting. The generation of Laves phase narrowed the crack initiation zone and shear lip zone and contributed to the decline of ductile fracture area percentage of aged WMs. The smaller ductile fracture area percentage contributed to the toughness reduction of aged WMs.

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“…It is required to realize continuous operation of propulsion →heating →bending →cooling →forming while the induction heating device fulfills the local heating of the pipe when the pipe bending machine is working [3]. At present, many researchers have conducted a lot of studies on the bending process of different materials, the mechanical properties, and the microstructure of the bent pipe [4][5][6][7]. However, the influence of various parameters on the heating effect of the induction heating forming process for large-diameter pipe is not yet clear.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization and Prediction Model of Induction Heating for Large-Diameter Pipe

Fang

Wang

et al. 2018

Mathematical Problems in Engineering

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The parameters of induction heating of large-diameter pipes have a direct effect on the final processing quality of the elbow, and the complexity of multifield coupling of magnetothermal force in induction heating can make it impossible to quantitatively optimize the design parameters of the induction heating device. In this paper, X80 pipeline steel induction heating is taken as the research object, and a corresponding numerical model is established. The influence of induction heating process parameters on the heating temperature of pipeline steel under the skin effect is determined. First, the influence of process parameters on the heating effect of pipeline steel is quantified by orthogonal test. Then, taking the optimum temperature difference between the inner and outer wall of X80 pipeline steel during the induction heating process as a target, the optimal process parameter set of the pipe induction heating is determined by using neural network genetic algorithm. Finally, comparing the relevant test criteria of the regression equation, the optimum mathematical prediction model of the outer wall temperature of the pipe induction heating process is obtained, which provides a theoretical basis for optimization of the process parameters of the pipe-based induction heating device.

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Some studies on P91 steel and their weldments

Cited by 220 publications

References 160 publications

A Review of Austenite Memory Effect in HAZ of B Containing 9% Cr Martensitic Heat Resistant Steel

A Review of Austenite Memory Effect in HAZ of B Containing 9% Cr Martensitic Heat Resistant Steel

Effect of Laves phase on the toughness of P92 weld metals

Parameter Optimization and Prediction Model of Induction Heating for Large-Diameter Pipe

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