Failure analysis of the TP347H austenitic stainless steel tube of boiler reheater in a coal-fired power plant

Zheng, Zhunbei; Zhang, Jun; Sun, Xingxin; Zhang, Jianwei; He, Hua; Lu, Xuegang

doi:10.1016/j.engfailanal.2020.105154

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…A typical pipe wall structure could have hundreds to thousands of welds. Free-of-defect in the welds is very important to the safe operation of the power station [2]. As a result, these welded wall structures require intensive non-destructive evaluation (NDE) to ensure structural integrity.…”

Section: Introductionmentioning

confidence: 99%

Binary classification of welding defect based on deep learning

Wang

Zhang

et al. 2022

Science and Technology of Welding and Joining

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The current study investigated the effects of data augmentation, convolutional layers depth, and learning rate on inspection accuracy of a deep learning model which detects welding defects. The experimental results suggested that simultaneous use of these two methods improved the model's performance more than the sum of using each of the two methods alone. As the number of convolutional layers increases above 10, the network cannot extract defect features effectively, and the model's accuracy will decrease. Learning rate could significantly influence convergence rate and accuracy, and using an initial learning rate of 1e-4 and decaying it at epoch 250 could reduce the loss function and increase model accuracy.

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Section: Introductionmentioning

confidence: 99%

Binary classification of welding defect based on deep learning

Wang

Zhang

et al. 2022

Science and Technology of Welding and Joining

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“…Austenitic stainless steels are widely used in superheater and reheater tubes in supercritical and ultrasupercritical units due to its high cost performance, it has good steam oxidation resistance and high creep strength [1]. However, after long-term service, austenitic stainless steels became vulnerable attributing to microstructure degradation [2][3][4], including precipitation and coarsening of chromium carbides, depletion of chromium along grain boundaries, coarsening of strengthening second phases, and formation of deformation-induced martensite. Intergranular attack is one of the main failure modes for austenitic stainless steels faced with the high-temperature water or molten salt during the long-term exposure in thermal power plants [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Reducing the carbon content in the material can effectively inhibit the precipitation of grain boundary carbides, but it will also decrease the strength of the material [15]. Nb-stabilised TP347H can effectively prevent sensitisation related intergranular corrosion failures due to the stronger affinity between Nb and C than that between Cr and C [2], but it does not mean the TP347H is immune to intergranular attacking. The segregation of impurities (S and P) to grain boundaries contribute to intergranular cracking due to lowering the grain boundary cohesion strength [7,16].…”

Section: Introductionmentioning

confidence: 99%

Investigation on thermo-mechanical processing and intergranular corrosion of TP347H stainless steel

Gao

Wang

Liu

et al. 2021

Materials Science and Technology

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The grain boundary character distributions of TP347H stainless steel after different thermo-mechanical processes in terms of grain boundary engineering (GBE) were investigated by EBSD. DL-EPR and electrolytic oxalic acid etch tests were carried out to evaluate the improvement of GBE treatment on intergranular corrosion. The low-Σ CSL grain boundary proportion of the TP347H can be increased to about 90% through a single-step thermo-mechanical treatment consisted of a slight tensile deformation (3%) and subsequent annealing process at 1150°C for 1 h. Compared with conventional TP347H, the optimum GBE microstructure suppressed intergranular corrosion due to the high proportion of low-Σ CSL grain boundaries, particularly the twin (Σ3) boundaries which is almost immune to intergranular corrosion.

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“…In fact, the temperature range of stabilization PWHT and reheat crack is highly coincident for 347 series stainless steel, and it has great potential to cause reheat crack with welding residual stress in thick wall pipes [6][7]. Some studies showed that coarse-grained heat-affected zone (CGHAZ) is the most susceptibility area for reheat crack in HAZ, and unmixed zone (UZ) is the most susceptibility area for intergranular corrosion [8][9][10]. But previous studies have involved the single properties of the two HAZ areas, and the studies on the comprehensive properties comparation has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Effects of Stabilizing Heat Treatment on Properties of Simulated Microstructures in the Heat-Affected Zone of 347H Stainless Steel

Chang

Zhang

Xie

et al. 2021

KEM

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In this paper, two kinds of heat affected zone (HAZ) simulation structures of 347H stainless steel, which are coarse grain zone (CGZ) and unmixed zone (UZ), were prepared by thermal simulator. The material properties of toughness, reheat crack susceptibility and intergranular corrosion susceptibility of the two kinds of HAZ simulation structures were studied by impact test, high temperature tensile test, electrochemical potentiodynamic reactivation (EPR) test and micro morphology test. The result shows that CGHAZ had better toughness. But after the stabilizing heat treatment, it was weakened while that of the UZ was enhanced. The reheat crack susceptibility of the CGZ and UZ both increases after stabilization heat treatment, and the tendency of the UZ are more obvious. Stabilizing heat treatment has a certain effect on the prevention of sensitization process, which can improve the intergranular corrosion resistance of the material. Stabilizing heat treatment is double-edged to 347H HAZ, and it needs combined with the specific situation to used.

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Failure analysis of the TP347H austenitic stainless steel tube of boiler reheater in a coal-fired power plant

Cited by 17 publications

References 11 publications

Binary classification of welding defect based on deep learning

Binary classification of welding defect based on deep learning

Investigation on thermo-mechanical processing and intergranular corrosion of TP347H stainless steel

Effects of Stabilizing Heat Treatment on Properties of Simulated Microstructures in the Heat-Affected Zone of 347H Stainless Steel

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