Grain Boundary Carbide Structure in Tempered Martensitic Steel with Serrated Prior Austenite Grain Boundaries

The cavitational mode of failure of prior austenite grain boundaries in bainitic creep-resisting low alloy steels is now well established as a principal factor in the high incidence of cracking problems which has developed on modern power plant in recent years. The microstructural features dominating the cavitation process at the reheat temperature in a ½CMV bainitic steel of high classical residual level have been determined. The prior austenite grain boundaries become zones of comparative weakness ca . 1 pm thick at 700 °C and are incapable of sustaining significant shear loads. Deformation is therefore initiated by a relaxation of load, through a process of prior austenite grain boundary zone shear, from inclined to transverse boundaries such that a concentration of normal stress develops across the latter. The overall deformation is thereafter determined by cavitation of the transverse boundary zones, the necessary inclined boundary displacements being accommodated by further grain boundary zone shear. Transverse boundary cavitation is shown to be an essentially time-independent process of localized ductile microvoid coalescence resulting from the plastic deformation of the boundary zone.

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Segregation of boron at prior austenite grain boundaries in a quenched martensitic steel studied by atom probe tomography

Scripta Materialia ◽

10.1016/j.scriptamat.2014.09.031 ◽

2015 ◽

Vol 96 ◽

pp. 13-16 ◽

Cited By ~ 48

Author(s):

Y.J. Li ◽

D. Ponge ◽

P. Choi ◽

D. Raabe

Keyword(s):

Grain Boundaries ◽

Atom Probe Tomography ◽

Prior Austenite ◽

Martensitic Steel ◽

Atom Probe ◽

Austenite Grain ◽

Prior Austenite Grain

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Identification of a Grain Boundary Nucleated Phase in 300m Steel

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097995 ◽

1981 ◽

Vol 39 ◽

pp. 294-295 ◽

Cited By ~ 1

Author(s):

R. Padmanabhan ◽

W. E. Wood

Keyword(s):

Grain Boundary ◽

Grain Boundaries ◽

Prior Austenite ◽

Lower Bainite ◽

Sem Analysis ◽

Boundary Phase ◽

Austenite Grain ◽

300M Steel ◽

Strain Fracture ◽

Prior Austenite Grain

One reason proposed for the improvement of plane strain fracture toughness (at similar yield strength levels) of UHSLA steels, austenitized at 1473°K, rather than the conventional 1143°K, is the elimination of non-martensitic phases, at prior austenite grain boundaries. However, contrary to expectations, optical microscopy results from early investigations suggested the existence of bainite at prior austenite grain boundaries in both 4340 and 300M steels when oil quenched from 1473°K and tempered at 180°C or 280°C (but not when quenched from 1143°K). The purpose of the present investigation was to identify through TEM the nature of grain boundary nucleated phase in 300M steel and to observe whether it is absent in the 1143°K heat treatment as reported earlier. A blocky grain boundary phase, resembling lower bainite was observed readily by SEM analysis of etched metallography samples (Fig. 1 a and b). This phase was studied further through TEM and the results are presented in Figs. 2 and 3 for the 1143°K and the 1473°K austenitized specimens respectively.

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Reconstruction and Size Prediction of Prior Austenite Grain Boundary (PAGB) using Artificial Neural Networks

Korean Journal of Metals and Materials ◽

10.3365/kjmm.2020.58.12.822 ◽

2020 ◽

Vol 58 (12) ◽

pp. 822-829

Author(s):

Bong-Kyu Kim ◽

Nam Hoon Goo ◽

Jong Hyuk Lee ◽

Jun Hyun Han

Keyword(s):

Neural Networks ◽

Grain Size ◽

Grain Boundary ◽

Grain Boundaries ◽

Detection Rate ◽

Prior Austenite ◽

Data Sets ◽

Austenite Grain ◽

Austenite Grains ◽

Prior Austenite Grain

To automatically reconstruct the prior austenite grains from as-quenched martensitic structure, we applied a deep learning algorithm to recognize the prior austenite grains boundaries hidden in the martensitic matrix. The FC-DenseNet architecture based on FCN (fully convolutional networks) was used to train the martensite and ground truth label of the prior austenite grain boundaries. The original martensite structures and prior austenite grain boundaries were prepared using different chemical etching solutions. The initial PAGS detection rate was as low as 37.1%, which is not suitable for quantifying the basic properties of the microstructure such as grain size or grain boundary area. By changing the weight factor of the neural net loss function and increasing the size of the data set, the detection rate was improved up to 56.1%. However, even when the detection rate reached 50% or more, the quality of the reconstructed PAGS was not comparable to the analytically calculated results based on EBSD measurements and crystallographic orientation relationships. The prior austenite grain size data sets were obtained from martensite samples via the FCDenseNet method, and had a linear correlation with the mechanical properties measured in the same samples. In order to improve the accuracy of the detection rate using neural networks, it is necessary to increase the number of neural networks and data sets.

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