T91-steel ageing ranking method based on transmission electron microscope

Abstract

The invention discloses a T91-steel ageing ranking method based on a transmission electron microscope. The T91-steel ageing ranking method comprises the following steps of: (1) acquiring transmission electron microscopic graphs of a sample to be ranked and the original sample; (2) carrying out statistics on the number and the sizes of crystal-boundary carbide particles in the transmission electron microscopic graphs of the sample to be ranked and the original sample; and (3) ranking the ageing degree of the sample to be ranked according to the size difference of the crystal-boundary carbide particles in the transmission electron microscopic graphs of the sample to be ranked and the original sample. The T91-steel ageing ranking method disclosed by the invention has the advantages that the ageing condition of T91 in use can be accurately evaluated, and intuitive and scientific basis is provided for maintenance and replacement of parts using T91 steel.

Description

technical field [0001] The invention belongs to the technical field of steel aging evaluation, and in particular relates to a method for evaluating the aging state of T91 steel based on a transmission electron microscope (TEM). Background technique [0002] T91, as a representative steel grade of new high-Cr ferritic heat-resistant steel, is widely used in key components such as superheater tubes and reheater tubes of supercritical unit boilers. Practical applications and simulation experiments show that after long-term service of T91 steel under high temperature and high pressure, the microstructure and structure will degrade, and the creep strength will drop sharply, making the service life much lower than the design life. The microstructure changes include the following aspects: 1) Grain growth: long-term service makes the fine grains in the steel gradually grow up with time, and the grain growth reduces the grain boundaries, decreases the strength, and reduces the mechan...

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Problems solved by technology

[0007] 1) The accuracy of quantification is not good: when the SEM image is used for binarization processing, the contrast between the carbide and the matrix in the scan image will not be very obvious; in addition, when the carbide gathers obviously at the grain boundary, it is difficult to distinguish Agglomerated multiple particles, therefore, it is difficult to only binarize carbide particles during binarization processing, resulting in errors and missing statistics, making the statistical results inaccurate

[0008] 2) Quantitative objects are inaccurate: Experimental research shows that coarsening and aggregation are mainly manifested in carbides at grain boundaries, and intragranular carbides are generally relatively stable. The above method does not distinguish between grain boundaries and intragranular carbides. The general statistics of carbides cannot be statistically processed for the direct cause of performance degradation, resulting in inaccurate quantitative objects

[0009] 3) The evaluation standard is unscientific: the existing methods use the statistical average size to evaluate the degree of aging. On the one hand, the difference in the size of carbides at different positions is ignored. The performance evolution of T91 steel is mainly reflected in the coarsening of carbides at grain boundaries. , the size of intragranular carbides is smaller than that of grain boundary carbides

On the other hand, the difference in size of the same type of carbides is ignored. Actual research shows that even if the average size of carbides is not large, in the presence of some large particles, these large particles will become the main cause of brittle fracture. lead to drastic changes in material properties

It can be seen that using the average size as a standard to evaluate the degree of aging cannot truly reflect the aging damage of materials

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