FIELD: measuring.
SUBSTANCE: invention relates to research methods for determining operating time and residual resource of material of a material sample. Essence: images of microstructure of zone of dangerous section are obtained and its quantitative analysis is carried out. Quantitative parameters used for analysis are: concentration of slip bands and defects in material (n), relative area of microstructure defects and slip bands formed during fatigue loading Frel. and average value of increment of fractal dimension ΔDF followed by. Obtained data serve as input data for an artificial neural network (ANN), at the output of which the material damage index Φ is used, which varies from 0 – initial undamaged state of material to 1 – state of destruction of material – formation of macrocracks. ANN is considered trained when the training sample reaches about 3,000 images with different levels of damage, after which the ANN operates in an automated mode and receives at the output the actual value of the damage of the material Φ for the investigated microstructure.
EFFECT: increased accuracy of evaluation of structural material damage and its residual resource.
1 cl, 1 dwg, 1 tbl
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Authors
Dates
2024-09-18—Published
2024-04-18—Filed