Damage assessment of materials at low temperatures using the parameter of the fractal dimension of the microstructure

Kabaldin, Yu. G.; Khlybov, A. A.; Anosov, M. S.; Shatagin, D. A.

doi:10.1088/1757-899x/971/4/042021

Cited by 1 publication

(8 citation statements)

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“…7 Therefore, many researchers use various types of fractal dimensions to assess fatigue damage. 7,[9][10][11][12][13][14][15][16] A brief description of digital processing images and calculating the fractal dimension is given below. More details related to software and algorithms for processing and computing can be found in Kabaldin et al 15,16 To eliminate the influence of noise and uneven illumination, a two-dimensional wavelet function was used for image processing.…”

Section: Metallographic Studiesmentioning

confidence: 99%

“…7,[9][10][11][12][13][14][15][16] A brief description of digital processing images and calculating the fractal dimension is given below. More details related to software and algorithms for processing and computing can be found in Kabaldin et al 15,16 To eliminate the influence of noise and uneven illumination, a two-dimensional wavelet function was used for image processing. The wavelet transform makes it possible to reliably identify the grain boundaries and the slip bands even with strong image noise, while the result depends little on the image brightness.…”

Section: Metallographic Studiesmentioning

confidence: 99%

“…An important advantage of the optical method is the ability to directly observe microstructure changes on the surface. Great opportunities for automated quantifying microstructural changes are opening up with using fractal analysis, 7,[9][10][11][12][13][14][15][16] neural networks, 15,[17][18][19][20][21] and artificial intelligence. 15,18,21 Fractal theory is used to investigate the collective evolution of surface short cracks under fatigue.…”

Section: Introductionmentioning

confidence: 99%

“…12 Fractal dimension of material microstructure can be used as a damage indicator. 16 The possibility of using the fractal dimension and convolutional neural network for tiling and classifying the share of viscous and brittle destructions on the fracture surface is shown in Kabaldin et al 15 Neural networks are a viable computational tool for modeling the chaotic behavior of short fatigue crack growth. 17 An artificial neural network with deep learning, hyper-parameter tuning, and cross-validation has been successfully applied to predict the microstructure effects on the mechanical properties of high strength steel.…”

Section: Introductionmentioning

confidence: 99%

“…3D fractal dimension of corroded surface can be used to predict residual fatigue live of precorroded specimens 12 . Fractal dimension of material microstructure can be used as a damage indicator 16 . The possibility of using the fractal dimension and convolutional neural network for tiling and classifying the share of viscous and brittle destructions on the fracture surface is shown in Kabaldin et al 15 …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fatigue life prediction of structural steel using acoustic birefringence and characteristics of persistent slip bands

Гончар

Kurashkin

Andreeva

et al. 2021

Fatigue Fract Eng Mat Struct

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Nondestructive evaluation methods provide additional information about the material fatigue behavior and enhance the comprehension of damage evolution thanks to relationship between microstructure and physical properties. This paper deals with optical and ultrasonic investigations of structural steel specimens tested for low-cycle fatigue. The development of persistent slip bands observed on the surface with an optical microscope was quantified using a previously trained neural network and fractal analysis. A surface damage parameter was defined as the ratio of total area of detected slip bands to the area of observed surface. Relationships between the rate of change and critical value of the damage parameter, the strain range, and the fatigue life were established. A single square relationship between relative number of cycles and ratio of the surface damage parameter to its critical value was obtained.Acoustic birefringence was measured by the echo method. The effect of the strain range on the rate of change in acoustic birefringence was investigated. A single linear relationship between relative number of cycles and change in acoustic birefringence was established. An algorithm for predicting the material remaining life, combining optical and ultrasonic data, was proposed.

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