Determining the Resource of Safe Operation for Objects by Images

Abstract

In this paper, a systematic study of the microstructure damage process of metals and alloys was carried out. The main elements of the microstructure surface image, as well as the rules for the formation and interaction of rough slip traces and cracks to determine the model of damage accumulation on the image of the microstructure surface under cyclic loading are determined. A classifier that allows to determine the number of loading cycles before a sample goes out of service is proposed. A modernized structure of the convolutional neural network was developed to classify images of the damaged microstructure of the metals and alloys surface. The proposed classifier for determining the number of loading cycles made it possible to achieve a classification accuracy of 78.43%.

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

“…For investigated steel, microstructure evolution due to low-cycle fatigue mainly consists in the appearance and subsequent growth of slip bands. The pretrained neural network 19,38 was used to obtain the data array containing information about the number, length, width, area, and orientation angle of the slip bands. The images sent to the input of the neural network were obtained using the algorithm of digital processing described below.…”

“…18 A convolutional neural network has been developed to classify the microstructure images of the damaged surface of metals and alloys. 19 A deep convolutional neural network has been implement to detect crack paths together with crack tips based on displacement fields obtained using digital image correlation. 20 The use of supervised machine learning to predict the mechanical properties of multiphase materials based on their microstructural images is explored in Ford et al 21 To assess internal microstructural damage, it is advisable to use an ultrasonic method based on the propagation of bulk waves.…”

“…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, 7,9–16 neural networks, 15,17–21 and artificial intelligence 15,18,21 …”

“…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.…”

“…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 18 . A convolutional neural network has been developed to classify the microstructure images of the damaged surface of metals and alloys 19 . A deep convolutional neural network has been implement to detect crack paths together with crack tips based on displacement fields obtained using digital image correlation 20 .…”