Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels

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

“…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.…”

“…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. 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.…”

“…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. 7,10,13 Textural fractography has been developed for study of fracture micromorphology.…”

“…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 …”

“…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 18 . A convolutional neural network has been developed to classify the microstructure images of the damaged surface of metals and alloys 19 .…”