Prediction of fatigue life of metastable austenitic steel by a combination of acoustic and eddy current data

Mishakin, V. V.; Гончар, А. В.; Kurashkin, K. V.; Kachanov, Mark

doi:10.1016/j.ijfatigue.2020.105846

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…In principle, changes in acoustic birefringence can result from several different physical phenomena: texture evolution due to plastic deformation, 30,31,33,34,37,41,[46][47][48] applied or residual stresses, 41,46-48 phase transformation, 27 and formation of oriented defects. 30,48 With regard to this study, we are guided by the following considerations.…”

Section: Acoustic Birefringence Measurementsmentioning

confidence: 99%

“…To date, several different techniques have been developed for assessing fatigue microstructural damage of metal using ultrasonic bulk waves. 8,[22][23][24][25][26][27] It also seems promising to use the well-known acoustic birefringence technique, which has already been successfully applied to monitor metal degradation during plastic deformation [28][29][30][31][32][33][34][35][36][37] and creep. 29,30,34 The acoustic birefringence technique has several engineering advantages.…”

Section: Introductionmentioning

confidence: 99%

“…To assess internal microstructural damage, it is advisable to use an ultrasonic method based on the propagation of bulk waves. To date, several different techniques have been developed for assessing fatigue microstructural damage of metal using ultrasonic bulk waves 8,22–27 . It also seems promising to use the well‐known acoustic birefringence technique, which has already been successfully applied to monitor metal degradation during plastic deformation 28–37 and creep 29,30,34 …”

Section: Introductionmentioning

confidence: 99%

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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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Section: Acoustic Birefringence Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Self Cite

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“…Deformation-induced martensitic transformation and its impact on the deformation behavior of ASSs during monotonic or cyclic loading via the TRIP or TRIP/TWIP effect has been the subject of numberless studies; for a recent review, see [6,8,11,12,15,16]. Among other areas in which the role of DIM is of outstanding importance, we may include (i) grain refinement via reversion annealing after prior cold working [8,17,18], (ii) magnetic stability at cryogenic temperatures (superconducting magnets) [19,20], (iii) non-destructive monitoring of fatigue damage and assessment of residual fatigue life [21][22][23][24][25], (iv) delayed cracking after deep drawing of lean-nickel alloyed ASSs [26,27], (v) the role of DIM in hydrogen environment embrittlement [28][29][30][31][32], and (vi) pitting corrosion and stress corrosion behavior [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning

Mikmeková,

Man,

Ambrož

et al. 2023

Metals

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This paper aims to demonstrate a novel technique enabling the accurate visualization and fast mapping of deformation-induced α′-martensite produced during cyclic straining of a metastable austenitic stainless steel, refined by reversion annealing to different grain sizes. The technique is based on energy and angular separation of the signal electrons in a scanning electron microscope (SEM). Collection of the inelastic backscattered electrons emitted under high take-off angles from a sample surface results in the acquisition of micrographs with high sensitivity to structural defects, such as dislocations, grain boundaries, and other imperfections. The areas with a high density of lattice imperfections reduce the penetration depth of the primary electrons, and simultaneously affect the signal electrons leaving the specimen. This results in an increase in the inelastic backscattered electrons yielded from the vicinity of α′-martensite, and a bright halo surrounds this phase. The α′-martensite phase can thus be separated from the austenitic matrix in SEM micrographs. In this work, we propose a deep learning method for a precise α′-martensite mapping within a large area. Various deep learning-based methods have been tested, and the best result measured by both Dice loss and IoU scores has been achieved using the U-Net architecture extended by the ResNet encoder.

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“…Известно, что пластическое деформирование хромоникелевых сталей с низкой энергией дефектов упаковки сопровождается образованием фазы деформационного мартенсита из исходной фазы аустенита. Выделение мартенситной фазы, имеющей более высокие прочностные свойства по сравнению с фазой аустенита, приводит к изменению электромагнитных, упругих и акустических свойств всего материала, а также оказывает влияние на процесс накопления поврежденности в более мягком аустените [1][2][3][4][5]. В [6] показано, что плотность микротрещин имеет высокую корреляцию с объемной долей деформационного мартенсита.…”

Section: Introductionunclassified

Влияние Микротрещин На Коэффициент Пуассона При Пластическом Деформировании Аустенитной Стали

Kirikov¹,

Mishakin²,

Клюшников³

2022

Журнал Технической Физики

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We researched the influence of damage accumulation on the Poisson's ratio measured by echo-pulse acoustic method during plastic deformation of 12Kh18N10T steel. On the basis of the obtained experimental data we calculated the partial contributions to the change in the Poisson's ratio of damage accumulation and separation of the strain induced martensite phase. The characteristics of stable cracks forming near strain martensite particles at small degrees of plastic strain have been analyzed by computer simulation. The theoretical dependence of the change in the Poisson's ratio due to crack formation during plastic deformation has been constructed. A good agreement between the experimental data and theoretical calculations has been obtained.

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Prediction of fatigue life of metastable austenitic steel by a combination of acoustic and eddy current data

Cited by 14 publications

References 17 publications

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

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

High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning

Влияние Микротрещин На Коэффициент Пуассона При Пластическом Деформировании Аустенитной Стали

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