Evaluation of Hydrogen Embrittlement Effect on Mechanical Strength of Modern Martensitic High-Strength Steels - Experimental Analysis and Artificial Neural Network-based Prediction

Eric A. K. Fangnon

Evaluation of Hydrogen Embrittlement Effect on Mechanical Strength of Modern Martensitic High-Strength Steels - Experimental Analysis and Artificial Neural Network-based Prediction

Julkaisun otsikon käännös: Vetyhaurauden vaikutus modernien martensiittisten korkeaujuuksisien terästen mekaaniseen lujuuteen : Kokeellinen analyysi ja tekoälypohjainen neuroverkkoennuste

Eric A. K. Fangnon

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Vetyhauraus (VH) vaikuttaa haitallisesti useisiin insinöörimateriaaleihin, ja erityisesti sillä on merkittävä haitallinen vaikutus teräksiin korkean arvon teollisuuden kriittisissä sovelluksissa. Tämän väitöskirjan tavoitteena on lisätä ymmärrystä VH-ilmiöstä ja sen vaikutuksesta uusien martensiittisten terästen mekaaniseen suorituskykyyn, keskittyen seuraaviin kolmeen avainkysymykseen: (i) kokeellisten olosuhteiden parantaminen vetykonsentraation (CH) tarkkaan mittaamiseen, (ii) mekaanisen lujuuden arviointiin eri vetyhauraus- ja mekaanisen kuormituksen olosuhteissa ja (iii) uuden tekoälypohjaisen neuroverkkotyökalun kehittäminen, joka soveltuu aluksi tutkimaan vetytermisen desorptiospektroskopian (TDS) ja vedyn teräkselle aiheuttaman vahinkoalttiuden välistä suhdetta, ja jota edelleen kehitetään ennustamaan vetykonsentraation (CH) ja mekaanisen lujuuden välistä suhdetta mahdollistaen pienemmän määrän kokeellisia testejä. Kokeellisen tarkkuuden parantamiseksi vetyhaurastumistutkimuksissa tutkituissa teräksissä kehitettiin näytekappaleen jäähdytysjärjestelmä, joka integroitiin TDS-laitteiston ilmalukkoon. Uusi näytekappaleen jäähdytysjärjestelmä, joka mahdollistaa lämpötilan laskemisen 213˚K:een asti, osoittautui merkittäväksi parannukseksi CH-mittauksissa, mahdollistaen tarkemman analyysin saaduista termisistä desorptiospektreistä, jotka ovat arvokkaita vetyä sitovia mikrorakenteellisiä ansoja koskevassa analyysissä. Neljän martensiittisen teräslajin vedynsitomiskykyä ja mekaanista suorituskykyä, joissa oli vaihteleva aiempien austeniittirakeiden (PAG) morfologia, tutkittiin yksiaksiaalisen vetokokeen ja TDS:n avulla eri vetyvarausolosuhteissa. Tulokset osoittavat, että vedyn sitoutuminen teräkseen samanlaisissa vetyvarausolosuhteissa, ilman ulkoista mekaanista kuormitusta, ei vaihtele terästen välillä. Vedyn sitoutumisen teräkseen havaittiin kuitenkin vaihtelevan ulkoisesta mekaanisesta kuormituksesta riippuen. Tämä vaihtelu ei suoraan riipu tai korreloi terästen eri PAG-morfologian kanssa. Lisäksi tulokset osoittivat, että vetykonsentraatiolla (CH) teräksessä on suurempi vaikutus vedyn tehostamaan vikaantumiseen johtavaan virumisnopeuteen kuin mekaanisella kuormituksella testattaessa teräksiä vakiokuormituksen alaisina. Kehitetyillä tiheillä neuroverkkotyökaluilla, jotka käsittävät kolme erilaista mallia, arvioitiin teräksen vetyhaurastumisalttiuden ja vedyn aiheuttaman vikaantumisen kriittisen vetykonsentraation (CH) ennustamiseksi teräksessä noin 98%:n tarkkuudella. Neuroverkkomallien parantaminen koulutusaineistolla voi tuottaa tehokkaan työkalun terästen vetyhaurauden määrittelmiseen. Tämä voidaan tehdä pienemmällä määrällä aikaa vieviä kokeellisia testejä vaarallisilla ja monimutkaisilla laitteistoilla.

Julkaisun otsikon käännös	Vetyhaurauden vaikutus modernien martensiittisten korkeaujuuksisien terästen mekaaniseen lujuuteen : Kokeellinen analyysi ja tekoälypohjainen neuroverkkoennuste
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Vilaça, Pedro, Vastuuprofessori Malitckii, Evgenii, Ohjaaja Yagodzinskyy, Yuriy, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-64-1639-7
Sähköinen ISBN	978-952-64-1640-3
Tila	Julkaistu - 2024
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

vetyhauraus
lämpödesorptiospektroskopia
martensiittiset korkean lujuuden teräkset
kriittiset vetykonsentraatiot
neuroverkko

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-64-1640-3

Siteeraa tätä

@phdthesis{5a7bd0d91c924469a7ea71fe935f04fe,

title = "Evaluation of Hydrogen Embrittlement Effect on Mechanical Strength of Modern Martensitic High-Strength Steels - Experimental Analysis and Artificial Neural Network-based Prediction",

abstract = "Hydrogen embrittlement (HE) is a challenge affecting several engineering materials, with a significant impact on steels, targeting critical components in high-value industrial applications. To better understand the HE phenomenon and its impact on the mechanical performance of newly developed martensitic steels, this dissertation focuses on the following three key issues: (i) enhancement of experimental conditions for the accurate measurement of hydrogen concentration (CH) in metalic materials, (ii) assessment of the mechanical strength of steels under different hydrogenation and mechanical loading conditions, and (iii) a new artificial neural network (ANN)-based tool, applied initially to investigate the relationship between the hydrogen thermal desorption spectroscopy (TDS) and steel's susceptibility to H-induced damage, is further developed to predict the relation between CH and mechanical strength at failure, with reduced requisite experimental testing. A specimen cooling system was developed and integrated with the air-lock in a TDS apparatus with the objective of enhancing the accuracy of CH measurements in the studied steels. The application of the new specimen cooling system, enabling temperatures down to 213˚K, proved to provide a significant improvement in CH measurement, allowing more accurate analysis of obtained thermal desorption spectra, valuable for H-trapping analysis. The H-uptake and mechanical performance of four martensitic steel grades with varying prior austenite grain (PAG) morphology were studied by monotonic uniaxial tensile testing and TDS in different H-charging conditions. The results demonstrate that the H-uptake under the same Hcharging conditions, without external mechanical loading, does not vary between the steels. However, a variation in H-uptake was evident with external mechanical loading. This variation does not directly depend on, or correlate with the different PAG morphology. Furthermore, the results showed that CH in the steel has a greater influence on the H-enhanced creep rates leading to failure, than the magnitude of the applied loads, during constant load mechanical testing conditions. The developed dense ANN tools, encompassing three different models, were evaluated for the prediction of steel's susceptibility to HE and CH responsible for the H-induced failure with an approximate accuracy of about 98%. Enhancing the ANN models with training data may yield a powerful tool for the HE characterization of steels. This can be done with a reduced dependency on time-consuming experimental testing with hazardous and complex apparatus.",

keywords = "hydrogen embrittlement, thermal desorption spectroscopy, martensitic high-strength steels, critical hydrogen concentrations, artificial neural networks, vetyhauraus, l{\"a}mp{\"o}desorptiospektroskopia, martensiittiset korkean lujuuden ter{\"a}kset, kriittiset vetykonsentraatiot, neuroverkko, hydrogen embrittlement, thermal desorption spectroscopy, martensitic high-strength steels, critical hydrogen concentrations, artificial neural networks",

author = "Fangnon, {Eric A. K.}",

year = "2024",

language = "English",

isbn = "978-952-64-1639-7",

series = "Aalto University publication series DOCTORAL THESES",

publisher = "Aalto University",

number = "16",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Evaluation of Hydrogen Embrittlement Effect on Mechanical Strength of Modern Martensitic High-Strength Steels - Experimental Analysis and Artificial Neural Network-based Prediction

AU - Fangnon, Eric A. K.

PY - 2024

Y1 - 2024

N2 - Hydrogen embrittlement (HE) is a challenge affecting several engineering materials, with a significant impact on steels, targeting critical components in high-value industrial applications. To better understand the HE phenomenon and its impact on the mechanical performance of newly developed martensitic steels, this dissertation focuses on the following three key issues: (i) enhancement of experimental conditions for the accurate measurement of hydrogen concentration (CH) in metalic materials, (ii) assessment of the mechanical strength of steels under different hydrogenation and mechanical loading conditions, and (iii) a new artificial neural network (ANN)-based tool, applied initially to investigate the relationship between the hydrogen thermal desorption spectroscopy (TDS) and steel's susceptibility to H-induced damage, is further developed to predict the relation between CH and mechanical strength at failure, with reduced requisite experimental testing. A specimen cooling system was developed and integrated with the air-lock in a TDS apparatus with the objective of enhancing the accuracy of CH measurements in the studied steels. The application of the new specimen cooling system, enabling temperatures down to 213˚K, proved to provide a significant improvement in CH measurement, allowing more accurate analysis of obtained thermal desorption spectra, valuable for H-trapping analysis. The H-uptake and mechanical performance of four martensitic steel grades with varying prior austenite grain (PAG) morphology were studied by monotonic uniaxial tensile testing and TDS in different H-charging conditions. The results demonstrate that the H-uptake under the same Hcharging conditions, without external mechanical loading, does not vary between the steels. However, a variation in H-uptake was evident with external mechanical loading. This variation does not directly depend on, or correlate with the different PAG morphology. Furthermore, the results showed that CH in the steel has a greater influence on the H-enhanced creep rates leading to failure, than the magnitude of the applied loads, during constant load mechanical testing conditions. The developed dense ANN tools, encompassing three different models, were evaluated for the prediction of steel's susceptibility to HE and CH responsible for the H-induced failure with an approximate accuracy of about 98%. Enhancing the ANN models with training data may yield a powerful tool for the HE characterization of steels. This can be done with a reduced dependency on time-consuming experimental testing with hazardous and complex apparatus.

AB - Hydrogen embrittlement (HE) is a challenge affecting several engineering materials, with a significant impact on steels, targeting critical components in high-value industrial applications. To better understand the HE phenomenon and its impact on the mechanical performance of newly developed martensitic steels, this dissertation focuses on the following three key issues: (i) enhancement of experimental conditions for the accurate measurement of hydrogen concentration (CH) in metalic materials, (ii) assessment of the mechanical strength of steels under different hydrogenation and mechanical loading conditions, and (iii) a new artificial neural network (ANN)-based tool, applied initially to investigate the relationship between the hydrogen thermal desorption spectroscopy (TDS) and steel's susceptibility to H-induced damage, is further developed to predict the relation between CH and mechanical strength at failure, with reduced requisite experimental testing. A specimen cooling system was developed and integrated with the air-lock in a TDS apparatus with the objective of enhancing the accuracy of CH measurements in the studied steels. The application of the new specimen cooling system, enabling temperatures down to 213˚K, proved to provide a significant improvement in CH measurement, allowing more accurate analysis of obtained thermal desorption spectra, valuable for H-trapping analysis. The H-uptake and mechanical performance of four martensitic steel grades with varying prior austenite grain (PAG) morphology were studied by monotonic uniaxial tensile testing and TDS in different H-charging conditions. The results demonstrate that the H-uptake under the same Hcharging conditions, without external mechanical loading, does not vary between the steels. However, a variation in H-uptake was evident with external mechanical loading. This variation does not directly depend on, or correlate with the different PAG morphology. Furthermore, the results showed that CH in the steel has a greater influence on the H-enhanced creep rates leading to failure, than the magnitude of the applied loads, during constant load mechanical testing conditions. The developed dense ANN tools, encompassing three different models, were evaluated for the prediction of steel's susceptibility to HE and CH responsible for the H-induced failure with an approximate accuracy of about 98%. Enhancing the ANN models with training data may yield a powerful tool for the HE characterization of steels. This can be done with a reduced dependency on time-consuming experimental testing with hazardous and complex apparatus.

KW - hydrogen embrittlement

KW - thermal desorption spectroscopy

KW - martensitic high-strength steels

KW - critical hydrogen concentrations

KW - artificial neural networks

KW - vetyhauraus

KW - lämpödesorptiospektroskopia

KW - martensiittiset korkean lujuuden teräkset

KW - kriittiset vetykonsentraatiot

KW - neuroverkko

KW - hydrogen embrittlement

KW - thermal desorption spectroscopy

KW - martensitic high-strength steels

KW - critical hydrogen concentrations

KW - artificial neural networks

M3 - Doctoral Thesis

SN - 978-952-64-1639-7

T3 - Aalto University publication series DOCTORAL THESES

PB - Aalto University

ER -

Evaluation of Hydrogen Embrittlement Effect on Mechanical Strength of Modern Martensitic High-Strength Steels - Experimental Analysis and Artificial Neural Network-based Prediction

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