Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework
Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rovinelli, Andrea [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Modelling and simulation in materials science and engineering - Bristol : IOP Publ., 1992, 25(2017), 4 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2017 ; number:4 |
| Links: |
|---|
| DOI / URN: |
10.1088/1361-651X/aa6c45 |
|---|
| Katalog-ID: |
OLC1998286657 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1998286657 | ||
| 003 | DE-627 | ||
| 005 | 20230715081519.0 | ||
| 007 | tu | ||
| 008 | 171125s2017 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1088/1361-651X/aa6c45 |2 doi | |
| 028 | 5 | 2 | |a PQ20171125 |
| 035 | |a (DE-627)OLC1998286657 | ||
| 035 | |a (DE-599)GBVOLC1998286657 | ||
| 035 | |a (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 | ||
| 035 | |a (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 530 |q DE-600 |
| 100 | 1 | |a Rovinelli, Andrea |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. | ||
| 540 | |a Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License | ||
| 650 | 4 | |a Materials Science | |
| 650 | 4 | |a Condensed Matter | |
| 650 | 4 | |a Physics | |
| 700 | 1 | |a Guilhem, Yoann |4 oth | |
| 700 | 1 | |a Proudhon, Henry |4 oth | |
| 700 | 1 | |a Lebensohn, Ricardo A |4 oth | |
| 700 | 1 | |a Ludwig, Wolfgang |4 oth | |
| 700 | 1 | |a Sangid, Michael D |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Modelling and simulation in materials science and engineering |d Bristol : IOP Publ., 1992 |g 25(2017), 4 |w (DE-627)131197843 |w (DE-600)1150126-1 |w (DE-576)038486210 |x 0965-0393 |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:2017 |g number:4 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1088/1361-651X/aa6c45 |3 Volltext |
| 856 | 4 | 2 | |u https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-PHY | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 25 |j 2017 |e 4 | ||
| author_variant |
a r ar |
|---|---|
| matchkey_str |
article:09650393:2017----::sesnrlaiiyfaiuidctraaeesosalrcgotv |
| hierarchy_sort_str |
2017 |
| publishDate |
2017 |
| allfields |
10.1088/1361-651X/aa6c45 doi PQ20171125 (DE-627)OLC1998286657 (DE-599)GBVOLC1998286657 (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo DE-627 ger DE-627 rakwb eng 530 DE-600 Rovinelli, Andrea verfasserin aut Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License Materials Science Condensed Matter Physics Guilhem, Yoann oth Proudhon, Henry oth Lebensohn, Ricardo A oth Ludwig, Wolfgang oth Sangid, Michael D oth Enthalten in Modelling and simulation in materials science and engineering Bristol : IOP Publ., 1992 25(2017), 4 (DE-627)131197843 (DE-600)1150126-1 (DE-576)038486210 0965-0393 nnns volume:25 year:2017 number:4 http://dx.doi.org/10.1088/1361-651X/aa6c45 Volltext https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 AR 25 2017 4 |
| spelling |
10.1088/1361-651X/aa6c45 doi PQ20171125 (DE-627)OLC1998286657 (DE-599)GBVOLC1998286657 (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo DE-627 ger DE-627 rakwb eng 530 DE-600 Rovinelli, Andrea verfasserin aut Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License Materials Science Condensed Matter Physics Guilhem, Yoann oth Proudhon, Henry oth Lebensohn, Ricardo A oth Ludwig, Wolfgang oth Sangid, Michael D oth Enthalten in Modelling and simulation in materials science and engineering Bristol : IOP Publ., 1992 25(2017), 4 (DE-627)131197843 (DE-600)1150126-1 (DE-576)038486210 0965-0393 nnns volume:25 year:2017 number:4 http://dx.doi.org/10.1088/1361-651X/aa6c45 Volltext https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 AR 25 2017 4 |
| allfields_unstemmed |
10.1088/1361-651X/aa6c45 doi PQ20171125 (DE-627)OLC1998286657 (DE-599)GBVOLC1998286657 (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo DE-627 ger DE-627 rakwb eng 530 DE-600 Rovinelli, Andrea verfasserin aut Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License Materials Science Condensed Matter Physics Guilhem, Yoann oth Proudhon, Henry oth Lebensohn, Ricardo A oth Ludwig, Wolfgang oth Sangid, Michael D oth Enthalten in Modelling and simulation in materials science and engineering Bristol : IOP Publ., 1992 25(2017), 4 (DE-627)131197843 (DE-600)1150126-1 (DE-576)038486210 0965-0393 nnns volume:25 year:2017 number:4 http://dx.doi.org/10.1088/1361-651X/aa6c45 Volltext https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 AR 25 2017 4 |
| allfieldsGer |
10.1088/1361-651X/aa6c45 doi PQ20171125 (DE-627)OLC1998286657 (DE-599)GBVOLC1998286657 (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo DE-627 ger DE-627 rakwb eng 530 DE-600 Rovinelli, Andrea verfasserin aut Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License Materials Science Condensed Matter Physics Guilhem, Yoann oth Proudhon, Henry oth Lebensohn, Ricardo A oth Ludwig, Wolfgang oth Sangid, Michael D oth Enthalten in Modelling and simulation in materials science and engineering Bristol : IOP Publ., 1992 25(2017), 4 (DE-627)131197843 (DE-600)1150126-1 (DE-576)038486210 0965-0393 nnns volume:25 year:2017 number:4 http://dx.doi.org/10.1088/1361-651X/aa6c45 Volltext https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 AR 25 2017 4 |
| allfieldsSound |
10.1088/1361-651X/aa6c45 doi PQ20171125 (DE-627)OLC1998286657 (DE-599)GBVOLC1998286657 (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo DE-627 ger DE-627 rakwb eng 530 DE-600 Rovinelli, Andrea verfasserin aut Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License Materials Science Condensed Matter Physics Guilhem, Yoann oth Proudhon, Henry oth Lebensohn, Ricardo A oth Ludwig, Wolfgang oth Sangid, Michael D oth Enthalten in Modelling and simulation in materials science and engineering Bristol : IOP Publ., 1992 25(2017), 4 (DE-627)131197843 (DE-600)1150126-1 (DE-576)038486210 0965-0393 nnns volume:25 year:2017 number:4 http://dx.doi.org/10.1088/1361-651X/aa6c45 Volltext https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 AR 25 2017 4 |
| language |
English |
| source |
Enthalten in Modelling and simulation in materials science and engineering 25(2017), 4 volume:25 year:2017 number:4 |
| sourceStr |
Enthalten in Modelling and simulation in materials science and engineering 25(2017), 4 volume:25 year:2017 number:4 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Materials Science Condensed Matter Physics |
| dewey-raw |
530 |
| isfreeaccess_bool |
false |
| container_title |
Modelling and simulation in materials science and engineering |
| authorswithroles_txt_mv |
Rovinelli, Andrea @@aut@@ Guilhem, Yoann @@oth@@ Proudhon, Henry @@oth@@ Lebensohn, Ricardo A @@oth@@ Ludwig, Wolfgang @@oth@@ Sangid, Michael D @@oth@@ |
| publishDateDaySort_date |
2017-01-01T00:00:00Z |
| hierarchy_top_id |
131197843 |
| dewey-sort |
3530 |
| id |
OLC1998286657 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1998286657</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715081519.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">171125s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1088/1361-651X/aa6c45</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20171125</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1998286657</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1998286657</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rovinelli, Andrea</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Materials Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Condensed Matter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Physics</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guilhem, Yoann</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Proudhon, Henry</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lebensohn, Ricardo A</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ludwig, Wolfgang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sangid, Michael D</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Modelling and simulation in materials science and engineering</subfield><subfield code="d">Bristol : IOP Publ., 1992</subfield><subfield code="g">25(2017), 4</subfield><subfield code="w">(DE-627)131197843</subfield><subfield code="w">(DE-600)1150126-1</subfield><subfield code="w">(DE-576)038486210</subfield><subfield code="x">0965-0393</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:4</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1088/1361-651X/aa6c45</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2017</subfield><subfield code="e">4</subfield></datafield></record></collection>
|
| author |
Rovinelli, Andrea |
| spellingShingle |
Rovinelli, Andrea ddc 530 misc Materials Science misc Condensed Matter misc Physics Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| authorStr |
Rovinelli, Andrea |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)131197843 |
| format |
Article |
| dewey-ones |
530 - Physics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0965-0393 |
| topic_title |
530 DE-600 Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework Materials Science Condensed Matter Physics |
| topic |
ddc 530 misc Materials Science misc Condensed Matter misc Physics |
| topic_unstemmed |
ddc 530 misc Materials Science misc Condensed Matter misc Physics |
| topic_browse |
ddc 530 misc Materials Science misc Condensed Matter misc Physics |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
y g yg h p hp r a l ra ral w l wl m d s md mds |
| hierarchy_parent_title |
Modelling and simulation in materials science and engineering |
| hierarchy_parent_id |
131197843 |
| dewey-tens |
530 - Physics |
| hierarchy_top_title |
Modelling and simulation in materials science and engineering |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)131197843 (DE-600)1150126-1 (DE-576)038486210 |
| title |
Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| ctrlnum |
(DE-627)OLC1998286657 (DE-599)GBVOLC1998286657 (PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320 (KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo |
| title_full |
Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| author_sort |
Rovinelli, Andrea |
| journal |
Modelling and simulation in materials science and engineering |
| journalStr |
Modelling and simulation in materials science and engineering |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
txt |
| author_browse |
Rovinelli, Andrea |
| container_volume |
25 |
| class |
530 DE-600 |
| format_se |
Aufsätze |
| author-letter |
Rovinelli, Andrea |
| doi_str_mv |
10.1088/1361-651X/aa6c45 |
| dewey-full |
530 |
| title_sort |
assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| title_auth |
Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| abstract |
Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. |
| abstractGer |
Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. |
| abstract_unstemmed |
Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 |
| container_issue |
4 |
| title_short |
Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework |
| url |
http://dx.doi.org/10.1088/1361-651X/aa6c45 https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936 |
| remote_bool |
false |
| author2 |
Guilhem, Yoann Proudhon, Henry Lebensohn, Ricardo A Ludwig, Wolfgang Sangid, Michael D |
| author2Str |
Guilhem, Yoann Proudhon, Henry Lebensohn, Ricardo A Ludwig, Wolfgang Sangid, Michael D |
| ppnlink |
131197843 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth |
| doi_str |
10.1088/1361-651X/aa6c45 |
| up_date |
2024-07-04T04:42:15.853Z |
| _version_ |
1803622165612855296 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1998286657</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715081519.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">171125s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1088/1361-651X/aa6c45</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20171125</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1998286657</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1998286657</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)h1063-8dc2a31a0f395002041440e1d35bfe644b93d3aa383372e218bcf0a3799533320</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0227354320170000025000400000assessingreliabilityoffatigueindicatorparametersfo</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rovinelli, Andrea</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © Distributed under a Creative Commons Attribution 4.0 International License</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Materials Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Condensed Matter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Physics</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guilhem, Yoann</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Proudhon, Henry</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lebensohn, Ricardo A</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ludwig, Wolfgang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sangid, Michael D</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Modelling and simulation in materials science and engineering</subfield><subfield code="d">Bristol : IOP Publ., 1992</subfield><subfield code="g">25(2017), 4</subfield><subfield code="w">(DE-627)131197843</subfield><subfield code="w">(DE-600)1150126-1</subfield><subfield code="w">(DE-576)038486210</subfield><subfield code="x">0965-0393</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:4</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1088/1361-651X/aa6c45</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://hal-mines-paristech.archives-ouvertes.fr/hal-01540936</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2017</subfield><subfield code="e">4</subfield></datafield></record></collection>
|
| score |
7.401041 |