Review and Assessment of Stress-Based Multiaxial Fatigue Models for High Cycle Fatigue Life Predictions
Notice bibliographique
Résumé
In a previous study [1], several multiaxial fatigue models were investigated and compared based on their ability to predict the fatigue limit under multiaxial loading conditions. The widely used historical models such as Findley [2] and Dang Van [3] were compared to several recently developed models. The methods were investigated for the purpose of assessing their potential use in automotive design. In the current study, the same multiaxial fatigue models were assessed based on their ability to perform life prediction under high cycle multiaxial loading. The experimental data used for the assessment of the seven different multiaxial models was taken from literature. Five of the models, Findley, McDiarmid, Susmel-Lazzarin, MZSL, and scaled normal stress were critical plane approaches. The other two models were the LTJ approach and the prismatic hull method, both of which are based on the von Mises criteria. The scaled normal stress approach was the only tensile failure mode model investigated with all other models being shear failure mode. Each stress-based model was used to predict the fatigue life and compare to the experimental results obtained from literature. When selecting data from literature, only high cycle multiaxial fatigue data was used. Experimental data from steel, stainless steel, and aluminum materials were investigated. Most of the materials exhibited shear failure mode, but some materials had mixed mode cracking. The models were judged based on their ability to predict the multiaxial fatigue life within factors of 3 and 5. The LTJ model had the best overall agreement with the experimental data, with 82% of life predictions within a factor of 3 and 96% within a factor of 5. This was due in part to its material parameter, which is derived from multiaxial test data. The LTJ model was one of two models that required multiaxial test data to generate a model parameter. All other models relied on either monotonic or uniaxial fatigue data, which is more readily available and much easier to generate. The Susmel-Lazzarin approach had the second-best overall agreement with 60% and 79% of predictions within factors of 3 and 5, respectively. The scaled normal stress approach, prismatic hull approach, and MZLS approach had life predications that were just slightly less accurate than the Susmel-Lazzarin method. The McDiarmid and Findley models had the worst correlation with the experimental data investigated in this study.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,004 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».