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IMPROVEMENT OF THE FUNCTIONAL PROPERTIES OF NANOSTRUCTURED Ti-Ni SHAPE MEMORY ALLOYS BY MEANS OF THERMOMECHANICAL PROCESSING

2014· article· en· W2139609605 sur OpenAlexfundno aff
Alena Kreitcberg

Notice bibliographique

RevueEspace École de technologie supérieure (École de technologie supérieure) · 2014
Typearticle
Langueen
DomaineMaterials Science
ThématiqueShape Memory Alloy Transformations
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaMinistry of Education and Science of the Russian Federation
Mots-clésThermomechanical processingMicrostructureMaterials scienceShape-memory alloyAnnealing (glass)MetallurgyAusteniteGrain sizeSevere plastic deformationTexture (cosmology)Composite materialComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Severe plastic deformation (SPD) is commonly used for nanostructure formation in Ti-Ni shape memory alloys (SMAs), but it increases the risk of damage during processing and, consequently, negatively affects functional fatigue resistance of these materials. The principal objective of this project is, therefore, to study the interrelations between the processing conditions, damageability during processing, microstructure and the functional properties of Ti-Ni SMAs with the aim of improving long-term functional performances of these materials by optimizing their processing conditions. \n \nFirst, microstructure and fatigue properties of Ti-Ni SMAs were studied after thermomechanical treatment (TMT) with different combinations of severe cold and warm rolling (CR and WR), as well as intermediate and post-deformation annealing (IA and PDA) technological steps. It was shown that either when WR and IA were introduced into the TMT schedule, or CR intensity was decreased, the fatigue life was improved as a consequence of less processing-induced damage and higher density of the favorable B2-austenite texture. This improvement was reached, however, at a price of a lower multi-cycle functional stability of these materials, the latter being a direct consequence of the microstructure coarsening after higher-temperature lower-intensity processing. At the end of this study, however, it was not possible to distinguish between contributions to the functional performances of Ti-Ni SMAs from different processing-related features: a) grain/subgrain size; b) texture; and c) level of rolling-induced defects. \n \nTo be capable of separating contributions to the functional properties of Ti-Ni alloys from grain/subgrain size and from texture, the theoretical crystallographic resource of recovery strain after different TMTs and, therefore, different textures, were calculated and compared with the experiment. The comparative analysis showed that the structural factors (grain/subgrain size) strongly dominate the texture contributions, and therefore, there is no real alternative to having nanocrystalline Ti-Ni alloys, if one needs to maximize the Ti-Ni alloys functional properties. Since the creation of such a microstructure requires the use of severe cold deformation techniques and neither of these techniques can be completely exempt from defects, it was deemed necessary to compare the damage tolerance of nanocrystalline Ti-Ni alloys to that of their nanosubgrained and mixed nanocrystalline/nanosubgrained counterparts. \n \nWith this objective in mind, a detailed analysis of interrelations between the level of the CR/WR-induced damage (edge microcrack size and concentration) and the fatigue life of Ti-Ni SMAs was carried out. It was shown that nanocrystalline structure provides higher tolerance to small-crack propagation than nanosubgrained or mixed nanocrystalline/nanosubgrained structures, and that low-temperature deformability of these alloys has to be improved to benefit from the property-enhancement potential of nanocrystalline structure. \n \nTo broaden our knowledge in the field of Ti-Ni alloy deformability, the strain-rate sensitivity of these alloys was studied. Different microstructures, varying from the coarse- to ultrafine-grained, were created by means of equal-channel angular pressing (ECAP) and subjected to strain-rate sensitivity testing. As a result, the material with ultrafine-grained microstructure demonstrated an improved deformability as compared to the coarse-grained structure, at any deformation temperature. Moreover, it was determined that the smaller the grain size, the lower the temperature and the higher the strain-rate at which superplasticity occurs. Based on the results obtained, combined thermomechanical processing (ECAP at elevated températures followed by CR) was proposed and validated in terms of structural refinement with reduced level of processing-induced defects. \n \nScientific contributions \nThis thesis contributes to the advancement of knowledge in the field of Ti-Ni SMAs’processing-structure-properties interactions, and the main conclusions of this study can be summed-up as follows: \n• Nanocrystalline Ti-Ni alloys significantly outperform nanosubgrain Ti-Ni alloys in terms of the absolute values and stability of their single- and multiple-cycle functional properties (superelasticity and shape memory characteristics). The main factor limiting the number of cycles to failure of the nanocrystalline alloys is the processing-related damage. \n• The structure of Ti-Ni alloys plays significantly higher role in the realization of their functional potential that does their texture. \n• In terms of fatigue life, the nanocrystalline structure has lower small-crack sensitivity than does the nanosubgrained structure. \n• Grain refinement makes it possible to improve deformability of Ti-Ni alloys at any temperature. \n• To produce nanocrystalline Ti-Ni SMAs free of processing-induced-defects, a novel three-step processing is proposed (ECAP+CR+PDA): grain-refining severe plastic deformation at elevated temperatures (ECAP), followed-up by amorphizing SPD at low temperatures (CR), and ended-up by nanocrystallizing post-deformation heat treatment (PDA).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,002

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,217
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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