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Record W2139609605

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

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

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2014
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education and Science of the Russian Federation
KeywordsThermomechanical processingMicrostructureMaterials scienceShape-memory alloyAnnealing (glass)MetallurgyAusteniteGrain sizeSevere plastic deformationTexture (cosmology)Composite materialComputer science
DOInot available

Abstract

fetched live from 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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.217
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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