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Record W7053185856

Transformaciones de fase en aceros de bajo contenido en C microaleados con Nb y V

2015· dissertation· en· W7053185856 on OpenAlexaboutno aff

Bibliographic record

VenueDeposito Adademico Digital Universidad De Navarra (University of Navarra) · 2015
Typedissertation
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAusteniteContinuous cooling transformationNiobiumPearliteMicroalloyed steelMicrostructureVanadiumTorsion (gastropod)Hot work
DOInot available

Abstract

fetched live from OpenAlex

The phase transformations occurring during continuous cooling in Nb-V microalloyed steels have been analyzed in depth on this thesis. In particular, three compositions of niobium microalloyed steels and three steels microalloyed with vanadium have been characterized. 
\nThe work is divided into six chapters and begins with a brief introduction which aims to value the importance of the steel in the modern world. Then, on the second chapter, the most important metallurgical concepts related especially to low carbon steels in the literature have been reviewed and summarized. 
\nChemical compositions and experimental techniques carried out are detailed on the third chapter. Experimental techniques have consisted mainly in two types of tests (dilatometry and multipass torsion tests). The data obtained from the continuous cooling tests have been used to develop two mathematical models. These models aim to summarize the behavior of the analyzed steels according to three leading variables: the austenite grain size, accumulated deformation and cooling rate. 
\nAt the fourth chapter, the results obtained and the principles governing the two developed models have been described. This chapter is divided into four blocks, the first one consists on a summary of the microstructural characterization performed and continuous cooling diagrams (CCT diagrams) obtained by dilatometry tests. These tests were carried out with the three Nb microalloyed steels and C-Mn-V1. 
\nIn the second block of results, a predictive model of the austenite transformation kinetics is developed. The microstructures obtained are composed of one or more phases (ferrite, pearlite and bainite) upon cooling from different austenite microstructures (recrystallized and deformed). 
\nOn the third block the study has been focused on the austenite-ferrite transformation (low cooling rates). Besides the improvement in predicting the mean ferrite size, a model that takes into account the heterogeneity of the austenite grain size distributions before the phase transformation has been developed. The effect of heterogeneity in the austenite-ferrite transformation has been studied in terms of different variables (Effective Austenite grain size and cooling rate). The objective of the model is to predict the grain size distribution of transformed ferrite from different austenite conditions. 
\nFinally, a study of the hot rolling process on different industrial profiles is described with two steels similar to C-Mn-V1 composition. The effect of varying temperatures and strain distribution has been studied by hot torsion multi-pass testing. Then, after quantifying the microstructural changes associated to these changes in the rolling process, the austenite-ferrite transformation model has been validated again. These microstructures correspond to a similar range of cooling rates compared to the microstructures obtained by dilatometry trials, which have been adjusted the model parameters. 
\nIn the fifth chapter of this work the conclusions and future lines of work have been presented. Finally, the three publications developed during the course of this thesis have been attached. Specifically, an international journal publication (ISIJ International, Vol 55, n.9, 2015), an international conference proceeding contribution (British Columbia, Canada, June 2015) and a contribution to the XIII National Materials Congress (Barcelona, June 2014).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.206
Teacher spread0.200 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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