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Record W4233995694 · doi:10.21203/rs.3.rs-190685/v1

Friction stir processing of austenitic stainless steel cold spray coating deposited on 304L stainless steel substrate: feasibility study

2021· preprint· en· W4233995694 on OpenAlexaff
Thomas Perard, A. Sova, H. Robe, Vincent Robin, Yasser Zedan, Philippe Bocher, Éric Feulvarch

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceFriction stir processingCoatingMicrostructureAustenitic stainless steelMetallurgyElectron backscatter diffractionIndentation hardnessTungsten carbideDynamic recrystallizationGrain sizeThermal sprayingComposite materialCorrosionHot working

Abstract

fetched live from OpenAlex

Abstract In this work the feasibility test of friction stir processing (FSP) of 1.5 mm thick austenitic stainless steel cold spray coating deposited on 304L stainless steel substrate was performed using tungsten carbide tool. Applied FSP parameters (advance speed 50 mm/min, rotation speed 300 rpm, axial force 20 kN, tilt angle 1.5°) allowed to perform FSP treatment with a higher depth than the coating thickness. As a result, the material mixing at the coating/substrate interface was observed. The microstructure observation revealed that the coating microstructure in the stir zone was significantly modified. EBSD analysis confirmed that full material recrystallization during FSP allowed to formation of dense and uniform fine-grain structure with the mean grain size 1.9 mm. Average coating microhardness was decreased from 406 HV to 299 HV. Further FSP parameters optimization should be carried out in order to improve the process reliability and avoid any coating failure during treatment.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
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 score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.006
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 head0.075
GPT teacher head0.372
Teacher spread0.297 · 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
Published2021
Admission routes1
Has abstractyes

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