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Record W3089346089 · doi:10.1016/j.jmrt.2020.09.104

Effect of laser welding on the mechanical and degradation behaviour of Fe-20Mn-0.6C bioabsorbable alloy

2020· article· en· W3089346089 on OpenAlexafffund
Jacopo Fiocchi, Carlo Alberto Biffi, Sofia Gambaro, Carlo Paternoster, Diego Mantovani, Ausonio Tuissi

Bibliographic record

VenueJournal of Materials Research and Technology · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaConsiglio Nazionale delle Ricerche
KeywordsMaterials scienceWeldingUltimate tensile strengthAlloyComposite materialMartensiteMetallurgyAusteniteElongationLaser beam weldingDuctility (Earth science)MicrostructureCreep

Abstract

fetched live from OpenAlex

The present work aims at exploring the influence of laser welding on the functional behaviour of a Fe-20Mn-0.6C (wt.%) bioabsorbable alloy. At first, the selection of the most suitable process speed (40 mm/s) was done in order to obtain a full penetration joint with limited taper. Then, microstructural and mechanical analyses of welded sheets confirmed suitable performance of the joint, without porosity, thus preserving chemical composition, mechanical resistance and ductility even after welding. In particular, the base material comprised both γ austenite and ε martensite, while the welded samples showed a further type of martensite, namely α’. Moreover, ultimate tensile strength (1095 MPa and 1104 MPa in base and welded material, respectively) and elongation to failure (61.3% and 60.9%, respectively) were almost not influenced by the welding process. Considering the absorbable nature of these alloys, static immersion degradation tests were carried out, and confirmed that the surface of the welded bead did not exhibit a significant variation of the material degradation rate after 14 days in modified Hanks’ solution. Finally, a significant accumulation of degradation products, mainly (Fe,Mn)CO3, was observed along the joining line.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.060
GPT teacher head0.320
Teacher spread0.260 · 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.

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".

Quick stats

Citations15
Published2020
Admission routes2
Has abstractyes

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