MétaCan
Menu
Back to cohort
Record W3152956556 · doi:10.1007/s11661-021-06248-y

Effect of Heat Treatment on Microstructure Evolution of X38CrMoV5-1 Hot-Work Tool Steel Produced by L-PBF

2021· article· en· W3152956556 on OpenAlexaff
Gregorio Carasi, Bosco Yu, Esther Hutten, Hatem S. Zurob, Riccardo Casati, Maurizio Vedani

Bibliographic record

VenueMetallurgical and Materials Transactions A · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcMaster University
FundersPolitecnico di MilanoMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsMicrostructureMaterials scienceTemperingQuenching (fluorescence)MartensiteMetallurgyTransmission electron microscopyFusionAlloyComposite materialOpticsNanotechnology

Abstract

fetched live from OpenAlex

Abstract The X38CrMoV5-1 hot-work tool steel produced by laser powder bed fusion was investigated to assess the effect of quenching and tempering and direct tempering on the as-built microstructure. After the printing process, the material microstructure appeared to be characterized by a fine cellular network consisting of γ -Fe cell boundaries and α ′-Fe cores. Scheil–Gulliver curves, X-ray diffraction patterns, and transmission electron microscopy images suggested a transformation of the inner core zone from δ -Fe to α ′-Fe through γ -Fe. Air quenching promoted the transition of the solidification structure into a fully martensitic microstructure. Both as-built and quenched samples revealed the presence of manganese oxides and vanadium carbonitrides forming core-shell structures. After tempering, starting from as-built and from quenched condition, a dispersion of nano-sized V and Cr-rich second phases was formed in the microstructure, achieving hardness values comparable to those obtained by the same alloy produced by conventional methods. The specimen tempered directly after the laser powder bed fusion process showed a hardness peak shifted towards higher temperatures compared to the conventionally tempered sample.

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 categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMetallurgical and Materials Transactions ASame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207