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Record W3177217420 · doi:10.3390/jmmp5030067

Structure and Mechanical Properties of Laser Powder Bed-Fused and Wrought PH13-8Mo-Type Precipitation Hardening Stainless Steels: Comparative Study

2021· article· en· W3177217420 on OpenAlexafffund
William Turnier Trottier, Alena Kreitcberg, Vladimir Braïlovski

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsÉcole de Technologie Supérieure
FundersMitacs
KeywordsMicrostructureMaterials scienceAlloyPrecipitation hardeningHardening (computing)ElongationMetallurgyAustenitePrecipitationDuctility (Earth science)AnisotropyComposite materialUltimate tensile strength

Abstract

fetched live from OpenAlex

This work focuses on the structure and properties of a laser powder bed-fused (LPBF) precipitation hardening stainless steel and its chemically analogous wrought counterpart, both subjected to an identical combination of solution and aging treatments with the objective of maximizing the material hardness. It was observed that both the LPBF and wrought alloy follows similar evolution of their phase composition, microstructure, and mechanical properties throughout the different stages of the technological workflow. After a solution treatment at 850 °C for 0.5 h and an aging at 525 °C for 2 h, both alloys achieve their highest hardness of ~50 HRC. Notwithstanding this similarity, the LPBF alloy offers a finer microstructure and a lower amount of retained austenite than its wrought counterpart. This microstructure provides comparable strength characteristics to both the LPBF and wrought alloys, while offering a significantly higher ductility to the former as compared to the latter. The elongation at break of the LPBF alloy is, however, strongly build orientation-dependent, with a measured anisotropy of 23%. This anisotropy is caused by the presence of processing-induced pores (average pore size ~23 µm) in the LPBF alloy preferably distributed in planes oriented perpendicular to the build direction.

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 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 score0.021
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.245
Teacher spread0.219 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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