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Record W4405527893 · doi:10.1016/j.addma.2024.104620

Tailoring mechanical properties and microstructures in laser powder bed fusion of 316 L stainless steel through aluminium alloying and combined ex-situ and in-situ heat treatments

2024· article· en· W4405527893 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdditive manufacturing · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsnot available
FundersCanadian Society of Endocrinology and MetabolismSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungÉcole Polytechnique Fédérale de Lausanne
KeywordsMaterials scienceIn situAluminiumMetallurgyMicrostructureFusionLaserComposite materialOptics

Abstract

fetched live from OpenAlex

Laser powder bed fusion (LPBF) is a bottom-up manufacturing technique using a high-energy laser to selectively melt metallic alloys, enabling the creation of complex microstructures. While rapid cooling rates and process stochasticity can lead to unpredictable properties, LPBF's layer-wise powder deposition method unlocks unprecedented opportunities for in-situ alloying and microstructure engineering. This work introduces a novel blend of 316 L stainless steel with less than two percent of aluminium, demonstrating the potential for readily tunable microstructures leading to versatile mechanical properties – either as-built or following a post-process heat treatment. Compared to conventional fully austenitic LPBF 316 L, this new alloy solidifies into a combination of large BCC delta-ferrite grains and fine FCC gamma-austenite grains. Medium temperature furnace heat treatments significantly reinforce the BCC phase through the formation of NiAl B2 precipitates, leading to hardness values up to 708 HV. Furthermore, in situ selective laser heat treatments allow the transformation from columnar to equiaxed microstructure in a very short time. This unique alloy therefore appears well suited for tailoring local mechanical properties of phases through appropriate ex situ or in situ heat treatments, paving the way for designing composite-like materials within a single build. • We introduce a novel blend based on the widely used 316 L stainless steel alloy and pure aluminium through in-situ alloying. • We demonstrate a new way of manufacturing next-generation components with spatially optimized properties. • A unique microstructure combining large ferrite grains with fine austenite grains leads to versatile mechanical properties. • Nanometric B2 precipitates form within BCC grains, which allows reaching strengths competing with Ultra-high Strength Steels.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
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)0.0000.000

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 head0.017
GPT teacher head0.221
Teacher spread0.204 · 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