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Record W4312078660 · doi:10.3390/ma16010001

In Envelope Additive/Subtractive Manufacturing and Thermal Post-Processing of Inconel 718

2022· article· en· W4312078660 on OpenAlexafffund
Sıla Ece Atabay, Priti Wanjara, Fabrice Bernier, Sheida Sarafan, Javad Gholipour, Josh Soost, Robert Amos, Prakash Patnaik, Mathieu Brochu

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcGill UniversityDepartment of National DefenceNational Research Council Canada
FundersNational Research Council CanadaMinistère de la Défense Nationale
KeywordsMaterials scienceMicrostructureInconelUltimate tensile strengthElongationGrain sizeMachiningTexture (cosmology)MetallurgyComposite materialEnvelope (radar)Alloy

Abstract

fetched live from OpenAlex

This study investigated the application of an in envelope additive/subtractive (LPBF) manufacturing method (Matsuura LUMEX-Avance-25) to fabricate IN718 benchmarking coupons. The coupons were then examined comprehensively for surface finish both with and without high-speed micro-machining. The microstructure of the manufactured IN718 coupons was investigated thoroughly in the as-fabricated condition and following three different standard and one non-standard post-processing heat treatments. As built coupons revealed columnar grain morphology mainly along the <100> direction with a cellular dendritic sub-grain structure and without any strengthening precipitates. Grain size, aspect ratio, and texture were maintained after each of the applied four heat treatments. Only one of the standard heat treatments resulted in the δ phase formation. The other three heat treatments effectively dissolved the Laves phase preventing the δ formation while promoting the formation of γ′/γ″ precipitates. Despite the observed differences in their microstructures, all of the heat treatments resulted in similar yield and ultimate tensile strength values that ranged between 1103−1205 MPa and 1347−1387 MPa, respectively. These values are above the minimum requirements of 1034 MPa and 1241 MPa for the wrought material. The non-standard heat treatment provided the highest elongation of 24.0 ± 0.1% amongst all the heat-treated specimens without a significant loss in strength, while the standard heat treatment for the wrought parts resulted in the lowest elongation of 18.3 ± 0.7% due to the presence of δ phase.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.007
GPT teacher head0.198
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 source (direct Gemma or distilled Codex), 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

Citations21
Published2022
Admission routes2
Has abstractyes

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