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Record W2888822749 · doi:10.1016/j.dib.2018.08.135

Data related to the sinter structure analysis of titanium structures fabricated via binder jetting additive manufacturing

2018· article· en· W2888822749 on OpenAlex
Evan Wheat, Mihaela Vlasea, James Hinebaugh, Craig Metcalfe

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceSinteringParticle sizeTitaniumParticle-size distributionParticle (ecology)Relative densityPowder metallurgyMetallurgyComposite materialChemical engineering

Abstract

fetched live from OpenAlex

The adoption of metal binder jetting additive manufacturing (AM) for functional parts relies on a deep understanding between the materials, the design aspects, the additive manufacturing process and sintering. This work focuses on the relationship between sintering theory and process outcomes. The data included in this article provides additional supporting information on the authors' recent publication (Wheat et al., 2018 [1]) on the sinter structure analysis of commercially pure titanium parts manufactured using powder bed binder jetting additive manufacturing. For this work, commercially pure titanium was deployed to study the effect of powder size distributions on green and sintered part qualities (bulk density, relative density, particle size, pore size, sinter neck size). This manuscript includes the overall computed tomography visualization methods and results for the green and sintered samples using uni- and bi-modal powders. Moreover, the effective particle and pore size for the different batches of powder are presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.002
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 head0.026
GPT teacher head0.270
Teacher spread0.245 · 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