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X-ray computed tomography vs Archimedes method: a head-to-head comparison

2023· article· en· W4389787248 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.

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

Bibliographic record

VenueMATEC Web of Conferences · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsPorosityMaterials scienceAluminiumHead (geology)TomographyAlloyReliability (semiconductor)Selective laser meltingComputed tomographyComposite materialBiomedical engineeringOpticsMicrostructureEngineeringSurgery

Abstract

fetched live from OpenAlex

Metal additive manufacturing (AM) is growing rapidly towards industrial adoption in various industries, but porosity remains a concern because it creates areas of stress that affects the mechanical properties and reduces reliability. Porosity can be quantified by X-ray computed tomography (XCT), but the method is relatively slow and expensive. The Archimedes density measurement method is widely used due to the low cost and ease of operation; however, it is limited in its precision for low porosity levels. In this work, a series of additively manufactured aluminium 6061-Ti6 alloy samples with different types and quantities of porosities are subjected to Archimedes, gas pycnometer and X-ray methods. This work clarifies the application range and limitations of each of these methods, using 10 mm cubes of Al 6061-Ti6 manufactured by laser powder bed fusion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.750

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 head0.039
GPT teacher head0.302
Teacher spread0.263 · 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