MétaCan
Menu
Back to cohort
Record W2318788881 · doi:10.1063/1.4940606

Inspection of additive manufactured parts using laser ultrasonics

2016· article· en· W2318788881 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

VenueAIP conference proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceInconelLaserLayer (electronics)FusionDeposition (geology)PorosityComposite materialOptics

Abstract

fetched live from OpenAlex

Additive manufacturing is a novel technology of high importance for global sustainability of resources. As additive manufacturing involves typically layer-by-layer fusion of the feedstock (wire or powder), an important characteristic of the fabricated metallic structural parts, such as those used in aero-engines, is the performance, which is highly related to the presence of defects, such as cracks, lack of fusion or bonding between layers, and porosity. For this purpose, laser ultrasonics is very attractive due to its non-contact nature and is especially suited for the analysis of parts of complex geometries. In addition, the technique is well adapted to online implementation and real-time measurement during the manufacturing process. The inspection can be performed from either the top deposited layer or the underside of the substrate and the defects can be visualized using laser ultrasonics combined with the synthetic aperture focusing technique (SAFT). In this work, a variety of results obtained off-line on INCONEL® 718 and Ti-6Al-4V coupons that were manufactured using laser powder, laser wire, or electron beam wire deposition are reported and most defects detected were further confirmed by X-ray micro-computed tomography.

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 score0.020
Threshold uncertainty score0.569

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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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