MétaCan
Menu
Back to cohort
Record W2944160519 · doi:10.1063/1.5099730

Laser-ultrasonic inspection of cold spray additive manufacturing components

2019· article· en· W2944160519 on OpenAlexaff
Daniel Lévesque, Christophe Bescond, C. V. Cojocaru

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceUltrasonic sensorLaserCoatingLaser ultrasonicsDeposition (geology)Substrate (aquarium)Composite materialOptoelectronicsOpticsAcousticsTunable laserWavelengthDiode-pumped solid-state laser

Abstract

fetched live from OpenAlex

Cold spray is a solid state coating technology with high deposition rates that is very suitable for large scale applications of additive manufacturing (AM). For quality control of complex parts, laser ultrasonics is particularly attractive due to its non-contact nature and is well adapted to online implementation. In this study, various inspection results performed off-line on metallic parts produced by the cold spray AM process are presented. Laser ultrasonics combined with the synthetic aperture focusing technique (SAFT) is used to detect flaws and through-thickness distributed porosity is investigated using the laser-ultrasonic backscattered signal. Also, laser shockwave is used to characterize bond strength at the interface between the deposition and the substrate. For post heat treatment of cold spray AM metallic parts, laser ultrasonics is used to monitor in real time recrystallization and sintering. Inspection results from either the top layer or the underside of the substrate are reported and discussed.

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 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 score0.015
Threshold uncertainty score0.942

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 head0.012
GPT teacher head0.203
Teacher spread0.190 · 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 teacher head, 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207