MétaCan
Menu
Back to cohort
Record W2969593459 · doi:10.1002/adem.201900617

Laser‐Based Additive Manufacturing Technologies for Aerospace Applications

2019· article· en· W2969593459 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

VenueAdvanced Engineering Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsAerospaceAutomotive industryMaterials scienceManufacturing engineeringAerospace materialsProcess (computing)Mechanical engineeringSystems engineeringAerospace engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Additive manufacturing (AM) is a transformative technology that has rapidly grown over the past decade. AM processes build parts layer by layer and have found applications in the aerospace, biomedical, and automotive fields. The technology holds particular promise for the aerospace industry due to the reduced process time, weight savings of parts, and opportunities for new material development. Herein, a review of laser‐based AM processes, existing AM systems, and aerospace parts being fabricated is presented. It further explores the material properties and microstructure of printed samples with both powder bed fusion and direct energy deposition processes. The benefits and challenges associated with the widespread use of the technology are discussed with emphases on the aerospace sector. Finally, the steps required for parts produced by AM processes to become certified for use in aerospace applications 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 categoriesMeta-epidemiology (narrow)
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 score0.265
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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