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Record W2620825062 · doi:10.4050/f-0070-2014-9563

Utilizing Selective Laser Sintering For Production Fabrication of Peculiar Support Equipment

2014· article· en· W2620825062 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Technology and Methodologies
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsFabricationSelective laser sinteringSinteringMaterials scienceLaserProduction (economics)OptoelectronicsProcess engineeringMetallurgyOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Traditionally, helicopter peculiar support equipment is designed, developed, and fabricated using conventional methods, primarily with metallic materials. Specifically, component repair and overhaul tools containing unique, complex features (e.g. internal involute splines) are fabricated using conventional broaching, machining, or electrical discharge machining (EDM) techniques. These techniques combined with the low volume production and acquisition of these products, result in high cost and long lead times. As an alternative, Selective Laser Sintering (SLS), the process of using 3D CAD models to "grow" parts using a laser to sinter powdered material, can be utilized with the primary benefits being inherent cost savings and lead time reductions. This process also facilitates the ability to develop unique, innovative, and simpler tools that would have been impractical or impossible to fabricate using conventional methods. Feasibility, proofing, and practical implementations are the focus of this paper.

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: none
Teacher disagreement score0.745
Threshold uncertainty score0.299

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 head0.036
GPT teacher head0.278
Teacher spread0.242 · 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

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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