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Record W2896186327 · doi:10.2351/1.5061067

Investigation of laser consolidation for manufacturing functional net-shape components for potential rocket engine applications

2007· article· en· W2896186327 on OpenAlexaff
Lijue Xue, Yangsheng Li, Thomas Van Daam, C.C. Bampton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNear net shapeConsolidation (business)Materials scienceMachiningMechanical engineeringCeramicLaserLaser beam machiningComputer scienceEngineeringLaser beamsMetallurgyOptics

Abstract

fetched live from OpenAlex

Laser consolidation is an emerging novel computer-aided manufacturing process that produces net-shape functional metallic parts layer by layer directly from a CAD model by using a laser beam to melt the injected powder and re-solidifying it on the substrate or previous layer. As an alternative to the conventional machining process, this novel manufacturing process builds net-shape functional parts or features on an existing part by adding instead of removing material. In this paper, laser consolidation of IN-625 and Ti-6Al-4V alloys has been investigated to build several generic shapes representing parts that may have potential rocket engine applications. Laser consolidation produces components with good surface finish, dimensional accuracy and mechanical properties, and demonstrates the potential for direct manufacturing of functional net-shape components and tooling. In addition, some preliminary results demonstrate that the laser consolidation, in combination with solid-state, net-shape powder metallurgy processing, may provide a unique manufacturing capability to make very complex components for the rocket engines.

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: none
Teacher disagreement score0.553
Threshold uncertainty score0.452

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 head0.023
GPT teacher head0.221
Teacher spread0.197 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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