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Record W1157062524 · doi:10.1115/gt2015-43971

Laser Consolidation: A Novel Additive Manufacturing Process for Making Net-Shape Functional Metallic Components for Gas Turbine Applications

2015· article· en· W1157062524 on OpenAlexaffabout
Lijue Xue, Yangsheng Li, Jianyin Chen, Shaodong Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNear net shapeConsolidation (business)MachiningSelective laser meltingMechanical engineeringMaterials scienceProcess engineeringManufacturing engineeringProcess (computing)Manufacturing processComputer scienceEngineeringMetallurgyComposite material

Abstract

fetched live from OpenAlex

Laser consolidation (LC) is a novel additive manufacturing process being developed by the National Research Council Canada (NRC) at its London facility. LC offers unique capabilities in the production of net-shape functional metallic parts requiring no further post-machining. NRC’s LC technology has achieved dimensional accuracy of up to +/−0.05 mm with a surface finish up to 1 μm Ra (depending on the materials used in the manufacturing process). The LC process differs from other additive manufacturing technologies by its high precision deposition system that can build functional parts or features on top of existing parts using various high performance materials and alloys. In this paper, laser consolidation of various high performance materials (such as Ni-base super alloys and Ti-6Al-4V alloy) will be discussed and the examples will be given on building complex functional components and repairing parts otherwise unrepairable for gas turbine and other applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.921

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 head0.067
GPT teacher head0.277
Teacher spread0.210 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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