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Record W4412112824 · doi:10.1063/5.0264299

Numerical analysis of the cladding layer forming characteristics under the different wire feed speed conditions in additive manufacturing with inclined substrate

2025· article· en· W4412112824 on OpenAlexaff
Yuewei Ai, Chenglong Ye, Yiyuan Wang, Chuanbin Du

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsCladding (metalworking)PhysicsLayer (electronics)Composite materialSubstrate (aquarium)Materials science

Abstract

fetched live from OpenAlex

Additive manufacturing with inclined substrate often exists in the practical applications. Most of the studies about laser wire additive manufacturing (LWAM) are mainly focused on the condition with horizontal substrate. A dynamic model for the LWAM with inclined substrate is developed to analyze the cladding layer (CL) forming process under the different wire feed speed conditions. The effect of wire feed speed on the CL forming characteristics in the LWAM with inclined substrate is discussed. It is found that the molten pool (MP) length and the maximum height of CL are increased, and the transfer period is decreased with the increase in wire feed speed. When the wire feed speed is 35 mm/s, some shallow valleys formed in the middle part of CL cause the increase in the fluctuation range of CL height compared with that under 40 mm/s wire feed speed condition. The CL height keeps in the relatively stable status without the obvious fluctuation under 40 mm/s wire feed speed condition. As the wire feed speed is increased to 45 mm/s, the CL morphology indicates the characteristic with the obvious peak and valley, and the forming quality of CL is deteriorated seriously. The obtained results are beneficial for promoting the LWAM application with inclined substrate.

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: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.436

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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