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Record W4392367029 · doi:10.29391/2024.103.016

Estimation of Bead Size and Catchment Efficiency in Laser Cladding

2024· article· en· W4392367029 on OpenAlexfundno aff
NITHEESH KUMAR RAMASAMY, GENTRY WOOD, 鹿谷 元一, Patricio F. Méndez

Bibliographic record

VenueWelding Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsMechanicsThermalFraction (chemistry)BeadStandard deviationMaterials scienceRange (aeronautics)Cladding (metalworking)Work (physics)Environmental scienceMathematicsStatisticsMeteorologyMechanical engineeringEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

This paper presents for the first time a set of closed-form predictions for the width, height, and catchment efficiency of laser-clad beads accounting for the distributed nature of the heat source and powder feed. These predictions are based only on known process parameters such as travel speed, laser power, powder feed rate, and material properties. The mathematical analysis is based on the methodology of asymptotics and blending, and the experimental work was performed in actual industrial conditions. To calculate the thermal efficiency of the process, a mathematical expression as a function of powder feed rate is framed, taking into consideration the shadowing due to the powder cloud. In calculating catchment efficiency, the fraction of powders falling on and ahead of the melt pool is calculated and the results reveal that the fraction falling ahead of the melt pool is negligible and does not contribute to the catchment. Predictions are quantitative and within the error expected for industrial conditions and tabulated material properties. Estimates of the height of the bead are greater than the measured height for every case by an average of 18%. Estimates of width and catchment efficiency are within the range of ± 10%, except for the cases of the low power and higher travel speed domains where a large deviation is observed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.171

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 head0.012
GPT teacher head0.316
Teacher spread0.305 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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