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Record W4318777077 · doi:10.5539/jmsr.v11n2p11

The Effect of Laser Irradiation on The Properties of Micro to Nanolayer Titanium Alloy

2023· article· en· W4318777077 on OpenAlexvenueno aff
Hebatalrahman Hebatalrahman

Bibliographic record

VenueJournal of Materials Science Research · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceIrradiationLaserAlloyMicrostructureTitanium alloyWavelengthComposite materialTitaniumFluenceElastic modulusOpticsMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

In this work, a new technique for Laser irradiation of Ti6Al4VELI was developed, this alloy is an alpha-beta structure, the irradiation was done at room temperature in the normal atmosphere without any external media, the effects of laser irradiation on the Ti6Al4VELI such as the chemical composition of the alloy before and after laser irradiation were recorded by Energy dispersive X-ray “EDX” technique. the affected zone (thickness) of the laser irradiation technique was limited to the surface in micro to the nanoscale. The variation in mechanical properties due to laser irradiation at different wavelengths was measured by the determination of the variation of modulus and hardness at maximum load with a number of pulses. The wear rate was measured for the Ti6Al4VELI before and after laser irradiation at 5000 pulses where the maximum improvement in the hardness occurs at all wavelengths, in the current study, it improves surface mechanical characteristics. The effect of laser irradiation on the structures was studied by SEM, the quantitative analysis was done at all irradiation conditions. Laser treatment is suitable for both field works and industry. The improvement in mechanical properties occurs due to microstructure changes without significant changes in chemical composition. the efficiency and lifetime of the alloys were increased, the work is registered as patent number 24014 in the Egyptian patent office.

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)0.014
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.314
Teacher spread0.251 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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