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Record W4210709588 · doi:10.21203/rs.3.rs-1297463/v1

Effects of laser process parameters on the hardness profile of AISI 4340 cylindrical samples: statistical and experimental analyses

2022· preprint· en· W4210709588 on OpenAlexaff
Karim Bensalem, Noureddine Barka, Mahmoud Mouradi, Amirhossein Sadeghian, Sasan Sattarpanah Karganroudi

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsIndentation hardnessMaterials scienceMicrostructureHardening (computing)MartensiteLaserHardnessComposite materialScanning electron microscopeOptical microscopeNd:YAG laserMetallurgyLaser power scalingOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract In the present paper, continuous Nd:YAG laser hardening of cylindrical AISI 4340 steel specimens was studied using experimental and statistical analyses. Three Laser parameters namely laser power, laser feed speed, and sample rotation speed were selected to evaluate their influence on the depth of the hardened zone, and the maximum surface hardness. Mathematical models were developed as a function of these three parameters and the analysis of variance (ANOVA) was used to conduct the statistical study. Microhardness measurements revealed three distinct regions in the heat-affected zone (HAZ) of all samples. The hardened zone (Z 1 ) near the surface with the highest value of hardness, the hardness loss zone (Z 2 ) where hardness started to decrease, and the overheated zone (Z 3 ) adjacent to the core with hardness values that were less than the base metal. Based on experimental measurements, a maximum surface hardness of 60.8 HRC was attained. Furthermore, the maximum depth of the hardened zone was observed as 500 µm. The microstructures of laser-hardened samples were studied using optical and scanning electron (SEM) microscopies. The hardened region seemed to have hard martensitic microstructure. By comparing the predicted and measured data for maximum microhardness values it was revealed that the models represent the experimental values with correlations close to 100%.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.095
GPT teacher head0.422
Teacher spread0.328 · 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
Published2022
Admission routes1
Has abstractyes

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