MétaCan
Menu
Back to cohort

Microstructural and Mechanical Characterization of Gray Cast Iron and AlSi Alloy after Laser Beam Hardening

2010· article· en· W2073897582 on OpenAlexaff
Waldemar Alfredo Monteiro, E.M.R. Silva, Luanna Vitória Neris Silva, Wagner de Rossi, S.J. Buso

Bibliographic record

VenueMaterials science forum · 2010
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsCanadian Linguistic Association
Fundersnot available
KeywordsMaterials scienceMicrostructureGray ironAlloyScanning electron microscopeIndentation hardnessHardening (computing)LaserCast ironMetallurgyEnergy-dispersive X-ray spectroscopyComposite materialWork hardeningLayer (electronics)Optics

Abstract

fetched live from OpenAlex

A localized source of heat, such as that of laser beam, can provide a convenient means of producing a surface layer of altered microstructure. By using surface hardening treatment, wear resistance can be increased. Experiments were performed using a Nd:YAG pulsed laser under different processing conditions. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray mapping (SEM) were employed to observe the effect of laser melting treatment on the microstructural properties of the samples. Depending on the selected laser treatment working conditions, different microstructures characteristics of surface melting can be achieved in the treated zone. Higher microhardness values were found at the treated area showing a superficial hardening of the sample and, consequently, an improvement of the wear resistance of these automotive alloys. The aim of this work is to find the optimal process parameters and to evaluate the characteristics of the laser superficial hardening (LSH) in a pearlitic gray iron and Al-Si alloy used in an automobile industry (bearing and piston materials in automotive industry).

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.410

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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicHigh Entropy Alloys StudiesFrench-language works237,207