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Record W2176117138

Estudio numérico del tratamiento termoquímico de carburizado y temple de una flecha de transmisión empleando diseño de experimentos

2015· article· es· W2176117138 on OpenAlexaff
E. Rodríguez-Morales, J.J. Montes-Rodríguez, A.G. Luna-Bustamante, D. Balderas-Puga, Cristian Luna-González

Bibliographic record

VenueRedalyc (Universidad Autónoma del Estado de México) · 2015
Typearticle
Languagees
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

El tratamiento termoquimico de carburizado y temple se aplica extensivamente en la industria de fabricacion de autopartes. Cuando el tratamiento se aplica en mas de una etapa, su estudio por medio de experimentos se hace demasiado extenso por la gran cantidad de combinaciones de las variables de tratamiento que pueden obtenerse. Una alternativa de estudio viable es simular numericamente el proceso y realizar experimentos virtuales, lo cual, en conjunto con la aplicacion de tecnicas estadisticas de diseno de experimentos, permite profundizar en el conocimiento del mismo a costos relativamente bajos, para encontrar asi condiciones de tratamiento mas favorables que repercuten en la disminucion de costos de produccion. En el presente estudio se simularon, con el paquete computacional comercial COMSOL Multiphysics®, las dos etapas de carburizado, de un proceso en cuatro etapas que incluye temple, de una flecha de transmision, obteniendose resultados que fueron validados por comparacion con mediciones de dureza realizadas en piezas de produccion tratadas, aplicando posteriormente un diseno de experimentos en base a un arreglo de Taguchi. Se obtuvieron asi resultados de simulacion para una variedad de combinaciones de parametros de tratamiento, resaltando aquellas que implican una disminucion de los costos de tratamiento

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.405
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.258
Teacher spread0.235 · 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.

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
Published2015
Admission routes1
Has abstractyes

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