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Record W2213084762 · doi:10.4271/2000-01-1000

Cost Reduction Processes for Transfer Case Sprockets: Part 1: Metallurgical Aspects

2000· article· en· W2213084762 on OpenAlexaff
François Chagnon, Jeffrey A. Danaher, Edmond Ilia, Steve Krise

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsFédération des Comités de Parents du Québec
Fundersnot available
KeywordsReduction (mathematics)Cost reductionComputer scienceSprocketMaterials scienceMechanical engineeringEngineeringMathematicsBusinessGeometry

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Transfer case sprockets usually require quenching to improve hardness and mechanical properties. This additional process step can be avoided with sinter hardening. Indeed, sinter hardening allows the production of P/M parts with high strength and apparent hardness directly from sintering because the martensitic transformation takes place during the cooling portion of the sintering operation. Therefore, this process eliminates the need for a post-sintering heat treatment with all the inherent related problems such as part distortion, oil contamination and added processing costs. Many low alloy steel powders have been developed for sinter hardening applications. These materials, combined with the availability of sintering furnaces equipped with enhanced cooling capacity, make sinter hardening particularly attractive for parts that are difficult to quench because of their size and shape. Moreover, the powder mix formulation and processing conditions can be tailored to achieve a specific microstructure.</div> <div class="htmlview paragraph">The objective of this paper is to review the metallurgical aspects involved in sinter hardening and to relate these to the characteristics of the base powder, admixed additions and processing conditions in order to develop appropriate mix formulations for the production of transfer case sprockets.</div>

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), Insufficient payload (model declined to judge)
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 score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.247
Teacher spread0.225 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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