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Record W2758313802 · doi:10.5006/c2017-09636

Optimizing Composition, Fabrication, and Inspection of Chromium Carbide Overlay (CCO) for Oil Sands Sliding and Impact Wear Applications

2017· article· en· W2758313802 on OpenAlexaffabout
Duane Serate, Jeff Liu, Ningyu Wang, Hugo Caouette-Fritsch, Robinson Gnanadurai R, Satya Kompally

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsOverlayFabricationChromiumChromium carbideMaterials scienceCarbideMetallurgyComputer science

Abstract

fetched live from OpenAlex

Abstract Chromium Carbide Overlay (CCO) has long been used in Canadian oil sands mining industry to protect process equipment and piping from severe abrasion, erosion, and erosion/corrosion. However, CCO with poor quality has led to many premature failures in the field service, and resulted in high production loss and maintenance cost for oil sands operators. Most of the failures were due to the spallation of CCO under sliding/impact wear. Extensive lab tests and assessments in conjunction with technical studies/literature reviews were applied toward common commercial CCO products to further understand the effects of overlay composition and welding parameters on underbead cracking, mechanical properties, and wear/abrasion resistance. The study revealed that the extent of underbead cracking is related to the welding heat input, consequently affecting the spallation resistance of CCO. Outcomes of this research include a proposed guideline on how to improve CCO performance under sliding and impact wear by optimizing composition, fabrication, and non-destructive examination (NDE) techniques, including development of an NDE procedure with respect to underbead cracking.

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 score0.165
Threshold uncertainty score0.314

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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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