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Record W2226135235 · doi:10.2172/1126492

Energy Saving Melting and Revert Reduction Technology (Energy-SMARRT): Clean Steel Casting Production

2013· report· en· W2226135235 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsNatural Resources Canada
FundersU.S. Department of Energy
KeywordsFoundryNozzleCastingAir entrainmentMaterials scienceMetallurgyScrapMechanical engineeringEngineeringEnvironmental scienceComposite material

Abstract

fetched live from OpenAlex

Inclusions in steel castings can cause rework, scrap, poor machining, and reduced casting performance, which can obviously result in excess energy consumption. Significant progress in understanding inclusion source, formation and control has been made. Inclusions can be defined as non-metallic materials such as refractory, sand, slag, or coatings, embedded in a metallic matrix. This research project has focused on the mold filling aspects to examine the effects of pouring methods and gating designs on the steel casting cleanliness through water modeling, computer modeling, and melting/casting experiments. Early in the research project, comprehensive studies of bottom-pouring water modeling and low-alloy steel casting experiments were completed. The extent of air entrainment in bottom-poured large castings was demonstrated by water modeling. Current gating systems are designed to prevent air aspiration. However, air entrainment is equally harmful and no prevention measures are in current practice. In this study, new basin designs included a basin dam, submerged nozzle, and nozzle extension. The entrained air and inclusions from the gating system were significantly reduced using the new basin method. Near the end of the project, there has been close collaboration with Wescast Industries Inc., a company manufacturing automotive exhaust components. Both computer modeling using Magma software and melting/casting experiments on thin wall turbo-housing stainless steel castings were completed in this short period of time. Six gating designs were created, including the current gating on the pattern, non-pressurized, partially pressurized, naturally pressurized, naturally pressurized without filter, and radial choke gating without filter, for Magma modeling. The melt filling velocity and temperature were determined from the modeling. Based on the simulation results, three gating designs were chosen for further melting and casting experiments on the same casting pattern using the lip pouring method. It was observed again that gating designs greatly influenced the melt filling velocity and the number of inclusion defects. The radial choked gating showed improvements in casting cleanliness and yield over the other gatings, even though no mold filters were used in the gating system.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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