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Record W2140961915 · doi:10.1109/ias.1999.800012

A virtual rolling mill for real time control system tuning, operator training and process simulation

2003· article· en· W2140961915 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
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsQUAD Engineering (Canada)
Fundersnot available
KeywordsMillProcess (computing)Operator (biology)Control systemProcess controlControl (management)Rolling millComputer scienceEngineeringControl engineeringSimulationMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Process simulation is an important topic for control and process engineers. It can be shown that a simplified virtual rolling mill can be adapted for real-time control system tuning, operator training and process simulation. This paper outlines an approach to fully exploit those potentials using an example. Real-time simulation of a mill stand in response to rolling and material transportation and accumulation is presented. Utilization of this model for tuning and testing of a master reference control system developed for a steel mill is described. In addition, operator training was conducted based on the virtual mill developed. Through integration, a complete mill control system and operator training tool and process simulation system based on a virtual mill was developed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.384

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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