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Record W2101166479

Electroplating Line Flexible Control using P-Time Petri Nets Modeling and Hoist Waiting Times Calculation

2008· article· en· W2101166479 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

VenueInternational journal of industrial engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsHoist (device)Petri netScheduling (production processes)Computer scienceReal-time computingReliability engineeringSimulationControl theory (sociology)EngineeringDistributed computingOperations managementControl (management)Mechanical engineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In automated electroplating lines, product quality depends on soak times in chemical tanks while line throughput depends on hoist moves cycle time. These parameters are antagonistic since on-line tuning of cycle time interferes with processing duration and thus quality, and vice versa. Furthermore, on-line tuning actions performed without exploiting process flexibility may affect hoist moves schedule feasibility and call for complex scheduling at the on-line level. In this paper a flexible control for electroplating lines (EPL) is proposed that allows quality and throughput tuning within calculated margins and with no need for hoist moves rescheduling. Firstly a P-time Petri Nets (P-time PNs) tool is used to model hoist move sequence. Afterwards, linear programs (LP) are proposed to determine cycle time and soak times tuning margins without the need to reschedule hoist moves. Flexibility will be achieved using empty-hoist wait times.

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 score0.395
Threshold uncertainty score0.728

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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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