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Record W2068400621 · doi:10.1080/19397038.2010.542835

Assembly operator training and process planning via virtual systems

2011· article· en· W2068400621 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 Sustainable Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsFanshawe College
Fundersnot available
KeywordsVirtual machineProcess (computing)Virtual realityVirtual actorComputer scienceOperator (biology)Systems engineeringHuman–computer interactionSimulationProcess managementEngineering

Abstract

fetched live from OpenAlex

In this paper, we present an integrated intuitive system for assembly operators training and assembly process planning by combining virtual reality with motion-tracking technologies. The developed conceptual prototype for assembly planning and training enables individuals to interact with a virtual environment in real time. It extends the new technologies of motion tracking and integrates them with virtual environment technologies to create real-time virtual work cell simulations in which assembly operators may be immersed with hands-on experiences. In addition to operators training, the experimental results to date are presented to demonstrate the potential contributions of human skills in achieving effective assembly planning including disassembly operations. It is expected that this approach will lead to environment-friendly and sustainable operations by conserving energy and cost that are first tested in a human-emerged virtual 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 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 score0.451
Threshold uncertainty score0.538

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 communication0.0000.001
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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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