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Record W1932201631 · doi:10.1109/aim.1997.652898

Modeling system integration of manufacturing automation

2002· article· en· W1932201631 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
TopicEngineering Technology and Methodologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutomationGrippersSystem integrationComputer scienceRedundancy (engineering)RobotManufacturing engineeringSystems engineeringComputer-integrated manufacturingMachine toolSoftwareSoftware engineeringEngineeringArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

Summary form only given. This paper introduces a formal model for integration of automation systems with manufacturing systems. The proposed model provides a means to algorithmically represent and manipulate the various activities, so that system integration can be achieved with minimum redundancy. Automation systems involve various devices such as processing machines and handling or transfer mechanisms, in particular, robots with dexterous hands or custom built grippers. The manufacturing systems consist of machine tools for metal cutting, metal forming, assembly, and various other operations including inspection. Design of automation systems, and the different manufacturing systems are generally conducted independently, and then they are integrated with each other. The model developed in this paper provides a systematic means to represent the activities involved in the various operations, and manipulate them to explore the integrated solutions. The approach holds significant promise as it forms a basis for the development of software tools for system integration.

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: none
Teacher disagreement score0.611
Threshold uncertainty score0.199

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 head0.049
GPT teacher head0.235
Teacher spread0.187 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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