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Record W2082259159 · doi:10.1115/1.2952819

Tool Feasibility Analysis for Fixture Assembly Planning

2008· article· en· W2082259159 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFixtureWorkspaceFastenerComputer sciencePlan (archaeology)Engineering drawingMachine toolEngineeringMechanical engineeringArtificial intelligenceRobot

Abstract

fetched live from OpenAlex

Tool feasibility is a critical issue for generating a complete fixture assembly plan to reduce production setup time. Previous fixture design systems rarely consider the assembly tool feasibility. Current methods of assembly tool feasibility analysis mainly depend on simulation-based or user-interactive approaches, which rely on users’ judgment. This paper presents a new approach to tool feasibility analysis for fixture assembly planning. The fixture workspace around a tool is represented by a newly defined global accessibility sphere with depth of a truncated half-line. The assembly tools are modeled as five articulated parts to fully describe the tool characteristics. Tool feasibility analysis is executed to verify assembly tools’ feasibility applied on a fastener. In particular, both tool motion and tool placement constraints during tool applications are integrated into the tool geometric reasoning. The example demonstrates the fast computing speed and intuitive simulation of several assembly tools applied in fixture assembly.

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)0.001
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 score0.362
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.237
Teacher spread0.218 · 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