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Record W2095661645 · doi:10.5194/ms-4-357-2013

On understanding of design problem formulation for compliant mechanisms through topology optimization

2013· article· en· W2095661645 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

VenueMechanical sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilEast China University of Science and Technology
KeywordsTopology optimizationCompliant mechanismComputer sciencePoint (geometry)Topology (electrical circuits)Path (computing)Function (biology)Mathematical optimizationEngineeringMathematicsFinite element methodStructural engineeringProgramming language

Abstract

fetched live from OpenAlex

Abstract. General problems associated with the design of compliant mechanisms through the topology optimization technique are defined in this paper due to the lack of comprehensive definitions for these problems in the literature. Standard design problems associated with rigid body mechanisms, i.e. function generation, path generation and motion generation, are extended to compliant mechanisms. Functional requirements and the associated 25 formulations in the literature are comprehensively reviewed along with their limitations. Based on whether the output is controlled quantitatively or not, these formulations are categorized into two types: (1) formulations for quantitative design; and (2) formulations for qualitative design. In addition, formulations that aim to solve the point flexure problem are also discussed. Future work is identified based on the discussion of each topic.

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: Methods · Consensus signal: none
Teacher disagreement score0.530
Threshold uncertainty score0.393

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 head0.070
GPT teacher head0.271
Teacher spread0.200 · 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