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Record W2376880377

Compliant Mechanism Design for IOL Haptics Using Topology Optimization

2015· article· en· W2376880377 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

VenueJournal of Beijing University of Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsTopology optimizationCompliant mechanismMechanism (biology)Displacement (psychology)KinematicsHaptic technologyFinite element methodTopology (electrical circuits)Lens (geology)Computer scienceEngineeringStructural engineeringSimulationPhysicsClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

Based on the biological data and adjustment mechanism of human eyes,a biomechanical model was established. In which the haptics of intraocular lens were analyzed by structural topology optimization method,and then a basic framework structure was obtained. The results of kinematics analysis of the mechanism show that the maximum displacement of target element reaches the expected goal. A fully compliant mechanism was conducted in this paper by using finife element method,followed by displacement analysis and stress analysis. The analysis confirms that the mechanism design is capable of amplifying motion,and indicates that structure of haptics can be improved or designed by using topology optimization method,finite element method and design theory of intraocular lens. The output displacement of compliant mechanism is 3. 34 times higher than that of input after topology of this model.Meanwhile the forward displacement of optical part is increased and the visual quality can be improved theoretically as well.

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 score0.526
Threshold uncertainty score0.385

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 head0.042
GPT teacher head0.220
Teacher spread0.178 · 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