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Record W2550267223 · doi:10.1139/tcsme-2013-0011

DESIGN OF A SONIC DRILL BASED ON VIRTUAL PROTOTYPE TECHNOLOGY

2013· article· en· W2550267223 on OpenAlexvenueno aff
Yu Wang, Baolin Liu, Qin Zhou, Yuanbiao Hu, Guomin Li

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsnot available
Fundersnot available
KeywordsKey (lock)Virtual prototypingComponent (thermodynamics)DrillComputer scienceSampling (signal processing)SoftwareDesign cycleSimulationEngineeringMechanical engineeringSystems engineering

Abstract

fetched live from OpenAlex

Sonic drilling is a novel sampling technology applied in many fields. Based on SolidWorks software, a dynamics virtual prototyping is utilized to develop a physical sonic driller by demonstrating collision detection and optimizing overall layout and key component structures. Key hydraulic and structural parameters are optimized using a mathematical model. Search for the optimized parameters and operating conditions for this multi-body mechanical system is conducted experimentally. The practical results show that the virtual prototype technology not only shortens the design cycle but also improves the quality of the conventional design. The new sonic driller designed by this method is both environmentally friendly and smarter.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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