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Record W3152937796

Development and Calibration of a 3-component Force Sensor for Meso-milling

2015· dissertation· en· W3152937796 on OpenAlexfundno aff
Yu Hui Feng

Bibliographic record

VenueTSpace · 2015
Typedissertation
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsCalibrationComponent (thermodynamics)Bandwidth (computing)PiezoelectricityEngineeringDevelopment (topology)Piezoelectric sensorElectronic engineeringComputer scienceControl engineeringMechanical engineeringElectrical engineeringPhysicsTelecommunicationsMathematics
DOInot available

Abstract

fetched live from OpenAlex

In the Autonomous Systems and Biomechatronics Laboratory (ASBLab), a concept of a novel three-component piezoelectric force sensor has been designed in order to achieve the requirements for meso-milling applications. The sensor is designed to have a minimum resolution of 10 mN, an operational frequency bandwidth of 8.3 kHz and a measuring range of up to 10-15 N. This thesis focuses on the development of a prototype of the force sensor design, including the design for physical development of the force sensor, the development of its amplification system and its calibration methodology. The result from the calibration test demonstrates that the prototype sensor developed has the required specifications for the meso-milling applications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.303
Teacher spread0.283 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations1
Published2015
Admission routes1
Has abstractyes

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