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Record W2026540911 · doi:10.1115/1.1461839

An Embedded Friction Sensor Based on a Strain-Gauged Diaphragm

2002· article· en· W2026540911 on OpenAlexaff
Aaron Dellah, Peter Wild, T. Moore, M. A. Shalaby, J. Jeswiet

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsDiaphragm (acoustics)Strain gaugeFinite element methodMechanical engineeringProcess (computing)Shear (geology)Structural engineeringSurface (topology)EngineeringMaterials scienceAcousticsComputer scienceVibrationComposite materialGeometryPhysics

Abstract

fetched live from OpenAlex

A new design of friction sensor, intended for use in industrial process applications, such as metal forming, has been developed. The sensor consists of a diaphragm whose outer surface is flush with the surface of the tool and whose inner surface is instrumented with strain gauges. Relative to previous sensor designs, this sensor offers reduced disturbance of the frictional interaction between the tool and the workpiece. A proof-of-concept prototype has been constructed and tested in a laboratory test apparatus. The results of testing validate the anticipated performance, based on finite element modeling. The sensor is able to measure both shear and normal forces and, of greater significance, is able to simultaneously differentiate between the two types of loading.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.207
Teacher spread0.198 · 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
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

Citations10
Published2002
Admission routes1
Has abstractyes

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