MétaCan
Menu
Back to cohort
Record W2104320220 · doi:10.1109/mdsp.1989.97029

Visual measurement of metal cutting tool wear

2003· article· en· W2104320220 on OpenAlexaff
David W. Capson, Clarence Wust

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEnhanced Data Rates for GSM EvolutionMachine toolMachiningTool wearInterferometryCutting toolComputer scienceBreakagePhase (matter)Mechanical engineeringComputer visionEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

Summary form only given, as follows. A machine vision system has been designed and constructed for automatic visual measurement of wear patterns on metal cutting tools used for machine shop lathes. Applications of the system include (1) adaptive control of the tool cutting path based on the tool wear to ensure high-precision machining and (2) prediction of tool breakage determined from wear patterns and cutting force measurements. Phase-stepping interferometry is used to project sinusoidal patterns onto the reflective surfaces of the cutting tool. Using multiple phase shifts of the patterns, three-dimensional information about the edge of the tool can be extracted from video images. The spatial resolution of the system is on the order of 2 to 5 mu m. A description of the system together with experimental results will be given.

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 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.053
GPT teacher head0.281
Teacher spread0.228 · 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 teacher head, 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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicOptical measurement and interference techniquesFrench-language works237,207