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Record W2025687625 · doi:10.1115/imece2009-10416

Temperature Measurement in High Speed Cylindrical-Plunge Grinding Using Thermocouple

2009· article· en· W2025687625 on OpenAlexaff
Dahu Zhu, Beizhi Li, Jingzhu Pang, Jianguo Yang, Dan Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsThermocoupleGrindingTemperature measurementMaterials scienceArc (geometry)Mechanical engineeringGrinding wheelSIGNAL (programming language)DissipationTemperature gradientMechanicsMetallurgyAcousticsComposite materialComputer scienceEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Temperature measurement is employed for research in grinding and for process monitoring. Because of the difficulty in arrangement of thermocouples, temperature measurement in grinding presents a number of challenges, particularly in high speed cylindrical-plunge grinding. Based on existing literature, only one thermocouple is considered for measuring the maximum temperature in the grinding contact arc, without considering the overall temperature variation. In this paper, a new measurement method, named four K-type thermocouples are proposed aslant along the direction of the width of workpiece which is developed for measuring the overall contact arc in high speed cylindrical-plunge grinding. It is shown that the temperature increases to the maximum with a sharp gradient, then decreases due to the strengthening of cooling effect and the decrease of depth of cut, which is consistent with previous study. The measured temperature signal reveals the generation rule and dissipation rule of grinding heat. The influence of grinding parameters on the temperature is also discussed in detail in the paper.

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: Empirical
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.044
GPT teacher head0.277
Teacher spread0.233 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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