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Record W1984276749 · doi:10.1364/ao.48.000c11

Laser photothermal radiometric instrumentation for fast in-line industrial steel hardness inspection and case depth measurements

2008· article· en· W1984276749 on OpenAlexaff
Xinxin Guo, Konesh Sivagurunathan, José Ángel García García, Andreas Mandelis, Salvatore Giunta, Salvatore Milletari

Bibliographic record

VenueApplied Optics · 2008
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceOpticsLaserPhotothermal therapyInstrumentation (computer programming)Nondestructive testingPhysics

Abstract

fetched live from OpenAlex

A contact-free, nondestructive laser photothermal radiometric instrumentation technique was developed to meet industrial demand for on-line steel hardness inspection and quality control. A series of industrial steel samples, flat or curvilinear, with different effective hardness case depths ranging between 0.21 and 1.78 mm were measured. The results demonstrated that three measurement parameters (metrics) extracted from fast swept-sine photothermal excitation and measurements, namely, the phase minimum frequency f(min), the peak or trough frequency width W, and the area S, are complementary for evaluating widely different ranges of hardness case depth: f(min) is most suitable for large case depths, and W and S for small case depths. It was also found that laser beam angular inclination with respect to the surface plane of the sample strongly affects hardness measurement resolution and that the phase frequency maximum is more reliable than the amplitude maximum for laser beam focusing on the sample surface.

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)0.000
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: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.062
GPT teacher head0.244
Teacher spread0.182 · 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

Citations22
Published2008
Admission routes1
Has abstractyes

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