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Inferring the surface roughness of Al-Si coated 22MnB5 steel using an in situ laser speckle characterization technique

2020· article· en· W3110334199 on OpenAlex
C.M. Klassen, Johannes Emmert, Kyle J. Daun

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceIntermetallicHot stampingMetallurgyAluminiumSurface roughnessCoatingSpeckle patternSurface finishWeldabilityComposite materialSiliconLaserProfilometerWeldingOpticsAlloy

Abstract

fetched live from OpenAlex

Abstract Hot stamping of aluminium-silicon (Al-Si) coated 22MnB5 steel blanks is widely used in the automotive industry to produce light yet crashworthy parts. However, the coating melts at ∼577°C and transforms into a rough intermetallic layer as iron from the base steel diffuses towards the surface. The blank surface roughness impacts the radiative properties during heating as well as weldability, paint adhesiveness, and cooling rate during forming and quenching. This study pioneers the use of laser speckle patterns, caused by the constructive and destructive interference of collimated light reflected off the blanks, to infer the evolving surface roughness of Al-Si coated steel coupons in situ . The results reveal a significant increase in surface roughness once intermetallic compounds reach the surface and that higher furnace set-points produce rougher parts.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.234
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