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Record W4386750096 · doi:10.3390/engproc2023043015

Industry 4.0: Correlation Analysis Applied to the Hot Stamping of AA7075 B-Pillars Pre-Assembled Using Friction Stir Welding

2023· article· en· W4386750096 on OpenAlexafffund
Mohamad Idriss, Guillaume D’Amours, François Nadeau, Danick Gallant, Ryan Myers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsNational Research Council Canada
FundersOffice of Energy Research and Development
KeywordsStampingWeldingMaterials scienceDistortion (music)Friction stir weldingHot stampingWork (physics)Phase (matter)Composite materialMechanical engineeringMetallurgyEngineeringPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

In this study, 220 AA7075-T6 B-pillars were fabricated using a thoroughly instrumented hot-stamping press under varied conditions. Feature engineering work identified nineteen attributes of the hot-stamping process as impacting four characteristics of the obtained B-pillars: electrical conductivity (%IACS), mechanical strength, distortion, and the presence of visible defects. Pearson correlation suggests an important correlation between the heating phase and the mechanical strength, as well as the %IACS values. As for distortion, the influence of the stamping phase is more obvious. Finally, no correlation was obtained between the hot stamping attributes and the presence of visible cracks. This is mainly due to the pre-assembly phase, i.e., Friction Stir Welding, which will be considered in future works.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.278
Teacher spread0.257 · 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 designObservational
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

Citations1
Published2023
Admission routes2
Has abstractyes

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