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Record W4206262646 · doi:10.31224/osf.io/cuhdr

Simulation and automation of aluminum panel shot peen forming

2022· preprint· en· W4206262646 on OpenAlexafffund
Vladislav Sushitskii, Pierre-Olivier Dubois, Hong Miao, Martin Lévesque, Frédérick P. Gosselin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLaser and Thermal Forming Techniques
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesPolytechnique Montréal
KeywordsShot (pellet)AutomationAluminiumOne shotEngineeringComputer scienceMaterials scienceMetallurgyMechanical engineering

Abstract

fetched live from OpenAlex

We present a methodology for automated forming of metal plates into freeform shapes using shot peening. The methodology is based on a simulation software that computes the peening pattern and simulates the effect of its application. The pattern generation requires preliminary experimental characterization of the treatment. The treatment is applied by a shot peening robot. The program for the robot is generated automatically according to the peening pattern. We validate the methodology with a series of tests. Namely, we form nine aluminum plates into doubly curved shapes and we also shape model airplane wing skins. The article describes the complete workflow and the experimental results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.285
Teacher spread0.241 · 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 designSimulation or modeling
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
Published2022
Admission routes2
Has abstractyes

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