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Record W4206132962 · doi:10.1016/j.ohx.2022.e00263

A low-cost open-source automated shot peen forming system

2022· article· en· W4206132962 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHardwareX · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsShot (pellet)AutomationAerospaceComputer scienceDistortion (music)EngineeringManufacturing engineeringMechanical engineeringTelecommunicationsAerospace engineering

Abstract

fetched live from OpenAlex

The aerospace industry relies on shot peen forming to form sheet metal parts and correct distortion in machined parts. The shot peen forming community is developing simulation and planning methods to meet the industrial need for automation. Researchers need to invest in expensive industrial robots contained in large blasting cabinets to perform the experimental validations of their proposed methods. To bypass this need for expensive industrial-grade equipment, this work presents a low-cost shot peen forming system that enables researchers to validate their methods experimentally on small samples. Conventional installations can cost a few 100,000 USD while the proposed prototype only costs 400 USD to produce. Such a prototype can operate on a tabletop with conventional electrical installations. The use of small samples for validation further reduces the cost of experiments and allows for faster development.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.025
GPT teacher head0.269
Teacher spread0.244 · 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