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Record W4367665830 · doi:10.23977/jeeem.2023.060202

Design and Implementation of Bolt Feeding Device and Bolt Assembly System

2023· article· en· W4367665830 on OpenAlexvenueno aff
Shiyong Li, Chunming Zhang, M. Q. Shuai, Junzhen Gong, Siyang Wang, Peicun Liu

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2023
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSiloAnchor boltEngineeringHead (geology)Structural engineeringPickupChannel (broadcasting)Power (physics)TOPSMechanical engineeringComputer scienceElectrical engineeringGeology

Abstract

fetched live from OpenAlex

In this paper, a bolt feeding device and a bolt assembly system are designed and manufactured. The bolt feeding device includes: incoming parts, including a material channel, which is used to accommodate bolts; the feeding part includes a silo and a first power part. The silo is equipped with a receiving hole which can be opposite to the material channel. The bolts in the material channel can be moved to the receiving hole. The first power part is used to push the bolts in the receiving hole, so that the head of the bolt and the silo are set in the axial clearance. The above scheme can facilitate the stable pickup of bolts by downstream equipment, and can largely avoid the falling of bolts during the transfer of downstream equipment, so as to facilitate the automatic assembly of bolts.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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