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Automatic Inspection System for Surface Defect of Precision Steel Ball

2014· article· en· W2049877863 on OpenAlexaff
Yi Ji Chen, Jhy-Cherng Tsai, Ya-Chen Hsu

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsBall (mathematics)Robustness (evolution)System of measurementEngineeringComputer scienceMechanical engineeringStructural engineeringMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

Precision steel ball is one of the most critical components for rolling transmission. As precision ball affects the performance of precision transmission system, fully inspection of these balls is an urgent need for the industry. This paper is to develop a real-time inspection system for surface defects of precision steel ball with fast and robust method and mechanism. The developed system consists of an optical measurement module as well as a mechanism module for full surface inspecting of the steel ball. The minimum defect and area can be detected by the developed system are 0.1mm and 0.01 mm2 respectively. The developed system has been testified against the designed specifications at speed higher than 3pc/sec and less than 0.5% missing rate. It verified the resolution, accuracy and robustness of the developed system which is capable for final defect inspection of steel balls for grade 100 bearing.

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)0.001
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 score0.253
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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 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
Published2014
Admission routes1
Has abstractyes

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