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Enhancing the quality inspection process in the food manufacturing industry through automation

2022· article· en· W4283750660 on OpenAlexfundno aff
Chengsi Lin, Muhamad Arfauz A. Rahman, Paul Maropoulos

Bibliographic record

Venue2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) · 2022
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsAutomationManufacturing engineeringProduction lineQuality (philosophy)Process (computing)Production (economics)Product (mathematics)Automated optical inspectionManufacturingManufacturing processComputer scienceEngineeringEngineering drawingBusinessMechanical engineering

Abstract

fetched live from OpenAlex

Automatic inspection system refers to the use of various inspection instruments to measure, indicate or record the main process parameters of the production process system. In the Fourth Industrial Revolution (Industry 4.0) era, competition among enterprises has become increasingly fierce. Most enterprises use automated testing to replace traditional manual testing to improve production efficiency and product quality. This paper investigates the current inspection process in the manufacturing industry. It proposes and designs an automated inspection system for sandwich filling coverage and thickness for sandwich production to replace the original manual inspection. After simulation and comparison of the improved production line, it was finally verified that the modified production line could improve the yield and quality of sandwiches and can help manufacturers save human resources.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.544

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.296
Teacher spread0.260 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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