MétaCan
Menu
Back to cohort
Record W4403249565 · doi:10.1016/j.procir.2024.08.372

Structured-light 3D Scanning Performance in Offline and In-process Measurement of 3D Printed Parts

2024· article· en· W4403249565 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

VenueProcedia CIRP · 2024
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
Keywords3d printed3d scanningStructured lightProcess (computing)3D printingEngineering drawingMaterials scienceComputer scienceEngineeringManufacturing engineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a geometric analysis comparison of structured-light 3D scanners against coordinate measuring machines (CMMs) in measuring 3D printed plastic parts. The resulting geometric analysis of a 3D printed part measured with a contact probe on a CMM and measured with a structured light 3D scanner is presented, along with an error analysis that includes a statistical comparison of the measured geometric deviation. This analysis is then used to determine if structured-light 3D scanners are reliable enough to perform a GD&T analysis of specific features. This paper also presents the results of in-process 3D scanning and compares them to offline 3D scanning to determine the suitability of in-process 3D scanning for comprehensive analysis of geometric deviation and GD&T features.

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 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 score0.453
Threshold uncertainty score0.419

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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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