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Record W3067746826 · doi:10.1016/j.procir.2020.05.185

Geometric Tolerance Characterization of Laser Powder Bed Fusion Processes Based on Skin Model Shapes

2020· article· en· W3067746826 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.

Bibliographic record

VenueProcedia CIRP · 2020
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of AlbertaAlberta Energy
Fundersnot available
KeywordsGeometric dimensioning and tolerancingDimensioningFusionGeometric shapeGeometric modelingCharacterization (materials science)ShrinkageMaterials scienceGeometric designComputer scienceMechanical engineeringBiological systemEngineering drawingGeometryMathematicsEngineeringComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Geometric tolerance characteristics of metal additive manufactured (AM) parts play a significant role in ensuring the part functionality. In such cases, prior estimation of geometric tolerances, i.e. geometric dimensioning and tolerancing (GD&T) characteristics, can prove vital to reduce part rejection and to minimize material wastage and cost. This article presents a framework to estimate geometric tolerances in laser powder bed fusion (LPBF) processes. For a given geometry, skin model shapes are generated based on material shrinkage and thermo-mechanical simulation. Samples from skin model shapes are utilized for geometric tolerance estimation. A case study is presented to validate the developed framework and demonstrate its applicability in metal AM.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.192
Teacher spread0.180 · 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