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Record W4388944075 · doi:10.4028/p-12dudu

Buckling Mechanism Simulation for Thin-Wall Components Made by Laser Powder Bed Fusion

2023· article· en· W4388944075 on OpenAlexaff
Reza Tangestani, Apratim Chakraborty, Trevor Sabiston, Lang Yuan, Étienne Martin

Bibliographic record

VenueKey engineering materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsPolytechnique MontréalUniversity of Waterloo
Fundersnot available
KeywordsBucklingResidual stressMaterials scienceFusionFinite element methodParametric statisticsStructural engineeringComposite materialStress (linguistics)Eigenvalues and eigenvectorsFailure mode and effects analysisMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The effect of part geometry on premature thin wall part failure in laser powder bed fusion (LPBF) is investigated using FEM simulation. Two FEM models are used to simulate the residual stress and buckling modes. Two experimental parts with different lengths are used for model validations. A LPBF FEM model evaluates the residual stress associated with the two experimental parts. A parametric buckling model is developed to determine the eigenvalues for 100 different part geometries including different part lengths (20-60 mm), widths (0.5-2 mm), and heights (10-50 mm). The results show that thin wall parts are more susceptible to buckling mode 1 when part length is small and to a combination of mode 1 and 3 when part length increases. In both cases the threshold stress for buckling is mostly sensitive to part thickness and height.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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