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Record W3103772229 · doi:10.11159/ffhmt20.111

Thermal Simulation of Big Area Additive Manufacturing

2020· article· en· W3103772229 on OpenAlexvenueno aff
Brian K. Friedrich, Kyosung Choo

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSlumpingAcrylonitrile butadiene styreneThermalLayer (electronics)ImplosionPyramid (geometry)Materials scienceHeat transferMechanical engineeringOpticsEngineeringMechanicsGeologyMeteorologyComposite materialPhysics

Abstract

fetched live from OpenAlex

A common failure mode of Big Area Additive Manufacturing (BAAM) is the phenomena of slumping.Slumping occurs when the structure retains excessive heat, often seen when there is insufficient cooling between layers.This study developed a transient thermal simulation model to aid in predicting the slumping phenomena, specifically in overhanging features.The simulation was modeled in ANSYS where the walls were created to match the dimensions in the experimental pyramid at 12.5 mm wide with a thickness of 5 mm.The structures overall size was 1.06 m by 0.77 m and 25 layers tall.Each layer was created independently to allow for element birth/death commands and for individual layer mesh parameters.Using the built-in element birth/death commands each layer would be inserted on top of the previous layer.As each new layer is activated a temperature input of 202°C is applied then subsequently turned off as the next layer is activated.The printing material, ABS (Acrylonitrile Butadiene Styrene), properties and heat transfer coefficient of the structures are functions of temperature.The simulation model is compared to an experimentally measured part.A FLIR E60 thermal imaging camera is utilized to capture the vertical thermographic profile of the build.The camera was paired with a computer running the FLIR Tools software package in order to record, save, and later analyze the thermographic history.The thermal images also captured three different vertical lines traversing all layers.Each pixel in the lines would record the corresponding temperatures of the structure.The data taken from the three lines show that the cooling present in the structure is of an exponential form.This result matches what was produced from the simulation, within 5 % error.The simulation allows for dwell times to be adjusted in the model until failure is no longer predicted.Utilizing these transient thermal modeling techniques will aid BAAM designers to identify potential slumping during the print process.

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: none
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.033
GPT teacher head0.222
Teacher spread0.189 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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