Thermal Simulation of Big Area Additive Manufacturing
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".