A Machine Learning Approach to Find Density Percentage Error Resulting by Infill Patterns in Additive Manufacturing
Bibliographic record
Abstract
This paper presents employing Machine Learning in predicting the error involved in the output density of the additive manufactured parts. Due to the fact that the density of a 3D part is related to the filled volume inside the body, the characteristics of infill lines become important. The existence of density percentage error is proven in previous studies, and it shows the deviation of actual infill density from the desired input density requested by the user. Since the amount of density error is different for various setups of infill parameters, being able to predict the density percentage error without going through the full calculation provides the base for infill setup optimization with the objective to minimize this error. A procedure is shown on how to work on the density error, by selecting a 2D infill pattern, recognizing the corresponding input parameters, and set up the error estimating. This study represents density percentage error prediction for a generic cubic model using a neural network. The developed results demonstrate the accuracy of the implemented neural network in predicting the density percentage error while the infill input parameters are changed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".