Uncertainty Quantification in Material Properties of Additively Manufactured Materials for Application in Topology Optimization
Bibliographic record
Abstract
Abstract This research presents an approach to measuring the inherent randomness in properties of materials fabricated by the fused filament fabrication (FFF) method. Defects associated with the layer-by-layer process introduce significant variability in the elastic modulus field of materials printed. To describe the random distribution in Young’s modulus fields, statistical properties of mean, variance, and correlation length must be estimated for bulk regions (the printed filaments) and fusion regions (the thin regions connecting printed filaments). The goal is to estimate the random properties from the surface strain fields calculated by digital image correlation (DIC) analysis. A machine learning algorithm is developed that can estimate the spatial variations in the elastic modulus. The model is trained on a dataset of simulated two-dimensional strain fields with known random distributions in the corresponding elastic modulus fields generated by finite element (FE) simulations. On the test data, we achieved the R2 score of 0.93 and 0.95 for the mean in the bulk and fusion Young’s modulus fields, respectively. Also, for the variance in bulk and fusion areas, the R2 score of 0.74 and 0.83 are achieved, respectively. The results demonstrate the feasibility of the proposed approach in measuring the randomness in material properties of FFF-based printed materials.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".