Predicting stress–strain relationships of additively manufactured materials under compression and tension using transfer learning and Wasserstein distance-based dataset pruning
Bibliographic record
Abstract
Additive manufacturing allows one to design materials and structures with tunable mechanical properties. However, predicting the stress–strain relationships of additively manufactured materials remains a challenge due to complex process-structure–property relationships in additive manufacturing. While machine learning has been used to predict stress–strain relationships, it usually requires large volumes of training data, which is time consuming and expensive. To address this issue, a generic transfer learning (TL) framework that integrates distance-based dataset pruning (DP) is developed. Four distance metrics, including Euclidean, Cosine, Mahalanobis, and Wasserstein distances, are used to prune redundant data in the source domain by determining the distance between the source domain and the training dataset in the target domain. Only the most representative stress–strain curves in the source domain are retained to pre-train a long short-term memory model. We demonstrate the TL framework on two datasets, including fused filament fabrication (FFF) fabricated polylactic acid (PLA) compressive dataset and laser powder bed fusion (L-PBF) fabricated AlSi10Mg tensile dataset. Experimental results show that TL integrated with Wasserstein distance-based DP achieves the best predictive performance and computational efficiency, with the average mean absolute percentage error of 15.18 % for Case 1 and 19.51 % for Case 2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".