Predicting Abrasion Resistance in Thermoplastic Polyurethanes Using Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Abrasion resistance is a critical property of thermoplastic polyurethane (TPU), especially for applications in automotive, footwear, and medical devices where durability under mechanical stress is essential. Conventional testing methods for abrasion, such as ISO 4649, are time‐consuming and require specialized equipment. This study introduces a machine learning (ML)‐based predictive framework to estimate TPU abrasion volume loss using mechanical and structural property data extracted from the CAMPUS database. Key features including viscoelastic moduli, tensile and tear strength, Shore hardness, and density were selected through Pearson correlation analysis. Six supervised regression models were developed and evaluated: Linear, Polynomial, Decision Tree, Random Forest, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR). SVR achieved the highest R 2 (0.79) on testing data, but its poor training performance indicated underfitting and sensitivity to data sparsity. In contrast, GBR demonstrated more consistent and generalizable predictions, achieving an R 2 of 0.72 and RMSE of 5.2 mm 3 . The results underscore the potential of ML algorithms to model structure–property relationships in elastomers. This approach offers a cost‐ and time‐efficient alternative for early‐stage screening of TPU grades and accelerates the design of abrasion‐resistant polymer formulations.
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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 | 0.001 | 0.001 |
| 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 it