Efficient Prediction of Fatigue Damage Analysis of Carbon Fiber Composites Using Multi-Timescale Analysis and Machine Learning
Satoru Yoshimori, Jun Koyanagi, Ryosuke Matsuzaki · 2024
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Description
Satoru Yoshimori, Jun Koyanagi, and Ryosuke Matsuzaki present, in twenty twenty-four, a faster way to predict fatigue damage in carbon fiber plastic. That material is light and strong, but its damage paths are complex. Their base model uses entropy, a measure tied to heat capacity, to gauge damage without knowing the load history. That model is accurate but slow, too slow for many repeated cycles. They speed it up by swapping cyclic loads for a steady push in the model, a trick called multi-timescale analysis.
They then tune key variables with machine learning and with a classic surface method. The learning method cut error sharply and slashed run time by more than ninety percent, while keeping accuracy.
Catalog record
| CREATOR | Satoru Yoshimori, Jun Koyanagi, Ryosuke Matsuzaki |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 22 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |