ML predicts Nitinol alloy wear across heat treatments in new tribology paper
Fowe · x · 2026-09-12
A new Journal of Materials Science study applies triboinformatic modeling to Nitinol (NiTi) alloys under different heat-treatment regimes, using machine learning to predict wear and friction while surfacing physically meaningful relationships. The authors also generalize the Archard law to capture nonlinear wear behavior across heat treatments using measured material properties—a step toward data-driven, interpretable tribological design.
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