Jun-Kai Niu

Adaptive physics-informed neural network framework for PTFE wear prediction through Archard-guided physical embedding

Accurate prediction of polytetrafluoroethylene (PTFE) wear volume from measured operating and surface-topography data is important for evaluating wear evolution in frictional components. However, traditional wear laws often use a simplified representation of the coupling between operating conditions and surface morphol...