A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy

Documents similaires

• Fracture mechanism of Ti60 alloy at high temperature in very high cycle fatigue regime and fatigue life prediction
  par: Qikai Zhou, et autres
  Publié: (2024)
• The effect of texture on the very high cycle fatigue performance and deformation mechanism of rolled AZ31B magnesium alloys
  par: Min Zhan, et autres
  Publié: (2024)
• A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction
  par: Peng Yue, et autres
  Publié: (2024)
• Fatigue crack initiation site transition of high‐strength steel under very high‐cycle fatigue
  par: Xiaoyuan Teng, et autres
  Publié: (2024)
• Predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy using backpropagation neural network optimized by the improved dung beetle algorithm
  par: Zihao Gao, et autres
  Publié: (2024)