Predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy using backpropagation neural network optimized by the improved dung beetle algorithm

Documenti analoghi

• Fracture mechanism of Ti60 alloy at high temperature in very high cycle fatigue regime and fatigue life prediction
  di: Qikai Zhou, et al.
  Pubblicazione: (2024)
• Frequency effect on low‐cycle fatigue behavior of pseudoelastic NiTi alloy
  di: Volodymyr Iasnii, et al.
  Pubblicazione: (2024)
• A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy
  di: Hang Li, et al.
  Pubblicazione: (2024)
• Comparative assessment of Ti‐6Al‐4 V titanium alloy fatigue life improvement by vibratory peening, shot peening, and vibratory finishing
  di: Maxime Paques, et al.
  Pubblicazione: (2024)
• Additively manufactured Ti‐6Al‐4V microstructure tailoring for improved fatigue life performance
  di: Roger Beal, et al.
  Pubblicazione: (2024)