Deep learning is a particular kind of machine learning, which achieves great power and
flexibility by a nested hierarchy of concepts. A general life prediction method for components …

Physics-informed neural network has strong generalization ability for small dataset, due to
the inclusion of underlying physical knowledge. Two strategies are enforced to incorporate …

High temperature low cycle fatigue tests at various strain amplitudes (±0.5%,±0.6%,±0.8%,±
1.0%) and strain rates (0.5%/s, 0.05%/s, 0.005%/s) were performed at 550° C. The stress …

Research on the creep-thermomechanical fatigue (CTMF) behaviors of austenitic stainless
steel for nuclear power plant pipelines is reviewed in the present paper. The stress response …

The multiaxial low cycle fatigue behavior of nickel-based alloy 690 at 350° C was
investigated. The multi-slip planar dislocation configuration under non-proportional loading …

Abstract Effects of strain amplitude on the isothermal fatigue (IF) and thermomechanical
fatigue (TMF) behaviors of 316LN stainless steel were comparatively studied. The physical …

This work studied the uniaxial, torsional and multiaxial low cycle fatigue behavior of 316LN
stainless steel at 550° C. Results show that the normalized kernel average misorientation …

Isothermal and thermomechanical fatigue tests with various tension dwell time (designated
as creep-IF and creep-TMF respectively) were conducted. The cyclic stress response and …

AISI 321 steels used as heat exchange tube are generally subjected to creep-fatigue
exposure during service. Assessing their remnant properties is therefore required to ensure …

Continuous and interrupted LCF tests were conducted to reveal the effects of temperature,
strain rate and strain amplitude on the LCF behavior of a nitrogen-alloyed 316LN stainless …