Low cycle fatigue and creep-fatigue interaction behavior of 316L(N) stainless steel and life prediction by artificial neural network approach

Low cycle fatigue (LCF) behavior of solutionized 316L(N) stainless steel (SS) has been studied at various temperatures, strain amplitudes, strain rates, hold times and in 20% prior cold worked condition. The alloy in general showed a reduction in fatigue life with, increase in temperature, increase in strain amplitude, decrease in strain rate, an increase in duration of hold time in tension and with prior cold work. The LCF and creep-fatigue interaction (CFI) behavior of the alloy was explained on the basis of several operative mechanisms such as dynamic strain ageing, creep, oxidation and substructural recovery. The capability of artificial neural network (ANN) approach to life prediction under LCF and CFI conditions has been assessed by using the data generated in the present investigation. It is demonstrated that the prediction is within a factor of 2