An approach to estimate the low cycle fatigue probabilistic curves of PBF-LB/M 316L steel from small size datasets using the remora optimization algorithm

Abstract

The significant dispersion in fatigue properties is a prevalent attribute observed in metals manufactured by the Additive Manufacturing (AM). To ascertain the safety of components subjected to low cycle fatigue (LCF) loadings at a temperature of 550 °C, it is necessary to carry out a fatigue reliability assessment of AM metals. Nevertheless, the number of LCF life tests is limited owing to time-consuming nature of test and high total production cost of AM materials. Such a small size dataset cannot satisfy the criteria of the traditional reliability assessment method. This work aims to develop a novel method to evaluate the LCF reliability of 316L stainless steel manufactured by laser-based powder bed fusion of metals (PBF-LB/M) technology. Firstly, the Weibull model is coupled with the Coffin-Manson-Basquin relationship in order to characterize the dispersion of life data points at various strain levels. Secondly, reliability results from a small size dataset show a maximum relative error in fatigue life of less than 25 %, which is similar to results from a large size dataset and the traditional method using fatigue data from public sources. Among this step, the remora optimization algorithm is firstly applied to calculate the characteristic parameters of newly-proposed method. Thirdly, the LCF reliability of PBF-LB/M 316L based on the small size dataset is evaluated. The N95% is equivalent to 44 % of N50%. Finally, the PBF-LB/M 316L is compared with traditional 316L considering the reliability. The LCF properties of PBF-LB/M 316L is similar to the traditional 316L at 50 % reliability, however it’s slightly worse at 95 % reliability.