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In this work, stainless steel 316L obtained by Laser Powder Bed Fusion (L‐PBF) has been produced and characterized. The experimental campaign focuses on the samples in the as‐built state under cyclic tension‐compression loadings. Low cycle fatigue (LCF) and high cycle fatigue (HCF) tests are carried out. Microstructure observations are performed before and after the loadings. As‐built L‐PBF 316L has a good LCF performance despite the presence of surface defects but a low fatigue limit in the HCF regime. Removing the surface roughness has a beneficial effect but does not improve the fatigue strength to that of machined wrought 316L. Observations on the microstructure of fatigue samples indicate that LCF is dominated by the local plastic deformation, which is mostly achieved by the slip in the studied material and is not sensitive to the defect. The lack‐of‐fusion defect impairs the fatigue resistance in the HCF regime. Highlights: Monotonic tensile and LCF behavior of L‐PBF 316L are not sensitive to the surface defects.HCF behavior of L‐PBF 316L is dominated by the surface or subsurface defects.Slip planes produced during cyclic deformation are not affected by the melt pool boundaries. [ABSTRACT FROM AUTHOR]

The numerical modelling of the behaviour of materials at the microstructural scale has been greatly developed over the last two decades. Unfortunately, conventional resolution methods cannot simulate polycrystalline aggregates beyond tens of loading cycles, and they do not remain quantitative due to the plasticity behaviour. This work presents the development of a numerical solver for the resolution of the Finite Element modelling of polycrystalline aggregates subjected to cyclic mechanical loading. The method is based on two concepts. The first one consists in maintaining a constant stiffness matrix. The second uses a time/space model reduction method. In order to analyse the applicability and the performance of the use of a space–time separated representation, the simulations are carried out on a three-dimensional polycrystalline aggregate under cyclic loading. Different numbers of elements per grain and two time increments per cycle are investigated. The results show a significant CPU time saving while maintaining good precision. Moreover, increasing the number of elements and the number of time increments per cycle, the model reduction method is faster than the standard solver. [ABSTRACT FROM AUTHOR]