Analysis of Fatigue Strength of L-PBF AlSi10Mg with Different Surface Post-Processes: Effect of Residual Stresses

Space and aerospace industries has been starting in the recent years the replacement process of parts and components obtained by traditional manufacturing processes with those produced by Additive Manufacturing (AM). The complexity of the obtainable parts makes, in general, challenging the superficial post processing of some zones, making a stringent requirement the investigation of the fatigue performances of components with rough superficial state or machined. The aim of this work is then to analyse and compare the fatigue performances of an additively manufactured (AMed) AlSi10Mg material considering both the effects of the manufacturing defects and residual stresses related to three different superficial states, namely machined, net-shape and sandblasted. The residual stress profiles of the three superficial states were found to play a key role in determining the fatigue properties of the analysed material, while the manufacturing defects at the failure origin were found to be comparable among the three series. To take into account the combined effect of residual stresses and manufacturing defects a fracture mechanics approach was considered for the estimation of the fatigue performances in both infinite and finite life regimes. It was found that by considering the nominal measured residual stress profiles in the fracture mechanics model the estimations were satisfactory compared to the experimental data-point. To increase the accuracy of the fatigue life estimations a series of numerical analyses were performed aimed to investigate the residual stresses relaxation during the cyclic loading. The adoption of the relaxed residual stress profiles in the fracture mechanics model resulted in good estimations respect to the experimental data-points, highlighting the necessity in adopting such developed approaches during the design phase of AM parts and components

Multiaxial fatigue behavior and modelling of additive manufactured Ti-6Al-4V parts: The effects of layer orientation and surface texture

Additive manufacturing (AM) technologies have enabled industries to create and manufacture highly intricate parts with designs that have previously been deemed impossible via subtractive means. Analogous to conventionally fabricated parts, additively manufactured (AM) parts are often subjected to cyclic loadings throughout their service life. Consequently, fatigue performance becomes an important consideration in design and qualification. The geometric complexity of AM parts is accompanied by varying thermal histories which affect microstructure, porosity, surface texture, and residual stresses. The aforementioned factors influence fatigue performance and possibly introduce multiaxial stress states even when the loading is uniaxial. Therefore, understanding the fatigue behavior under multiaxial loadings is necessary to assess reliable in-service part performance. In this study, the effects of build layer orientation and surface texture on the multiaxial fatigue behavior of unmachined Ti-6Al-4V parts fabricated via a laser beam powder bed fusion process are investigated. Fatigue tests include axial, torsion, in-phase, and 90◦ out-of-phase axial/torsion loadings. Results indicate that the fatigue cracks in unmachined Ti-6Al-4V start from surface defects and are oriented along the maximum tensile stress plane. Accordingly, fatigue results are correlated using the tensile-based Maximum Principal Stress (MPS) approach. Finally, the influence of surface defects on the fatigue performance is incorporated by evaluating the surface micro-notches along the MPS using Murakami’s ̅̅̅̅̅̅̅̅ area √ parameter to obtain an initial crack size, which is then used with the NASGRO crack propagation model to predict fatigue lives

Strain Localizations in Notches for a Coarse-Grained Ni-Based Superalloy: Simulations and Experiments

Alloys used for turbine blades have to safely sustain severe thermomechanical loadings during service such as, for example, centrifugal loadings, creep and high temperature gradients. For these applications, cast Ni-based superalloys characterized by a coarse-grained microstructure are widely adopted. This microstructure dictates a strong anisotropic mechanical behaviour and, concurrently, a large scatter in the fatigue properties is observed. In this work, Crystal Plasticity Finite Element (CPFE) simulations and strain measurements performed by means of Digital Image Correlations (DIC) were adopted to study the variability introduced by the coarse-grained microstructure. In particular, the CPFE simulations were calibrated and used to simulate the effect of the grain cluster orientations in proximity to notches, which reproduce the cooling air ducts of the turbine blades. The numerical simulations were experimentally validated by the DIC measurements. This study aims to predict the statistical variability of the strain concentration factors and support component design

Strain Localizations in Notches for a Coarse-Grained Ni-Based Superalloy: Simulations and Experiments

Alloys used for turbine blades have to safely sustain severe thermomechanical loadings during service such as, for example, centrifugal loadings, creep and high temperature gradients. For these applications, cast Ni-based superalloys characterized by a coarse-grained microstructure are widely adopted. This microstructure dictates a strong anisotropic mechanical behaviour and, concurrently, a large scatter in the fatigue properties is observed. In this work, Crystal Plasticity Finite Element (CPFE) simulations and strain measurements performed by means of Digital Image Correlations (DIC) were adopted to study the variability introduced by the coarse-grained microstructure. In particular, the CPFE simulations were calibrated and used to simulate the effect of the grain cluster orientations in proximity to notches, which reproduce the cooling air ducts of the turbine blades. The numerical simulations were experimentally validated by the DIC measurements. This study aims to predict the statistical variability of the strain concentration factors and support component design