The purpose of this styudy is to investigate the cyclic behaviour of the AA2024-T351 aluminum alloy widely used in the aircraft industry. This alloy shows a relatively low ductility at room temperature and is generally heat treated in various conditions to suit particular applications. Monotonic and cyclic tests have been conducted in order to characterize the fatigue behaviour and determine the fatigue life of aluminum alloy. Cyclic tests in the Low Cycle Fatigue (LCF) regime were performed under fully reversed total strain amplitudes ranging between 0.6% and 1.2%. The elastoplastic behaviour was analysed through the stress-strain hysteresis loops leading to evaluate kinematic and isotropic hardenings. The AA2024-T351 was also shown to be prone to cyclic strain hardening. Besides, symmetric High Cycle Fatigue (HCF) tests were also performed and the Stress-Number of cycles (S-N) curve until 107 cycles was plotted. A fatigue limit of about 150 MPa was found. Based on all LCF and HCF tests, the fatigue life could be represented in a strain approach by the Manson-Coffin-Basquin law. Moreover, observations of the fracture surfaces were carried out using a Scanning Electron Microscope (SEM) in order to detect the crack initiation and follow the propagation for the two fatigue regimes.
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Themes: Fatigue and Fracture of Additively Manufactured Materials
MECHANICAL RESISTANCE ASSESMENT OF 316L STAINLESS STEEL ADDITIVELY-REPAIRED STRUCTURES
To quickly characterize the static and cyclic mechanical strength of a structure repaired by additive manufacturing, a specific specimen is developed and then repaired using two processes (laser direct energy deposition and cold spray) with adjustable parameters. The fundamental role of the microstructure in the vicinity of the repaired area in the initiation and propagation of cracks under cyclic loading is highlighted and discussed
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FRACTURE TOUGHNESS OF A DUPLEX STAINLESS STEEL BUILT BY DIRECTED ENERGY DEPOSITION : EFFECT OF THE DEPOSITION DIRECTION
Additive manufacturing of duplex stainless steels (DSS) has recently seen some research interest. In particular, the use of directed energy deposition (DED) is still new and the fabricated materials remain to be fully characterized. In addition, materials produced by additive manufacturing can present anisotropic fracture properties. This study aims to characterize the fracture toughness of a DSS manufactured by DED, taking into account the orientation with regard to the printing strategy.
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ACCELERATED DESIGN AND INTEGRITY ASSESSMENT OF ADDITIVELY MANUFACTURED METALLIC STENTS USING MACHINE-LEARNING MODELS
In this work, we investigate the potential of laser powder bed fusion (L-PBF) to meet the stringent requirements imposed on metallic stents for the treatment of aortic dissection. Here, we use microstructure-based modeling to describe the mechanical properties of L-PBF 316L stainless steel. The derived structure-property relationships then serve as a database for training machine learning (ML) models, such as convolutional networks (CNN) and graphical neural networks (GNN). Based on the established modeling framework, we are able to predict the deformation and fracture behavior of 316L stents and identify the improved stent design in an efficient manner.
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EFFECTS OF DEFECT, LOADING MODE AND MICROSTRUCTURE ON LPBF 316L FATIGUE BEHAVIOR
The present study aims to investigate the high cycle fatigue (HCF) performance of steel 316L fabricated by the laser powder bed fusion (LPBF) process. Bending and torsional fatigue test specimens built horizontally (0°), inclined (45°), and vertically (90°) have been prepared and tested in the as-built and polished states. The presence of multiple lack-of-fusion defects at the surface or subsurface is detrimental to the endurance under cyclic loading. A more pronounced defect sensitivity in bending compared to torsion is found. Microstructural features are seen to compete with inherent defects to affect fatigue performance in the condition that the effective defect sizes are close to the critical fatigue crack size.
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CORRECTING FOR RESIDUAL STRESS EFFECTS ON FATIGUE CRACK GROWTH RATES OF ADDITIVELY MANUFACTURED TYPE 304L STAINLESS STEEL
Additively manufactured (AM) metal builds contain residual stress that can influence measured fatigue crack growth rates (FCGRs), which may then bias the interpretation of the performance of AM materials. In the present work, the on-line crack compliance (OLCC) method was used to determine the residual stress intensity factor, Kres, while simultaneously collecting fatigue crack growth rate data in edge crack compact (C(T)) specimens of both AM and wrought materials. Measured near-threshold FCGR data in AM 304L C(T) specimens appear elevated in comparison with data from wrought specimens over a range of applied ∆K. By quantitatively accounting for residual stress, the results for materials processed by the different methods are brought into good agreement, demonstrating the importance of accounting for residual stress when interpreting fatigue crack growth data in AM materials.
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FINITE ELEMENT MODELLING IN PREDICTING THE EFFECT OF DEFECTS ON STRESS CONCENTRATION AND FATIGUE LIFE OF L-PBF ALSI10MG ALLOY
The elastic-plastic finite element analysis is performed to obtain the stress field around pores and evaluate their resultant effects on fatigue life for L-PBF (Laser Powder Bed Fusion) produced AlSi10Mg alloy. The stress field is calculated for both single and multiple pore models, where stress concentration is evaluated as a function of the pore location and its size. A multi-scale finite element (FE) model is proposed based on the inherent porosity data from Computed Tomography (CT) to predict the overall fatigue life with high (90%) accuracy. The predicted fatigue life (cycles) are calculated using the rainflow counting algorithm in fe-Safe software using the stress-strain data obtained from the proposed FE model developed using the Abaqus software. Using the proposed model, it is possible to generate S-N curves for any loading condition for a given porosity characteristic (porosity density and average pore size).
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IMPACT OF MICRO AND MESOSTRUCTURE ON THE FAILURE RESISTANCE OF LASER POWDER BED FUSION-PROCESSED MATERIALS
Engineering materials processed using additive manufacturing (AM) techniques such as laser powder bed fusion (LPBF) often exhibit unique microstructures and defects that must be controlled to obtain peak performance in mechanical properties and as such a level of damage-tolerance that cannot be achieved in cast alloys. However, our understanding of how processing conditions control micro- and mesostructure and, in turn, mechanical performance, particularly regarding failure resistance, is weak. Furthermore, heat treatments that have been designed to achieve peak performance in cast alloys are often not optimized for alloys that have been processed using AM techniques. Here, we report our work on the effect of processing parameters such as layer thickness, hatch spacing, and scan strategy on crack resistance curve (R-curve) behavior in different orientations of LPBF-processed AlSi10Mg and correlate mechanical performance with meso- and microstructural features such as melt pool arrangement, cell morphology, grain size, grain orientation, and texture. Compared to that we show how heat-treatments impact fracture resistance as well as their anisotropy in two orthogonal orientations in an LPBF-processed 18Ni-300 maraging steel.
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PREDICTING SURFACE ROUGHNESS IN METALLIC ADDITIVELY MANUFACTURED PARTS USING MACHINE LEARNING
A melt pool geometry-based approach is developed to predict surface roughness in metal additively manufactured parts for a range of processing parameters. It is shown that surface roughness on a particular facet can be estimated by stacking melt pools along the facet, extracting their outer contour and applying the necessary transformations. To be able to predict surface quality of various processing parameters in a reasonable time frame, a machine learning framework is developed. This framework is trained over melt pool data generated by high-fidelity FE simulations.
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ANALYSIS OF FATIGUE CRACK GROWTH WITH OVERLOAD EFFECTS THROUGH T-STRESS
Fatigue crack is a major concern to all industries for safety reasons. Fatigue life predictions for structural components such as railways or turbine disks are based on fracture mechanics analysis. Such components are inevitably submitted to underloads or overloads. The aim of this paper is to provide a DIC-BASED experimental analysis of overload 2D fatigue cracks using higher order terms in the Williams’ series expansion.
The prediction of the fatigue life of these components is often based on crack propagation calculations. However, overloads and underloads perturb steady state fatigue crack growth conditions and affect the growth rates by retarding or accelerating growth. The application of overloads generates complex effects on the crack behavior which induce delays that are difficult to predict. The mechanisms that have been proposed to explain retardation after tensile overload include, e.g. residual stress, crack closure and plasticity ahead of the crack tip.
In this work, based on DIC we use full-field measurements to obtain LEFM crack tip features (Stress Intensity Factor and T-stress). Therefore, with these crack tip features, we propose to analyze the T-stress effect on the crack growth propagation.
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