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Large temperature gradients inherent to additive manufacturing (AM) processes induce large residual stress (RS) in the final part. Because RS can influence the tribological properties, this study focuses on the relationship between wear sliding properties and RS in IN718 coatings. Such coatings were deposited with a Laser metal deposition (LMD) machine using two different scanning strategies.
The wear resistance and RS state were investigated after surface milling. RS were measured before and after wear tests on a reciprocating sliding test apparatus.
Two different X-ray diffraction techniques were employed to measure the surface and subsurface state RS: Laboratory Energy Dispersive X-ray Diffraction (LEDXD) and Synchrotron X-ray Energy Dispersive Diffraction (SXEDD). Due to the milling process, the coatings show similar depth distributions of RS from 22 to 92 μm depth, but exhibit different magnitudes depending on the scanning strategy used. Reciprocating sliding wear tests induced high compressive residual stresses that erased the initial RS state, and a similar wear behavior was observed in the two samples. These samples possess similar texture and grain morphology. This demonstrates that the influence of RS on wear resistance is a second-order effect. Nevertheless, it was observed that RS can still impact the wear performance at the early testing stages of the repaired parts.
The microstructure of an apatite-wollastonite (code name AP40) glass-ceramic is analyzed in this study by combining 2D microscopy, phase analysis, X-ray absorption and synchrotron X-ray refraction computed tomography (XCT and SXRCT, respectively). It is shown that this combination provides a useful toolbox to characterize the global microstructure in a wide scale range, from sub-micrometer to millimeter. The material displays a complex microstructure comprising a glassy matrix with embedded fluorapatite and wollastonite small crystals. In this matrix, large (up to 200 μm) spike-shaped structures are distributed. Such microstructural features are oriented around a central sphere, thereby forming a structure resembling a sea urchin. A unique feature of SXRCT, in contrast to XCT, is that internal interfaces are visualized; this allows one to show the 3D distribution of these urchins with exceptionally good contrast. Furthermore, it is revealed that the spike-shaped structures are not single crystals, but rather composed of sub-micrometric crystals, which are identified as fluorapatite and diopside phases by SEM-EDX analysis.
The safe fatigue design of metallic components fabricated by additive manufacturing (AM) is still a largely unsolved problem. This is primarily due to (a) a significant inhomogeneity of the material properties across the component; (b) defects such as porosity and lack of fusion as well as pronounced surface roughness of the asuilt components; and (c) residual stresses, which are very often present in the as‐built parts and need to be removed by post‐fabrication treatments. Such morphological and microstructural features are very different than in conventionally manufactured parts and play a much bigger role in determining the fatigue life. The above problems require specific solutions with respect to the identification of the critical (failure) sites in AM fabricated components. Moreover, the generation of representative test specimens characterized by similar temperature cycles needs to be guaranteed if one wants to reproducibly identify the critical sites and establish fatigue assessment methods taking into account the effect of defects on crack initiation and early propagation. The latter requires fracture mechanics‐based approaches which, unlike common methodologies, cover the specific characteristics of so‐called short fatigue cracks. This paper provides a discussion of all these aspects with special focus on components manufactured by laser powder bed fusion (L‐PBF). It shows how to adapt existing solutions, identifies fields where there are still gaps, and discusses proposals for potential improvement of the damage tolerance design of L‐PBF components
This article is an outcome of a workshop on Fatigue of Additive Manufactured Metallic Components jointly organized by the Federal Institute for Materials Research and Testing (BAM) Berlin, Germany and the National Institute of Standards and Technology (NIST) Boulder, CO, U.S.A. The aim of the workshop was a comprehensive discussion of the specific aspects of additively manufactured (AM) components in regard to failure under cyclic loading. Undoubtedly, a better understanding and the further development of approaches for damage tolerant component design of AM parts are among the most significant challenges currently facing the use of these new technologies.
This article presents a thorough overview of the workshop discussions. It aims to provide a review of the parameters affecting the damage tolerance of AM parts with special emphasis on the process parameters intrinsic to the AM technologies, the resulting defects and residual stresses. Based on these aspects, concepts for damage tolerant component design for AM are reviewed and critically discussed.
The printability of artificial defects inside the additively manufactured laser powder bed fusion (LPBF) 316L stainless steel is investigated. The printing parameters of the LPBF process are optimized to produce artificial defects with reproducible sizes at desired positions while minimizing redundant porosity. The smallest obtained artificial defect is 90 μm in diameter. The accuracy of the geometry of the printed defect depends on both the height and the diameter in the input model. The effect of artificial defects on the very-high-cycle fatigue (VHCF) behavior of LPBF 316L stainless steel is also studied. The specimens printed with artificial defects in the center are tested under VHCF using an ultrasonic machine. Crack initiation is accompanied by the formation of a fine granular area (FGA), typical of VHCF. Despite the presence of relatively large artificial defects, FGA formation is observed around accidental natural printing defects closer to the surface, which can still be considered as internal. The causes for this occurrence are discussed.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
The damage mechanisms of metallic components produced by process laser powder bed fusion differ significantly from those typically observed in conventionally manufactured variants of the same alloy. This is due to the unique microstructures of additively manufactured materials. Herein, the focus is on the study of the evolution of creep damage in stainless steel 316L specimens produced by laser powder bed fusion. X-ray computed tomography is used to unravel the influence of the process-specific microstructure from the influence of the initial void distribution on creep damage mechanisms. The void distribution of two specimens tested at 600 °C and 650 °C is analyzed before a creep test, after an interruption, and after fracture. The results indicate that the formation of damage is not connected to the initial void distribution. Instead, damage accumulation at grain boundaries resulting from intergranular cracking is observed.
Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses.
It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques.
The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al–Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output.