This study presents the correlation between process parameters and porosity formation in a TiAl6V4 alloy produced by selective laser melting. The porosity is investigated by 2D and 3D methods aiming ...to identify the mechanisms of void formation, their morphology as well as volume fraction as a function of the energy density. An evident minimum volume fraction is observed between process parameters with significant overheating and insufficient fusion. It is shown that these two marginal regions define two mechanisms of void formation. Two dominant types of voids morphology are identified and examined regarding pore orientation versus their elongation, which together with the curvature distribution analysis allow revealing critical defects.
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•The initial porosity of SLM Ti6Al4V was significantly reduced by applying a systematic processing strategy.•Marginal applied energy densities - excessive or insufficient - lead to two different mechanisms of pore formation.•The correlation between pore orientation, and morphology allowed revealing voids critical for mechanical performance.•Process optimization should consider pore formation mechanisms besides overall porosity to achieve low-void microstructures.
The need to reduce the ecological footprint of (water, land, air) vehicles in this era of climate change requires pushing the limits in the development of lightweight structures and materials ....
Peritectic titanium alloys for 3D printing Barriobero-Vila, Pere; Gussone, Joachim; Stark, Andreas ...
Nature communications,
08/2018, Letnik:
9, Številka:
1
Journal Article
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Metal-based additive manufacturing (AM) permits layer-by-layer fabrication of near net-shaped metallic components with complex geometries not achievable using the design constraints of traditional ...manufacturing. Production savings of titanium-based components by AM are estimated up to 50% owing to the current exorbitant loss of material during machining. Nowadays, most of the titanium alloys for AM are based on conventional compositions still tailored to conventional manufacturing not considering the directional thermal gradient that provokes epitaxial growth during AM. This results in severely textured microstructures associated with anisotropic structural properties usually remaining upon post-AM processing. The present investigations reveal a promising solidification and cooling path for α formation not yet exploited, in which α does not inherit the usual crystallographic orientation relationship with the parent β phase. The associated decrease in anisotropy, accompanied by the formation of equiaxed microstructures represents a step forward toward a next generation of titanium alloys for AM.
The microstructure and phase composition in selective laser melted (SLM) Ti-6Al-4V plays a key role for its mechanical performance. The microstructure evolution in SLM Ti-6Al-4V was studied in the ...as-built condition and after sub-transus heat treatments between 400 °C and 800 °C focusing on elemental partitioning and the role of lattice defects on precipitation of the β phase. With SLM parameters corresponding to low volume energy density (EV = 77 J/mm3) the as-built microstructure consisted of acicular martensite and showed a higher density of lattice defects than that synthesized under high EV = 145 J/mm3 condition. High energy X-ray synchrotron diffraction indicated the presence of ∼2 wt.% β-phase at this high EV. Moreover, atom-probe tomography revealed enrichments in β-stabilizers at one- and two-dimensional lattice defects. These fine enriched one-dimensional columnar and two-dimensional features are identified as precursors of β-phase, revealing the role of lattice defects for β-precipitation. Upon annealing at 400 °C and 530 °C, β-films began to fragment into β−platelets and nanoparticles, whereas annealing at 800 °C led to a coarse-lamellar α/β-microstructure. Moreover, α2-Ti3Al was found in the 400 °C annealed condition. In line with the microstructure changes, Vickers hardness increased upon annealing at temperatures up to 530 °C and dropped when coarsening occurred at higher temperatures. Substantial element partitioning occurred during thermally driven martensite decomposition, which was significantly stronger for Fe than for V.
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Data-driven models based on deep learning have led to tremendous breakthroughs in classical computer vision tasks and have recently made their way into natural sciences. However, the absence of ...domain knowledge in their inherent design significantly hinders the understanding and acceptance of these models. Nevertheless, explainability is crucial to justify the use of deep learning tools in safety-relevant applications such as aircraft component design, service and inspection. In this work, we train convolutional neural networks for crack tip detection in fatigue crack growth experiments using full-field displacement data obtained by digital image correlation. For this, we introduce the novel architecture ParallelNets-a network which combines segmentation and regression of the crack tip coordinates-and compare it with a classical U-Net-based architecture. Aiming for explainability, we use the Grad-CAM interpretability method to visualize the neural attention of several models. Attention heatmaps show that ParallelNets is able to focus on physically relevant areas like the crack tip field, which explains its superior performance in terms of accuracy, robustness, and stability.
While the volumetric energy density is commonly used to qualify a process parameter set, and to quantify its influence on the microstructure and performance of additively manufactured (AM) materials ...and components, it has been already shown that this description is by no means exhaustive. In this work, new aspects of the optimization of the selective laser melting process are investigated for AM Ti-6Al-4V. We focus on the amount of near-surface residual stress (RS), often blamed for the failure of components, and on the porosity characteristics (amount and spatial distribution). First, using synchrotron x-ray diffraction we show that higher RS in the subsurface region is generated if a lower energy density is used. Second, we show that laser de-focusing and sample positioning inside the build chamber also play an eminent role, and we quantify this influence. In parallel, using X-ray Computed Tomography, we observe that porosity is mainly concentrated in the contour region, except in the case where the laser speed is small. The low values of porosity (less than 1%) do not influence RS.
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•Significant improvement in dimensional accuracy of Inconel 718 thin channels produced by laser powder fusion (LPBF) through a multi-component optimization is achieved.•Roughness of ...flat surfaces tilted at 0°, 45°, 90° and 135° to the working platform is optimized.•Build direction effect on the quality of inclined shaped channels is analysed using computed tomography.•Optimized LPBF contour parameters in combination with modified channel shapes allowed nearly ideally round channels in all building orientations for design of combustion chambers for future aircraft engines.
A laser powder bed fusion (LPBF) strategy was developed for manufacturing small channels with high dimensional accuracy in Inconel 718 structures. Particular attention was paid to surface characteristics such as equivalent diameter and shape factor. The inherent surface quality of external surfaces was optimized by a systematic variation of the LPBF contour parameters as well as the channel cross-section. The mean arithmetic roughness Sa was analysed for upskin, vertical and downskin surfaces with respect to the build platform. Simultaneously, the effect of the build direction on the quality of internal free-shaped surfaces was investigated on channels with diameters from 500 to 1000 µm and build orientations from the horizontal (0°) to vertical (90°). A significant improvement in dimensional accuracy was achieved by using an optimized droplet-shaped cross-section that is scaled as a function with the build inclination. An angular analysis of the surface roughness in different regions of the channels confirms that this modified cross-section reduces the fraction of channel regions that show a particularly high surface roughness due to inward melting. In combination with an optimized contour processing strategy, the modified channel resulted in the best properties for inclinations below 45°. The shape factor increased from 0.4 to almost 0.9, i.e., close to the ideally round shape.
The occurrence of fatigue cracks is an inherent part of the design of engineering structures subjected to nonconstant loads. Thus, the accurate description of cracks in terms of location and ...evolution during service conditions is mandatory to fulfill safety‐relevant criteria. In the present work, we implement a deep convolutional neural network to detect crack paths together with their crack tips based on displacement fields obtained using digital image correlation. To this purpose, fatigue crack propagation experiments were performed for AA2024‐T3 rolled sheets using specimens with different geometries. Several hundred datasets were acquired by digital image correlation during the experiments. A part of the displacement data from one of the specimens was then used to train the neural network. The results show that the method can accurately detect the shape and evolution of the cracks in all specimens. Adding synthetic data generated by finite element analyses to the training step improved the accuracy for cracks with stress intensity factors that exceeded the range of the original training data.
Selective laser melting is a promising powder-bed-based additive manufacturing technique for titanium alloys: near net-shaped metallic components can be produced with high resource-efficiency and ...cost savings ....
Abstract Today’s societal challenges require rapid response and smart materials solutions in almost all technical areas. Driven by these needs, data-driven research has emerged as an enabler for ...faster innovation cycles. In fields such as chemistry, materials science and life sciences, automatic and even autonomous data generation and processing is already accelerating knowledge discovery. In contrast, in experimental mechanics, complex investigations like studying fatigue crack growth in structural materials have traditionally adhered to standardized procedures with limited adoption of the digital transformation. In this work, we present a novel infrastructure for data-centric experimental mechanics in the field of fatigue crack growth. Our methodology incorporates a robust code base that complements a multi-scale digital image correlation and robot-assisted test rig. Using this approach, the information-to-cost ratio of fatigue crack growth experiments in aerospace materials is significantly higher compared to traditional experiments. Thus, serves as a catalyst for discovering new scientific knowledge in the field of structural materials and structures.
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