Additive Fertigung
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In manufacturing, fusion welding processes use a lot of resources, which presents an opportunity to reduce environmental impact. While there is a general understanding of the environmental impact of these processes, it is difficult to quantitatively assess key parameters. This study introduces a welding-specific methodology that uses life cycle assessment (LCA) to evaluate the environmental impact of fusion welding technologies. Our approach analyses the main parameters that affect the environmental performance of different welding techniques, including traditional methods and additive manufacturing through the Direct Energy Deposition-Arc (DED-Arc) process. We integrate real-time resource usage data to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact, facilitating the advancement of sustainable manufacturing practices.
Life cycle assessment of fusion welding processes considering upstream and downstream process steps
(2023)
In manufacturing, fusion welding processes consume significant resources, presenting a significant opportunity for reducing environmental impact. Although there is a qualitative understanding of the environmental implications of these processes, a quantitative assessment of key parameters remains complex. This study introduces a welding-specific methodology that employs life cycle assessment (LCA) to quantitatively evaluate the environmental footprint of fusion welding technologies. Our approach identifies and analyses the principal parameters affecting the environmental performance of various welding techniques, including traditional joint welding and additive manufacturing via the Direct Energy Deposition-Arc (DED-Arc) process. Real-time resource usage data is integrated to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact. This facilitates the advancement of sustainable manufacturing practices.
New developments in nickel-based superalloys and production methods, such as the use of additive manufacturing (AM), can result in innovative designs for turbines. It is crucial to understand how the material behaves during the AM process to advance industrial use of these techniques. An analytical model based on reaction-diffusion formalism is developed to better explain the solidification behavior of the material during laser metal deposition (LMD). The well-known Scheil-Gulliver theory has some drawbacks, such as the assumption of equilibrium at the solid-liquid interface, which is addressed by this method. The solidified fractions under the Scheil model and the pure equilibrium model are calculated using CALPHAD simulations. Differential scanning calorimeter is used to measure the heat flow during the solid-liquid phase transformation, the result of which is further converted to solidified fractions. The analytical model is compared with all the other models for validation.
Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings,walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.
Creep and fracture behavior of conventionally and additively manufactured stainless steel 316L
(2020)
A critical task within the frame of establishing process-structure-property-performance relationships in additive manufacturing (AM) of metals is producing reliable and well-documented material behavior’s data and knowledge regarding the structure-property correlation, including the role of defects. After all, it represents the basis for developing more targeted process optimizations and more reliable predictions of performance in the future. Within this context, this contribution aims to close the actual gap of limited historical data and knowledge concerning the creep behavior of the widely used austenitic stainless steel 316L, manufactured by Laser-Powder-Bed-Fusion (L-PBF). To address this objective, specimens from conventional hot-rolled and AM material were tested under application-relevant conditions according to existing standards for conventional material, and microstructurally characterized before and after failure. The test specimens were machined from single blocks from the AM material. The blocks were manufactured using a standard scan and build-up strategy and were subsequently heat-treated. The creep behavior is described and comparatively assessed based on the creep lifetime and selected creep curves and characteristic values. The effect of defects and microstructure on the material’s behavior is analyzed based on destructive and non-destructive evaluations on selected specimens. The AM material shows shorter creep lives, reaches the secondary creep stage much faster and at a lower strain, and features lower creep ductility compared to its conventional counterpart. The creep damage behavior of the AM material is more microstructure than defect controlled and is characterized by the formation and accumulation of single intergranular damage along the whole volume. Critical features identified are the grain morphology and the grain-boundary as well as the dislocation’s density. Micro-computed tomography (µCT) proves to be an alternative to metallography to analyze the creep damage.
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.
Directed energy deposition is a metal additive manufacturing process that builds parts by joining material in a layer-by-layer fashion on a substrate. Those parts are exposed to rapid thermo-cycles which cause steep stress gradients and the layer-upon-layer manufacturing fosters an anisotropic microstructure, therefore stress relief heat treatment is necessary. The numerical simulation can be used to find suitable parameters for the heat treatment and to reduce the necessary efforts to perform an effective stress relieving. Suitable validation Experiments are necessary to verify the results of the numerical simulation.
In this paper, a 3D coupled thermo-mechanical model is used to simulate the heat treatment of an additive manufactured component to investigate the application of a C-ring geometry for the distortion-based validation of the numerical simulation.
Therefore, the C-ring samples were 3D scanned using a structured light 3D scanner to quantify the distortion after each process step.
In additive manufacturing (AM) directed energy deposition (DED), parts are built by welding layers of powder or wire feedstock onto a substrate with applications for steel powders in the fields of forging tools, spare parts, and structural components for various industries. For large and bulky parts, the choice of toolpaths influences the build rate, the mechanical performance, and the distortions in a highly geometry-dependent manner. With weld-path lengths in the range of hundreds of meters, a reliable, automated tool-path generation is essential for the usability of DED processes. This contribution presents automated tool-path generation approaches and discusses the results for arbitrary geometries. Socalled “zig-zag” and “contour-parallel” processing strategies are investigated and the tool-paths are automatically formatted into machine-readable g-code for experimental validation to build sample geometries. The results are discussed in regard to volume-fill, microstructure, and porosity in dependence of the path planning according to photographs and metallographic cross-sections.
The understanding of process-microstructure-property-performance (PMPP) relationships in additive manufacturing (AM) of metals is highly necessary to achieve wide-spread industrial application and replace conventionally manufactured parts, especially regarding safety-relevant applications. To achieve this understanding, reliable data and knowledge regarding material’s microstructure-property relationships (e.g. the role of defects) is needed, since it represents the base for future more targeted process optimizations and more reliable calculations of performance. However, producing reliable material data and assessing the AM material behaviour is not an easy task: big challenges are e.g. the actual lack of standard testing methods for AM materials and the occasional difficulties in finding one-to-one comparable material data for the conventional counterpart.
This work aims to contribute to end this lack of reliable material data and knowledge for the low cycle fatigue behaviour of the most used titanium alloy in aerospace applications (Ti-6Al-4V). For this purpose, two sets of test specimens were investigated. The first set was manufactured from cylindrical rods produced by an optimized DED-L process and the second was manufactured from a hot formed round bar. The test specimens were cyclically loaded until failure in the low-cycle-fatigue (LCF) regime. The tests were carried out according to ISO 12106 between 0.3 to 1.0 % axial strain amplitude from room temperature up to 400°C. The LCF behaviour is described and compared between materials and with literature values based on cyclic deformation curves and strain-based fatigue life curves. Besides, the parameters of Manson-Coffin-Basquin relationship were calculated. The microstructures (initial and after failure) and fracture surfaces were comparative characterized. Thereby, the focus lied on understanding the role of grain morphology and defects on the failure mechanisms and fatigue lifetimes. For this latter characterization, optical microscopy (OM), scanning electron microscopy (SEM) and micro computed tomography (µCT) were used.
Components distort during directed energy deposition (DED) additive manufacturing (AM) due to the repeated localised heating. Changing the geometry in such a way that distortion causes it to assume the desired shape – a technique called distortion-compensation – is a promising method to reach geometrically accurate parts. Transient numerical simulation can be used to generate the compensated geometries and severely reduce the amount of necessary experimental trials. This publication demonstrates the simulation-based generation of a distortioncompensated DED build for an industrial-scale component. A transient thermo-mechanical approach is extended for large parts and the accuracy is demonstrated against 3d-scans. The calculated distortions are inverted to derive the compensated geometry and the distortions after a single compensation iteration are reduced by over 65%.