Ingenieurwissenschaften und zugeordnete Tätigkeiten
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The combination of laser powder bed fusion (LPBF), known for its geometrical freedom and accuracy, and the nozzle-based laser metal deposition process (LMD), known for its high build-up rates, has great potential to reduce the additive manufacturing times for large metallic parts. For the industrial application of the LPBF-LMD hybrid process chain, it is necessary to investigate the infuence of the LMD process on the LPBF substrate. In addition, the build plate material also has a signifcant impact on the occurrence of distortion along the additive manufacturing process chain. In the literature, steel build plates are often used in laser-based additive manufacturing processes of Inconel 718, since a good metallurgical Bonding can be assured whilst reducing costs in the production and restoration of the build plates. This paper examines the distortion caused by LMD material deposition and the infuence of the build plate material along the hybrid additive manufacturing process chain. Twin cantilevers are manufactured by LPBF and an additional layer is subsequently deposited with LMD. The distortion is measured in the as-built condition as well as after heat treatment. The efect of diferent LMD hatch strategies on the distortion is determined. The experiments are conducted using the nickel-base alloy Inconel 718. The results show a signifcant infuence of LMD path strategies on distortion, with shorter tool paths leading to less distortion. The remaining distortion after heat treatment is considerably dependent on the material of the build plate.
Additive manufacturing, and therefore directed energy deposition, is
gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process Parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes.
First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parameters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %.
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.
Rapid localized heating and cooling during additive manufacturing using laser deposition method (LMD) lead to loss of dimensional accuracy as well as cracking of built parts. Finite-Element welding simulations allow prediction of geometrical deviations and accumulated residual stresses as well as their optimization before conducting experiments. Due to the great length of stacked welds, calculation times for fully transient thermomechanical simulations are currently long, the calculation stability suffers from the high number of contact bodies in the model and the modelling effort is high, as the geometries need to be sliced and positioned layer-wise.
In this contribution, an integrated modelling approach is demonstrated for a thin-walled LMD component made from 30 layers of 1.4404 (316L) stainless steel: Instead of the layer-by-layer modelling strategy commonly found in the literature, the whole component mesh is kept in one piece and the fully transient, layer-by-layer material deposition is implemented via element sets. In contrast to prior simulations, nonlinear contact between the layers does not have to be considered, significantly decreasing calculation times. The calculated distortions are compared to recently published, in-situ digital image correlation (DIC) measurements as well as numerical simulations conducted with the established layer-wise modelling strategy to judge result quality. Finally, the improvement in calculation time and ease-of-use is compared between both modelling approaches and conclusions regarding future usage for industrial-scale components are drawn.