Directed energy deposition additive manufactured parts have steep stress gradients and an anisotropic microstructure caused by the rapid thermo-cycles and the layer-upon-layer manufacturing, hence heat treatment can be used to reduce the residual stresses and to restore the microstructure. The numerical simulation is a suitable tool to determine the parameters of the heat treatment process and to reduce the necessary application efforts. The heat treatment simulation calculates the distortion and residual stresses during the process. Validation experiments are necessary to verify the simulation results. This paper presents a 3D coupled thermo-mechanical model of the heat treatment of additive components. A distortion-based validation is conducted to verify the simulation results, using a C-ring shaped specimen geometry. Therefore, the C-ring samples were 3D scanned using a structured light 3D scanner to compare the distortion of the samples with different post-processing histories.
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.
Resistance spot welding is an established joining process for the production of safetyrelevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms.
Especially, when predicting a data set from outside of the training space.
The application of anti-corrosion coated, high-strength steels in the automotive industry has increased in recent years. In combination with various zinc-based surface coatings, liquid metal embrittlement cracking can be observed in some of these materials. A high-quality, crack-free spot-welded joint is essential to realize the lightweight potential of the materials. In this work, the LME susceptibility of different coatings, which will be determined by the crack length and the occurrence rate, will be investigated using a welding under external load setup. The uncoated specimens did not show any LME. EG, GI and GA showed significantly less LME than ZM coatings. The latter coatings showed much larger crack lengths than the EG, GI and GA coatings. Furthermore, two mechanisms regarding the LME occurrence rate were observed: the occurrence of LME in zinc–magnesium coatings was theorized to be driven by the material properties of the coatings, whereas the occurrence of LME at EG, GI and GA samples was forced mainly by the application of the external tensile load.
In the experimental setup of this work, the materials were exposed to unusually high mechanical loads (up to 80% of their yield strength) to evoke LME cracks.
Ensuring the required mechanical-technological properties of welds is a critical issue in the application of multi-wire submerged arc welding processes for welding high-strength fine-grained steels. Excessive heat input is one of the main causes for microstructural zones with deteriorated mechanical properties of the welded joint, such as a reduced notched impact strength and a lower structural robustness. A process variant is proposed which reduces the weld volume as well as the heat input by adjusting the welding wire configuration as well as the energetic parameters of the arcs, while retaining the advantages of multiwire submerged arc welding such as high process stability and production speed.