Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

Filters

3
2 1
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Transferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Deposition
    ( 2022-11-04)
    Marko, Angelina
    ;
    Bähring, Stefan
    ;
    Raute, Maximilian Julius
    ;
    Biegler, Max
    ;
    Rethmeier, Michael
    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.
  • Publication
    Automated Tool-Path Generation for Rapid Manufacturing of Additive Manufacturing Directed Energy Deposition Geometries
    ( 2020)
    Biegler, Max
    ;
    Wang, Jiahan
    ;
    Kaiser, Lukas
    ;
    Rethmeier, Michael
    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 tool-paths 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. So-called “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.
  • Publication
    Finite element analysis of in-situ distortion and bulging for an arbitrarily curved additive manufacturing directed energy deposition geometry
    ( 2018)
    Biegler, Max
    ;
    Marko, Angelina
    ;
    Graf, Benjamin
    ;
    Rethmeier, Michael
    With the recent rise in the demand for additive manufacturing (AM), the need for reliable simulation tools to support experimental efforts grows steadily. Computational welding mechanics approaches can simulate the AM processes but are generally not validated for AM-specific effects originating from multiple heating and cooling cycles. To increase confidence in the outcomes and to use numerical simulation reliably, the result quality needs to be validated against experiments for in-situ and post process cases. In this article, a validation is demonstrated for a structural thermomechanical simulation model on an arbitrarily curved Directed Energy Deposition (DED) part: at first, the validity of the heat input is ensured and subsequently, the model's predictive quality for in-situ deformation and the bulging behaviour is investigated. For the in-situ deformations, 3D-Digital Image Correlation measurements are conducted that quantify periodic expansion and shrinkage as they occur. The results show a strong dependency of the local stiffness of the surrounding geometry. The numerical simulation model is set up in accordance with the experiment and can reproduce the measured 3 dimensional in-situ displacements. Furthermore, the deformations due to removal from the substrate are quantified via 3D scanning, exhibiting considerable distortions due to stress relaxation. Finally, the prediction of the deformed shape is discussed in regards to bulging simulation: to improve the accuracy of the calculated final shape, a novel extension of the model relying on the modified stiffness of inactive upper layers is proposed and the experimentally observed bulging could be reproduced in the finite element model.