Additive Fertigung
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- 9.6 Additive Fertigung metallischer Komponenten (62)
- 5.4 Multimateriale Fertigungsprozesse (55)
- 5.2 Metallische Hochtemperaturwerkstoffe (34)
Metal Additive Manufacturing (AM) technologies such as Laser Powder Bed Fusion (LPBF) are characterized by layer wise construction, which enable advancements of component design, leading to potential efficiency and performance improvements. However, the rapid cooling rates associated with the process consequently leads to the generation of high magnitude residual stresses (RS). Therefore, a deep understanding of the formation of RS, the influence of process parameters on their magnitude and the impact on mechanical performance is crucial for widespread application. The experimental characterization of these RS is essential for safety related engineering application and supporting the development of reliable numerical models. Diffraction-based methods for RS analysis using high energy synchrotron X-rays and neutrons enable non-destructive spatially resolved characterization of both surface and bulk residual stresses in complex components. This presentation will provide an overview of recent research conducted by the BAM at large scale facilities for the characterization of residual stresses in LPBF metallic alloys. Special focus will be given to the challenges posed by textured LPBF materials for the reliable choice of the diffraction elastic constants (DECs), which is crucial to the accurate calculation of the level of RS.
The high cooling rates (~106 K/s) occurring during Laser Powder Bed Fusion (PBF-LB/M) of AlSi10Mg induce to the formation of a fine nanometric silicon network in the as-built condition. Such unprecedented microstructure enhances the mechanical strength when compared to equivalent as-cast materials. Nevertheless, PBF-LB/M also leads to high magnitude residual stress (RS) due to the extreme localized temperature gradients. The presence of RS can be detrimental to the fatigue life of engineering components, and great efforts are focused on understanding their generation and evolution after post-process heat treatments. Typically, T6 heat treatments are used to mitigate RS and improve mechanical performances by Mg2Si precipitation during ageing at 160-180°C. Nevertheless, the solutionizing at 500-540°C vanishes the fine silicon network, leading to the formation of micrometric (average of ~2-5 µm) polygonal Si particles, similar to those observed in T6 heat-treated Al-Si cast materials. Therefore, the aim of this work is to evaluate the ability of two so-called low temperature heat treatments (i.e., at 265°C and 300°C) to mitigate RS while retaining the fine as-built microstructure inherent to PBF-LB/M AlSi10Mg. The fatigue behavior of the as-built material is subsequently compared to the two low temperature conditions.
Synchrotron X-ray computed tomography (SXCT) at BAMline has been paired with in-situ tensile loading to monitor damage evolution in LPBF Metal Matrix Composite (MMC) 7017 Aluminium alloy + 3 wt% Zr + 0.5 wt% TiC. The processing of the material leads formation to different categories of Zr-rich inclusions, precipitates and defects. In-situ SXCT test disclosed the critical role of the pre-cracks in the reinforcement phases in the failure mechanisms of LPBF MMC. The damage was initiated from lack-of-fusion defects and cracks propagated through coalescence with other defects.
LTCCs (Low-temperature co-fired ceramics) consist of three-dimensionally distributed, hermetically bonded ceramic and metallic components with structure sizes within [10; 100] µm. A non-destructive imaging technique is needed that provides 3D, sharp, high-contrast resolution of these structures, as well as porosity and defect analysis, which is made difficult by the very different X-ray absorption coefficients of the individual components of the microstructure. A HDR method is being developed that allows a combination of different tomograms, each with X-ray energies adapted to individual materials.
High-strength steels offer great potential in weight-optimised modern steel structures. Additive manufacturing processes, such as Wire Arc Additive Manufacturing (WAAM), enable near-net-shape manufacturing of complex structures and more efficient manufacturing, offering significant savings in costs, time, and resources. Suitable filler materials for WAAM are already commercially available. However, the lack of knowledge or technical guidelines regarding welding residual stresses during manufacturing and operation in connection with cold cracking risk limit their industrial application significantly. In a project of BAM and TU Chemnitz, the influences and complex interactions of material, manufacturing process, design and processing steps on residual stress evolution are investigated. By developing process recommendations and a special cold cracking test, economic manufacturing, and stress-appropriate design of high-strength steel WAAM components are main objectives.
The present study focuses on determining the influence of heat control (interpass temperature, heat input, cooling time) and the design aspects of the components on the hardness and residual stresses, which are analysed by X-ray diffraction. Defined reference specimens, i.e., hollow cuboids, are automatically welded with a special WAAM solid wire. The influences of wall length, wall thickness and wall height on the residual stresses are analysed. Geometric properties can be selectively adjusted by wire feed and welding speed but cannot be varied arbitrarily. This was addressed by adapted build-up strategies. The results indicate a significant influence of the heat control and the wall height on the residual stresses. The interpass temperature, wall thickness and wall length are not significant. These analyses allow recommendations for standards and manufacturing guidelines, enabling a safe and economic manufacturing of high-strength steel components.
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
The elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L were investigated between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled sheet). The moduli were determined using the dynamic resonance method. The data set includes information on processing parameters, heat treatments, grain size, specimen dimensions and weight, Young’s and shear modulus as well as their measurement uncertainty.
The dataset was generated in an accredited testing lab using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.
The elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 were investigated between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). The moduli were determined using the dynamic resonance method. The data set includes information on processing parameters, heat treatments, grain size, specimen dimensions and weight, Young’s and shear modulus as well as their measurement uncertainty.
The dataset was generated in an accredited testing lab using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.
The elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V were investigated between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). The moduli were determined using the dynamic resonance method. The data set includes information on processing parameters, heat treatments, microstructure, grain size, specimen dimensions and weight, Young’s and shear modulus as well as their measurement uncertainty.
The dataset was generated in an accredited testing lab using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.