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High-strength fine-grained structural steels have great potential for weight-optimized, efficient structures in many modern steel applications. Further advances in efficiency can be achieved through additive manufacturing and bionic design. Commercial high-strength filler materials for wire arc additive manufacturing (WAAM) are already provided by the consumable producers. Today, application would be strictly limited due to absence of quantitative findings or any guidelines for the industry regarding welding-related stresses and component safety during manufacturing and service. Hence, process- and material-related influences and design-related restraint conditions associated with formation of residual stresses and cold cracking risk are investigated. The aim is the accessibility of special WAAM self-restraining cold cracking tests and easy applicable processing recommendations, enabling an economical, fit-for-purpose and crack-safe WAAM of high-strength steels. This first study focuses on determination of interactions between WAAM process parameters, resulting layer geometry, microstructure and residual stresses, analyzed via X-ray diffraction. Defined reference specimens are automated welded using a special WAAM solid wire (yield strength >820 MPa). Geometric properties can be specifically adjusted by wire feed and welding speed, but cannot be varied arbitrarily, since a high heat input causes local overheating, inadmissible changes of microstructure and mechanical properties, defects and comparable high tensile residual stresses.
Additive Manufacturing (AM) through the Selective Laser Melting (SLM) route offers ample scope for producing geometrically complex parts compared to the conventional subtractive manufacturing strategies. Nevertheless, the residual stresses which develop during the fabrication can limit application of the SLM components by reducing the load bearing capacity and by inducing unwanted distortion, depending on the boundary conditions specified during manufacturing. The present study aims at characterizing the residual stress states in the SLM parts using different diffraction methods. The material used is the nickel based superalloy Inconel 718. Microstructure as well as the surface and bulk residual stresses were characterized. For the residual stress analysis, X-ray, synchrotron and neutron diffraction methods were used. The measurements were performed at BAM, at the EDDI beamline of -BESSY II synchrotronand the E3 line -BER II neutron reactor- of the Helmholtz-Zentrum für Materialien und Energie (HZB) Berlin. The results reveal significant differences in the residual stress states for the different characterization techniques employed, which indicates the dependence of the residual state on the penetration depth in the sample. For the surface residual stresses, longitudinal and transverse stress components from X-ray and synchrotron agree well and the obtained values were around the yield strength of the material. Furthermore, synchrotron mapping disclosed gradients along the width and length of the sample for the longitudinal and transverse stress components. On the other hand, lower residual stresses were found in the bulk of the material measured using neutron diffraction. The longitudinal component was tensile and decreased towards the boundary of the sample. In contrast, the normal component was nearly constant and compressive in nature. The transversal component was almost negligible. The results indicate that a stress re-distribution takes place during the deposition of the consecutive layers. Further investigations are planned to study the phenomenon in detail.
The triaxial distribution of the residual stress in laser powder bed fused austenitic steel 316L was determined by X-ray and neutron diffraction. The residual stress analysis results were linked to the thermal history of the specimens, which were manufactured with varying inter-layer-times and scanning velocities. A clear link between the in-process temperature of the specimens and the residual stress was found, based on in-situ monitoring data.