F10
Additive Manufacturing 1

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08:35
conference time (CEST, Berlin)
An Update from the NAFEMS Metallic Additive Manufacturing Focus Team
27/10/2021 08:35 conference time (CEST, Berlin)
Room: F
S. Van Der Veen (Airbus CTO, FRA)
S. Van Der Veen (Airbus CTO, FRA)
08:55
conference time (CEST, Berlin)
An Integrated Physics-based Digital Twin to Reveal Process-Structure-Properties Relations in Laser Powder-bed Fusion Additive Manufacturing
27/10/2021 08:55 conference time (CEST, Berlin)
Room: F
G. Vastola (Institute of High Performance Computing, SGP); J. Mikula, R. Laskowski, R. Ahuwalia, M. Wei, K. Bai, Y. Zeng, Y-W. Zhang (A*STAR Institute of High Performance Computing, SGP)
G. Vastola (Institute of High Performance Computing, SGP); J. Mikula, R. Laskowski, R. Ahuwalia, M. Wei, K. Bai, Y. Zeng, Y-W. Zhang (A*STAR Institute of High Performance Computing, SGP)
Despite tremendous efforts in improving metal 3D printers’ accuracy and reliability, mainstream insertion of additive manufacturing (AM) in industrial shopfloors is still limited by uncertainty and inconsistency in the AM process. Computer modeling and simulation is the natural answer to address such issues before printing, thus reducing the cost of trial-and-error. However, an exhaustive feature-rich, high-fidelity simulation of AM is extremely challenging, due to the tight coupling between different length scales (from part-scale to powder-scale) and time scales (from build time to scan vector time). By leveraging our in-house capabilities, we have integrated them together into one platform which combines a thermal simulation at the scale of the part, a discrete element method simulation of powder spreading, a ray-tracing simulation of laser-matter interaction, a powder-scale simulation of powder melting and solidification and microstructure evolution, two phase-field simulations of dendritic and precipitates formation, a crystal plasticity calculation for prediction of mechanical properties, and a part-scale simulation of residual stress and distortion, to provide a multiscale simulation platform for AM. Importantly, we also developed a physics-based classification capability which, given an overall thermal history of the component, identifies the regions that have experiences similarly, or different, thermal histories for microstructure evolution. Using the platform, we have investigated the effect of process parameters on porosity, microstructure and mechanical properties, where our integration work allowed us to explicitly link the scale of the part to the scale of the powder, and vice versa. In terms of model validation, hundreds of test coupons were printed and analysed in terms of porosity, microstructure, and mechanical properties, while distortion was validated with an actual industrial component. Here, we intend to show that a focus on computational speed and a seamless integration among length scales provide the user a holistic view of the manufacturing process and supports informed decisions on material choice, part design, and process parameters.
Integrated modelling, digital twin, powder-bed fusion additive manufacturing, metals
09:15
conference time (CEST, Berlin)
Experimental and Numerical Investigation of Metallic Powder Material Inside Additive Manufactured Particle Dampers
27/10/2021 09:15 conference time (CEST, Berlin)
Room: F
G. Hauenstein (SMS Concast AG, CHE); R. Baumann, C. Haack (Hochschule Luzern, CHE)
G. Hauenstein (SMS Concast AG, CHE); R. Baumann, C. Haack (Hochschule Luzern, CHE)
Particle dampers are a promising approach for passive reduction of vibrations over a wide frequency range. With additive manufacturing, it is possible to manufacture parts with embedded powder cavities. In previous investigations of additive manufactured particle dampers, the non-linear damping behavior was determined experimentally. The dynamic behavior of the metallic powder in the cavity is unknown. The goal of this thesis is to determine the dynamic behavior of the internal powder in an additive manufactured particle damper. The research questions are: • How can the energy dissipation of powder be characterized? • In addition, what are the main dependencies for the product development of additive manufactured particle dampers? In this thesis, the dynamic behavior of the stainless steel CL 20ES powder is modeled with the Discrete Element Method. The Discrete Element Method is a numerical method for simulation of particle motions and particle interactions. Due to the powder material used, which has particle sizes in the micrometer range and a high number of particles per volume, it is only possible to perform this investigation on scaled cavities in the millimeter range. The dynamic behavior of the powder is determined under harmonic base excitation of the cavity. The calculation of the energy dissipation is based on the reaction forces of the powder on the cavity. This work examines the influence of different cavity sizes, cavity shapes, excitation amplitudes and frequencies on the energy dissipation. The investigation shows that powder in cavities leads to high-energy dissipation under dynamic vibration. Due to this, it is an effective concept for passive reduction of vibrations. Additive manufactured particle dampers have a similar mechanism of energy dissipation as single particle impact dampers or vibration absorbers. The energy dissipation is caused by the inelastic collision between particles and cavity. The efficiency of the particle damper is only achieved when the excitation amplitude is large enough. The minimal required amplitude depends on the amount of particles in the cavity and on the cavity size. The simulation results provide a better understanding of the particle damping mechanisms, which may help in the design of the next generation of additive manufactured particle dampers. It can be said that (i) more powder leads to more energy dissipation, (ii) the powder should be positioned in the place with maximum amplitude to achieve greater energy dissipation and (iii) that the cavity length in the direction of movement has a more positive influence on the energy dissipation than the cross section in the direction of movement.
DEM, Additive Manufacturing, Particle Simulation, Damping, Vibration
09:35
conference time (CEST, Berlin)
Predicting the Fatigue Life of Additively Manufactured Metal Components Using a Machine Learning Enhanced Durability Analysis
27/10/2021 09:35 conference time (CEST, Berlin)
Room: F
H. Erdelyi, N. Lammens, M. Hack, M. Schulz, S. Straesser (Siemens Digital Industries Software, BEL)
H. Erdelyi, N. Lammens, M. Hack, M. Schulz, S. Straesser (Siemens Digital Industries Software, BEL)
Metal laser powder bed fusion (LPBF) is an additive manufacturing (AM) technology applicable for manufacturing of structurally loaded components. As with many industrial applications, ensuring the safe use of the 3D printed component during service life is a strong requirement. As such, durability analysis is performed to calculate the fatigue life for the given load conditions and to validate the design. However, conventional durability analysis does not consider the local material properties and defects resulting from the AM process and as such cannot predict accurately the fatigue performance of the 3D printed part. With LPBF the component is built layer by layer, having a laser scan and weld the metal bead by bead. This process leads to varying local material properties resulting from the AM induced local conditions like microstructure, porosity content and surface roughness. To predict accurately the fatigue life of a 3D printed metal component one needs to characterize the impact of the above-mentioned local conditions on the fatigue property of the material and to efficiently consider these local fatigue properties in the part scale durability calculation. This poses a rather significant challenge as there are endless combinations of local conditions and post treatments (for example surface and heat treatments). Performing a solely test based characterization would lead to a very large and very expensive test campaign. The solution to this problem proposed by the authors relies on a Machine Learning based material model able to predict the effect of any combination of AM induced local condition on the fatigue property of the material. Combined with an enhanced durability solver the solution can efficiently consider the AM induced local properties on the part scale and provide a more accurate fatigue analysis. The authors acknowledge SIM and VLAIO (Flanders, Belgium) for funding the “M3-FATAM” (HBC.2016.0446) project (M3 research program).
Additive Manufacturing, Machine Learning, Laser Powder Bed Fusion, Durability, Fatigue, 3D Printing
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