F13
Additive Manufacturing 4

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15:30
conference time (CEST, Berlin)
Towards the Virtual Fatigue Characterization of Additive Manufacturing Defects: Mesh-based and Meshfree Simulations
27/10/2021 15:30 conference time (CEST, Berlin)
Room: F
D. Garijo, Z. Mugica, I. Rivero, M. Lozano, A. Palomar, J. Gómez-Escalonilla (Airbus Defence and Space, ESP)
D. Garijo, Z. Mugica, I. Rivero, M. Lozano, A. Palomar, J. Gómez-Escalonilla (Airbus Defence and Space, ESP)
The presence of process-dependent, largely variable complex patterns of intrinsic defects such as lack of fusion or multiple interacting dispersed pores and voids has a major detrimental effect in the fatigue strength of additive manufacturing (AM) parts. These defects can trigger the nucleation and ultimate coalescence of small micro-cracks in airframe components subjected to vibratory environments, thus limiting the general implementation and certification of AM technology in the aerospace sector. Nowadays, industry efforts to consolidate a reliable behavioural modeling of AM components focus on three main areas: (i) collection of physical tests results to build comprehensive databases, (ii) statistical data treatment to enable stochastic approaches for AM materials and (iii) development of deterministic, high-fidelity numerical models for Virtual Fatigue Testing (ViFT). In this context, the characterization of AM micro-defects through surrogate models generated with Machine Learning (ML) algorithms is also arising as a keystone to reduce the volume of physical and numerical experiments needed to mimic the behaviour of AM parts at the different length scales of the test pyramid. This work presents ViFT simulations based on the Continuum Damage Mechanics (CDM) formulation at the defect and coupon scales. Two computational strategies, Finite Element (FE) and Element-Free Galerkin (EFG) methods, are benchmarked in problems with characteristic AM defectology. Different fatigue damage models, including the ones developed by Chaboche and Peerlings, are calibrated with experimental stress-life data in the High Cycle Fatigue (HCF) regime and coded into material subroutines. A CDM-meshfree EFG approximation with structured nodal arrangement and decoupled cell integration scheme is studied in order to assess alternative constitutive-discretization strategies for ViFT simulation at upper length scale levels of the test pyramid. Finally, a preliminary insight into the role of ML-based surrogate models in numerical simulations and their potential to tackle the sources of uncertainty of probabilistic ViFT is discussed.
Fatigue, Additive Manufacturing, Virtual Testing, Finite Element, Element-Free Galerkin
15:50
conference time (CEST, Berlin)
Simulation Driven Product Development and DFAM* for Next Generation Products (*Design for Additive Manufacturing)
27/10/2021 15:50 conference time (CEST, Berlin)
Room: F
S. Acharya, W. Schwarz, M. Masoomi(Ansys Inc., USA); R. O'Hara (nTopology, USA); K. Genc (Synopsys Inc, USA); J. Spragg (EOS, USA)
S. Acharya, W. Schwarz, M. Masoomi(Ansys Inc., USA); R. O'Hara (nTopology, USA); K. Genc (Synopsys Inc, USA); J. Spragg (EOS, USA)
Additive Manufacturing (AM) of metal parts has become broadly accessible in the last decade. However, most industry sectors still have uncertainty when it comes to AM adoption for production. This is especially true when it comes to new unconventional design paradigms that can be only manufactured with technologies such as AM. The use of simulation can reduce the risk of failure while accelerating the product development process for these next-generation products. The presentation will focus on a novel heat exchanger design that was developed in partnership between nTopology, ANSYS, Synopsys, EOS, and North Star Imaging (NSI). The specific heat exchanger design for aerospace applications (Fuel Cooled Oil Cooler) leverages properties of triply periodic minimal surfaces (TPMS) to generate a highly efficient single component design to replace existing plate and tube exchanger designs. Design simulations for fluid and thermal simulations were used to verify and optimize the performance of the new design. The resulting design is 80% lighter yet more efficient than comparable traditional designs. Additive Manufacturing process simulations (coupled thermal-structural) were used to optimize the process so that the residual stresses and the support requirements were minimized or eliminated, altogether. The successful build of the optimized design was scanned for potential build failures or porosity using CT. The simulation of the as-built geometry (from CT scan) is useful in validating the performance of the printed part. Additionally, the mechanical integrity of the as-built designs for structural integrity is discussed. In addition to the computational demands, the specific design geometry brings challenges typical when leveraging novel designs for additive manufacturing. The geometry was generated using an implicit geometry engine. The complex TPMS structure can create large STL files affecting downstream mesh size required for reasonable accuracy. The methodology is proposed general and can be applied to a broad class of products manufactured with AM.
DFAM, Additive Manufacturing, CFD, CHT, Process Simulation, Porosity, Thermomechanical Fatigue
16:10
conference time (CEST, Berlin)
Generating, Simulating and Additively Manufacturing Minimal Surface Heat Exchangers
27/10/2021 16:10 conference time (CEST, Berlin)
Room: F
J. Coronado (PTC, CAN); A. Vlahinos (Advanced Engineering Solutions LLC, USA); D. Rakestraw (PTC, USA); D. Krzeminski (EOS North America USA); F. Alefel (EOS, DEU)
J. Coronado (PTC, CAN); A. Vlahinos (Advanced Engineering Solutions LLC, USA); D. Rakestraw (PTC, USA); D. Krzeminski (EOS North America USA); F. Alefel (EOS, DEU)
Minimal surfaces minimize the total surface area subject to some boundary or volume constraint. Soap bubbles, catenoidal soap-film surfaces and gyroids on butterfly wings are some examples in nature. In the early seventies, mathematicians discover the mathematical expressions of minimal surfaces. These surfaces were not used in product design since we couldn’t generate these designs in CAD and we couldn’t manufacture them. Recent advances in CAD systems and additive manufacturing enable designers to design and manufacture these amazing designs. Minimal surfaces such a Gyroids, Schwarz, Lidinoid, Diamond, SplitP, Neovius have remarkable properties for strength, heat transfer and manufacturability. When a volume is infilled with a minimal surface is subdivided into separate continues volumes that are intermingled. This property makes minimal surface geometries ideal for heat exchangers. In addition, at any point the angle of these surfaces measured relative to normal to the print tray is smaller than the 45 degrees and therefor these geometries can be 3D printed without supports. In order to evaluate the performance and optimize these designs a conjugate heat transfer analysis must be performed. The challenge to the simulation process is that the minimal surface geometries are not the typical B-Rep geometry but they are generated by implicit or voxel-based modelers. This presentation will demonstrate the processes to overcome these challenges with an example of an avionics heat exchanger. The hot fluid is the transmission oil and the cold fluid is the jet fuel. The steps of conceptual design, the parametric geometry generation, the real time preliminary simulation, and the final CFD validation will be presented. In designing for additive manufacturing, a “manufacturing process simulation” needs to be performed in order to evaluate the residual stress levels, the surface accessibility and the distortion sensitivity. The manufacturing process simulation dictates design changes in order to reduce residual stress and distortion. The design modifications to improve manufacturability will also be presented. The digital thread from concept to design to simulation to additive manufacturing and to postprocessing, with subtractive manufacturing, will be demonstrated. The heat exchanger has been produced using additive manufacturing and is in the process to be qualified as an end-user component.
heat exchanger, additive manufacturing, heat transfer, manufacturing process simulation, minimal surface, gyroid
16:30
conference time (CEST, Berlin)
Microstructure, Porosity and Meltpool Simulations as a Method for Process Parameter Optimization in Metal Additive Manufacturing
27/10/2021 16:30 conference time (CEST, Berlin)
Room: F
C. Robinson (ANSYS, USA)
C. Robinson (ANSYS, USA)
Process parameter optimization in Additive Manufacturing is a resource intensive operation when done using trial-and-error experimentation. For metal powder bed fusion processes, parameter optimization typically involves 3 or more stages, months of time, and tens of thousands of dollars. This is true not only for introduction of a new material but also when changing powder suppliers for an existing material or just seeking to improve machine productivity by changing laser scan parameters and/or layer thickness. The first stage of process parameter optimization uses single bead experiments to determine laser scanning parameters which create good deposits for a desired layer thickness. These experiments are done by spreading a layer of powder over a baseplate and then creating a series of single laser scan tracks at various laser powers and scan speeds. In most cases hundreds of scan tracks are created. Scan tracks are visually inspected and those scan tracks which result in contiguous, smooth beads are cross-sectioned and measured using microscopy. Cross-sections which reveal porosity-free deposits with meltpool depth to width ratios between 0.3 and 0.6 are considered candidate laser scan parameters. The second stage of process parameter optimization is the production of porosity cubes. For the candidate laser power and scan speed combinations identified in stage 1, scan strategies are produced by varying laser scan spacing and scan pattern (such as a stripe or chessboard pattern). In most cases dozens, and in some cases hundreds, of laser power, speed, scan spacing and scan pattern combinations are investigated by building up at least one small cube of material using each combination. In some cases 3 replicates of each combination are produced to enable statistical analysis. For each cube that completes successfully, they are cross-sectioned, polished, photographed and analyzed to determine whether the cube has a low enough porosity level to be useful. In most cases users are looking for fully dense porosity cubes produced using process parameters which result in the fastest build speed. In the third stage of process parameter optimization, the microstructure for each porosity cube parameter combination of interest is investigated. Depending upon the alloy of interest, a material scientist may desire a columnar microstructure, an equiaxed microstructure, a certain phase, and/or a certain amount of precipitates. In some cases people re-use the porosity cubes for microstructure analysis if the cubes are large enough for such a use. In other cases, new microstructure samples are created. Once the microstructures of interest are identified, additional stages may involve tensile, fatigue, corrosion, and other testing to down-select the best process parameters. The use of simulation tools can dramatically reduce the time to achieve process parameter optimization. A combination of single bead, porosity and microstructure simulations, along with confirmatory experiments, result in an order of magnitude reduction in the time and cost needed for process parameter optimization. As a result, simulation-driven process parameter optimization enables additive manufacturing practitioners to explore a much broader process parameter space in a shorter time and lower cost than trial-and-error experiments. In this talk, an overview of simulation approaches for process parameter optimization will be presented, along with case studies and examples of comparisons between simulation results and experimental results. The use of new additive simulation tools for process parameter optimization clearly show that simulation driven process parameter optimization is a better way to develop new process parameters than the traditional, experimental approach.
Additive Manufacturing, 3D Printing, Microstructure Simulation, Porosity Simulation, Meltpool Simulation
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