Additive Manufacturing Benchmarks 2022 (AM-Bench 2022): Tuesday Plenary
Program Organizers: Brandon Lane, National Institute of Standards and Technology; Lyle Levine, National Institute of Standards and Technology
Tuesday 8:30 AM
August 16, 2022
Room: Regency Ballroom I & II
Location: Hyatt Regency Bethesda
Session Chair: David Deisenroth, National Institute of Standards and Technology
8:30 AM Plenary
Combining Multi-Physics Simulations with Data-Driven Models for Accelerated AM Process Prediction: Gregory Wagner1; Zhengtao Gan2; Wing Liu1; 1Northwestern University; 2University of Texas at El Paso
Detailed multi-physics simulations have been crucial in elucidating AM process-structure-property relationships and guiding process improvements. However, traditional simulations are limited by their computational expense, and plagued by uncertainties in parameters, properties, and model form. Data-driven methods such as machine learning, on the other hand, can have predictive power but require more data than is typically available from direct measurements, and cannot be reliably extrapolated to new materials or process conditions. In this talk, I will summarize recent work in our research groups to combine the strengths of multi-physics simulation with data-driven methods. Novel approaches include physics-informed machine learning, calibration and optimization methods, data-driven dimensional analysis, and model order reduction. Using these techniques, we are improving the speed and accuracy of process predictions at scales from the melt pool to the full part.
9:00 AM Plenary
In-Situ Spatial Monitoring and Layer-to-Layer Control of Additive Manufacturing Processes: Robert Landers1; Douglas Bristow2; Edward Kinzel3; Cody Lough4; Tengfei Luo1; Sandipan Mishra5; 1University of Notre Dame; 2Missouri Univ of Science & Technology; 3Univ. of Notre Dame; 4Kansas City national Security Campus; 5Rensselaer Plytechnic Institute
Additive Manufacturing (AM) is a disruptive class of manufacturing processes that fabricates parts with complex geometries, uses minimal tooling, and can potentially tailor mechanical properties locally. However, achieving accurate geometries with AM processes is challenging due to variability (e.g., changes in part thermal characteristics, material composition, scan path) and that the process is prone to defects (e.g., porosity). This has led to significant research efforts into in-situ monitoring and control. In this talk, we will discuss the use of radiometric measurements (infrared imaging and optical emission spectroscopy) to generate features related to part microstructural properties and laser line scanners to provide morphological spatial properties during the process. These spatial measurements provide the basis for our work in layer-to-layer control where in-situ data is used to intelligently adjust process parameters between layers. In this talk we will provide examples from laser metal deposition, selective laser melting, and digital forming of glass.
9:30 AM Plenary
Suppressing Filament Defects in Embedded 3D Printing: Leanne Friedrich1; Ross Gunther2; Jonathan Seppala1; 1National Institute of Standards and Technology; 2Georgia Institute of Technology
Extrusion-based additive manufacturing techniques including Direct Ink Writing (DIW) have enabled the fabrication of complex, custom constructs across a wide variety of materials. However, DIW requires self-supporting inks. Embedded Ink Writing (EIW) expands the printable space into less viscous materials, making it particularly useful for soft biomaterials and functional materials. In EIW, a nozzle is submerged into a viscoelastic support bath and extrudes continuous filaments. Because the bath is viscoelastic, it fluidizes at the nozzle, allowing ink deposition, but it behaves like a solid at low shear stresses, holding the printed structure in place. In EIW, filament defects including sharp edges, surface roughness, rupture, and contraction can inhibit shape fidelity and mechanical and functional properties of printed parts. Using computational fluid dynamics simulations in OpenFOAM and in-situ imaging experiments, we determine that these defects can be controlled using the local viscosity ratio, capillary number, and nozzle shape.
10:00 AM Break