Additive Manufacturing Benchmarks 2022 (AM-Bench 2022): Wednesday Plenary
Program Organizers: Brandon Lane, National Institute of Standards and Technology; Lyle Levine, National Institute of Standards and Technology

Wednesday 8:30 AM
August 17, 2022
Room: Regency Ballroom I & II
Location: Hyatt Regency Bethesda

Session Chair: Leanne Friedrich, National Institute of Standards and Technoloty


8:30 AM  Plenary
Additive Manufacturing - A world Full of Computational Opportunities and Challenges: Ferdinando Auricchio1; 1University of Pavia
     Additive Manufacturing (AM) is a complex physical process, involving different thermo-mechanical phenomena at very different scales; accordingly, simulation is fundamental to predict temperature and stress distributions during and after the printing process. Furthermore, AM allows for new unknown freedom in terms of complex shapes which can be manufactured, opening the door to a new set of design requirements.The presentation will focus on immersed method to describe the complex physics as well as on topology optimization schemes to solve problems associated to the freedom which is possible now thanks to AM. The presentation will close with an excursus on our experience on the use of AM to support industrial developments and the design of innovative AM technologies under developments in our labs.

9:00 AM  Plenary
Understanding Variation in the Additive Manufacturing Supply Chain for Improved Modeling Performance: Donald Godfrey1; 1SLM Solutions Americas
    In the current government acceptance criterion environment, there is a heavy burden on “Performance Based Data”. However, “Performance Based Data” cost hundreds-of-thousands of dollars and many times exceed one million dollars for each alloy to achieve ”B-Basis” . The current lack of material property data can be addressed by developing modeling software that accurately predict material properties. A barrier to adoption of AM is the need to qualify parts on a range of machines across a number of vendors, and the cost of qualifying each machine type or sometimes even machine serial number through qualification builds and testing is burdensome. Using better process monitoring, advancing NDE and coupling that to ICME could allow for better generic material data sets. This presentation will focus on the variances of an additive manufacturing supply-chain and the need to minimize these variables for better more accurate modeling and predictability.

9:30 AM  Plenary
Predicting Mechanical Properties of Material Extrusion-fabricated Structures with Limited Information: Amy Peterson1; David Kazmer1; Austin Colon1; Ahmed Adisa1; 1University of Massachusetts Lowell
    Modeling polymer material extrusion additive manufacturing for prediction of mechanical properties requires substantial training data. This work takes advantage of entanglement theory and molecular dynamics approaches to predict isothermal weld time necessary to achieve bulk properties. Acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) samples were used to validate and refine the modeling approaches and identify transport phenomena that drive weld evolution, motivated by a desire to identify what kinds and how much information is necessary to create an acceptably accurate model. We also performed detailed analysis of the types of fracture that occurred across a range of processing conditions and relate this mechanical behavior to print conditions.

10:00 AM Break