Additive Manufacturing: ICME Gap Analysis: Session II
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Additive Manufacturing Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Dongwon Shin, Oak Ridge National Laboratory; Richard Otis, Jet Propulsion Laboratory; Xin Sun, Oak Ridge National Laboratory; Greta Lindwall, KTH Royal Institute of Technology; Mei Li, Ford Motor Company; David Furrer, Pratt & Whitney
Tuesday 2:00 PM
February 25, 2020
Room: 7B
Location: San Diego Convention Ctr
Session Chair: Greta Lindwall, KTH Royal Institute of Technology; David Furrer, Pratt & Whitney
2:00 PM Introductory Comments
2:05 PM Invited
Overview of DOE-BES Research and Strategic Planning: Linda Horton1; John Vetrano1; 1US Department of Energy, Office of Basic Energy Sciences
The mission of the Basic Energy Sciences (BES) program is to support fundamental research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels in order to provide the foundations for new energy technologies and to support Department of Energy (DOE) missions in energy, environment, and national security. BES has a long-standing strategic planning process that includes BES advisory committee reports, topical in-depth community workshops and reports, and rigorous program reviews. BES-supported activities include research on materials built with atom-by-atom precision and computational models that can be used to predict the behavior of materials before they are synthesized. In the portfolio, computational materials science, synthesis science and mechanical behavior studies provide underpinning science relevant to ICME and a wide range of technologies. This talk will overview the currently funded research and outline the process by which the overarching strategies are developed.
2:40 PM Invited
Making Metal Additive Manufacturing Practical – What’s Missing?: Lyle Levine1; Carelyn Campbell1; Mark Stoudt1; Greta Lindwall2; Eric Lass1; Fan Zhang1; Brandon Lane1; 1National Institute of Standards and Technology; 2KTH
The extreme processing conditions of metal additive manufacturing (AM) create position-specific microstructures with high stresses, extreme compositional gradients, and unexpected phases that complicate component and process design and certification. Many of these challenges stem from the use of existing casting alloys that were never designed for these processing conditions. Early research has largely focused on optimizing post build processing to compensate for unwanted features, but a more proactive approach of modifying existing alloys and developing new alloys using an ICME framework is required. Several efforts are incorporating an ICME approach, but significant gaps remain in our ability to couple simulation approaches and our access to the relevant thermophysical and model validation data. Examples will be taken from our work on AM Ni-based superalloys and martensitic stainless steels. Although a path forward is clear for addressing some of these challenges, others remain elusive.
3:15 PM Invited
Challenges to Predict the Microstructure and Properties of metallic AM components: Carolin Korner1; Matthias Markl1; Johannes Köpf1; Alexander Rausch1; Zerong Yang1; 1University of Erlangen-Nuremberg
Microstructure evolution during AM is governed by processes on time and length scales that encompass many orders of magnitude. Melting and evaporation processes have to be described on a nanosecond scale whereas the whole building process takes many hours. Many effects during AM, such as binding faults, surface roughness or epitaxial grain growth from the powder bed, can only be understood by taking into account individual powder particles, i.e. on the microscale, whereas the component size is in the range of centimeter. This contribution discusses the state-of-the-art of AM microstructure prediction and reveals gaps to be filled in the future.
3:50 PM Break
4:15 PM Invited
CALPHAD-based ICME Design for Additive Manufacturing: Successes and Challenges: Wei Xiong1; 1University of Pittsburgh
Phase transformations in laser melting can readily influence the mechanical properties of the 3D printed components. An effective ICME simulation with a reliable prediction of phase transformations becomes essential, and can significantly help additive manufacturing (AM) design with lower costs and reduced development cycles. In this talk, we discuss several cases by applying the CALPHAD approach for powder-bed laser melting simulation and steel powder composition design. A CALPHAD-based ICME framework is established using CALPHAD-informed thermal modeling, CALPHAD and finite element coupled phase transformation, mean-field type simulation for post-processing design. The case studies on steels and Inconel superalloys demonstrate the effectiveness of such a design framework. More importantly, knowledge gaps are identified using case studies. Supporting metallurgical experiments for model calibration and improvement are essential for the successful ICME design. The ICME framework for AM require the collaborative efforts made by mechanical engineering and materials science.
4:50 PM Invited
Efficient Mechanistic Modeling of Additive Manufacturing (AM) Processes: Sergei Burlatsky1; David Furrer2; 1United Technologies Research Center; 2Pratt & Whitney
Additive manufacturing has the potential to unlock unprecedented component geometric design optimization with unique materials on-demand. Additive manufacturing processes are very complicated and have driven considerable effort in the characterization of processes and additively manufactured materials. The known path dependency of final material and component capabilities have naturally driven the focus of AM technology development toward process monitoring and control. Advanced methods to identify and optimize AM build paths are a significant goal for the entire industry. Though various process design and optimization approaches are possible, these approaches often do not readily provide a means for application to arbitrary geometries with understanding of causal relationships between processing parameters and final build quality. There is a need for computationally efficient modeling tools and methods that can simulate AM processes for rapid process development and part qualification.