Purveyors of Processing Science and ICME: A SMD Symposium to Honor the Many Contributions of Taylan Altan, Wei Tsu Wu, Soo-Ik Oh, and Lee Semiatin: Additive Manufacturing
Sponsored by: TMS Structural Materials Division, TMS: Shaping and Forming Committee, TMS: Titanium Committee
Program Organizers: Adam Pilchak, Pratt & Whitney; Ayman Salem, MRL Materials Resources LLC; Viola Acoff, University of Mississippi; Nathan Levkulich, UES; Michael Glavicic, Rolls-Royce; Yufeng Zheng, University of North Texas; John Joyce-Rotella, Air Force Research Laboratory
Monday 2:30 PM
February 24, 2020
Room: 30E
Location: San Diego Convention Ctr
Session Chair: Aymn Salem, MRL Materials Resources LLC; Pete Collins, Iowa State University
2:30 PM Invited
An (incomplete) ICME Framework for Modeling Additive Manufacturing: Peter Collins1; Thomas Ales1; Andrew Baker2; Yunzhi Wang3; D. Harlow4; Hamish Fraser3; 1Iowa State University; 2The Boeing Company; 3The Ohio State University; 4Lehigh University
While an ICME framework may never be complete, it can be useful to understand some phenomenon and make predictions that impact the understanding of the properties and performance of new materials or new processes. In this extensive work, we use an ICME approach to link experimental data and models to understand some of the important details of additive manufacturing of titanium alloys. Specifically, we link processing to thermal history, their effect of composition by adopting strategies similar to those adopted previously by Semiatin, and then to microstructure, properties, and performance. While we will reflect upon the work that was conducted under a multi-year effort and the impact that the “Purveyors of Processing Science and ICME” have had, we will also aim to be provocative, and consider how the Materials Science and Engineering communities ICME efforts can be linked beyond (i.e., to NDE methods), thus helping practitioners make real engineering decisions.
3:00 PM
Modeling of the Solidification Structure Evolution of Ti-6Al-4V Processed via Electron Beam Powder Bed Fusion: Laurentiu Nastac1; Edwin Schwalbach2; Kevin Chaput2; Todd Butler2; 1University of Alabama; 2Air Force Research Laboratory
A fast-acting discrete source additive manufacturing process model was coupled with a stochastic solidification structure model to predict grain structure evolution of Ti-6Al-4V alloy during Electron Beam Powder Bed Fusion (EB-PBF). The capabilities of the developed model include studying the effects of process parameters (power input, speed, beam shape) and part geometry on solidification conditions and their impact on the resulting solidification structure evolution. Grain size, morphology, and crystallographic orientation can be assessed, and the model can assist in achieving better control of the solidification microstructures and to establish trends in the solidification behavior in AM components. Validation was accomplished based on EB-PBF experiments using an Arcam A2 previously performed at Oak Ridge National Lab and subsequently analyzed at AFRL. It is expected that the approach can be extended to predict solidification structure of superalloys as well as laser PBF processes.
3:30 PM
Mesoscale Simulations of Processing-microstructure Linkages during Additive Manufacturing: Bala Radhakrishnan1; Younggil Song1; Sarma Gorti1; John Turner1; Ranadip Acharya2; Lyle Levine3; 1Oak Ridge National Laboratory; 2United Technologies Research Center; 3National Institute of Standards and Technology
Additive Manufacturing of structural alloys represents a processing regime involving small melt pools, and rapid heating and cooling rates that lead to unique solidification microstructures and microstructures arising from subsequent solid-state transformations in structural alloys. This talk will focus on the ongoing developments in mesoscale modeling at the Oak Ridge National Laboratory through the Exascale Computing Project, ExaAM. The phase field simulations make use of an in-house developed code MEUMAPPS that takes advantage of the state-of-the-art, high performance computing platforms to carry out high fidelity, three-dimensional simulations of solidification and solid-state transformation in structural alloys such as Ti-6Al-4V, and Ni-base alloys 781 and 625. Simulations are compared with measurements performed in AM processed materials. Research performed at ORNL under contract DE-AC05-00OR22725 and supported by the Exascale Computing Project 17-SC-20-SC, and the Advanced Manufacturing Office through the HPC for Manufacturing Program at the Department of Energy.
4:00 PM Break
4:20 PM Invited
Role of Thermo-mechanical-chemical Transients: Relevance to Welding and Additive Manufacturing of Structural Metals: Sudarsanam Babu1; 1University of Tennessee, Knoxville
Manufacturability of metallic components, with simple or complex shapes, require comprehensive description of their constitutive behavior. In this paper, thermal, mechanical and chemical transients introduced during welding and additive manufacturing (AM) will be reviewed. Role of these transients on solid, liquid, gas and plasma phase stabilities will be discussed with recent results from iron, aluminum, nickel and titanium-based alloys. The role of liquid-plasma instability on creation of non-equilibrium phases within spatter, during laser powder bed processing of 316 stainless steels, will be presented. The creation of defects due to interplay of keyhole, gas porosities and lack of fusion in Al-Si-Mg alloys will be discussed and ability to mitigate the same using innovative scan strategies will be presented. Similarly, the potential to use innovative scanning strategies to create site-specific texture/properties in nickel base and titanium alloys will be introduced. Finally, these results will be tied to the qualification of AM components.
4:50 PM Invited
Optimizing Metals Additive Manufacturing: Aaron Stebner1; 1Colorado School of Mines
The Alliance for the Development of Additive Processing Technologies (ADAPT) is developing a combined physics – machine learning platform for assessing Process-Structure-Property relationships in metals additive manufacturing. In this presentation, we will show how such a framework can be used to optimize parts, processes, and materials for additive manufacturing, resulting in reduced times and costs for qualifications. The parts example will cover rapid qualification of a 17-4 Stainless Steel door hinge for an Army ground vehicle. The materials example will document ways and means to make laser powder bed fusion manufactured Inconel 718 stronger & more ductile than wrought material. The processes example will show how machine learning can be used to determine the parameters for a new printer that has a more powerful laser than previous generations of machines, with verification carried out in printing Ti-64 coupons.
5:20 PM
Metallic Alloy Microstructure Selection during Rapid Solidification and Additive Manufacturing: Amy Clarke1; Joseph McKeown2; Jonah Klemm-Toole1; Alec Saville1; Chandler Becker1; Benjamin Ellyson1; Yaofeng Guo1; Chloe Johnson1; Brian Milligan1; Andrew Polonsky3; Kira Pusch3; Kester Clarke1; Hunter Martin4; Damien Tourret5; Alain Karma6; Sven Vogel7; Niranjan Parab8; Tao Sun8; Kamel Fezzaa8; Tresa Pollock3; 1Colorado School of Mines; 2Lawrence Livermore National Laboratory; 3University of California, Santa Barbara; 4HRL Laboratories; 5IMDEA Materials; 6Northeastern University; 7Los Alamos National Laboratory; 8Advanced Photon Source, Argonne National Laboratory
The solidification of metallic alloys is spatially and temporally multiscale. Solidification theory predicts that combinations of thermal gradient (G) and solid-liquid interface velocity (V) dictate microstructure selection. In-situ characterization is providing new opportunities to fundamentally understand metallic alloy solidification dynamics and microstructure selection toward the development of experimentally-validated modeling. Here we highlight in-situ imaging of rapid (m/s) solidification in Al alloys after laser melting with dynamic transmission electron microscopy (DTEM) at sub-micron and sub-second scales. We also image laser-melt pool interactions and solidification dynamics during simulated additive manufacturing of Al and Ni alloys at the Advanced Photon Source at Argonne National Laboratory, and explore the role of AM scan strategy on texture as a function of build height in Ti-6Al-4V by neutron diffraction. In-situ and complementary ex-situ characterization will enable the development of multiscale microstructure selection models and the prediction and control of metallic alloy solidification dynamics for advanced manufacturing.