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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium 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
Sponsorship TMS Structural Materials Division
TMS: Shaping and Forming Committee
TMS: Titanium Committee
Organizer(s) Adam L. Pilchak, US Air Force Research Laboratory
Ayman A. Salem, MRL Materials Resources LLC
Viola L. Acoff, University of Alabama
Nathan Levkulich, UES
Michael George Glavicic, Rolls-Royce
Yufeng Zheng, University of Nevada, Reno
Scope The ability to design and repeatedly produce complex, highly durable components for demanding aerospace applications is generally taken for granted these days, but this was not always the case. Edisonian techniques and institutional knowledge were the prevailing methods to choose alloys and develop processing routes with a primary focus of form over function. Little attention was paid to material microstructure and its evolution over the course of processing, and even fewer attempts were made to model it. This all changed when a small group of scientists and engineers came together at Battelle Memorial Institute in the late 1970’s and worked on a wide range of metals processing techniques. Their early success, leveraging the momentum building in the steel industry during World War 2, stemmed from their combined expertise in mechanics, metallurgy, processing science, and computational methods. Their work was constantly advancing the state of the art and often far before the rest of the world was ready for it. For example, their team was the primary contractor for the very first Air Force Materials Lab Processing Science Program. During this program, the team developed (what we call now) a foundational engineering problem using integrated computational materials science and engineering (ICME, or "ICMSE" in some places) to optimize the process for creating a dual-microstructure/dual-property Ti-6Al-2Sn-4Zr-2Mo disk – and they did this ~25 years before the widespread adoption of ICME in our community.

They were masters of understanding processes and developing practical simulations of them. They devised elegant and convincing validation experiments and paid careful attention to boundary conditions, process parameters, and material behavior under processing conditions. Their work at Battelle and that which followed when they each went their separate ways has touched every facet of metals processing including: solid, liquid, and vapor phase processes, power and wrought metallurgy, conventional and solid state joining processes, high-speed machining processes, and additive manufacturing (a decade before the current explosion of effort). Their work touched a vast array of technologically important materials including titanium and its alloys, nickel-base and cobalt-base superalloys, aluminum alloys, various intermetallics, and high entropy alloys, among others. Within these alloy systems, the honorees have contributed well over 1,000 papers to the body of literature on analytical and numerical modeling of microstructure and texture evolution and collectively advised over 200 graduate students! Their work led to the formation of a small business focused on simulating virtually every aspect of the metals processing value stream in the early 1990’s. This company continues to thrive today and is an integral part of the aerospace metals supply chain that produces flight-critical rotating components. The contributions of Taylan Altan, Wei Tsu Wu, Soo-Ik Oh, and Lee Semiatin to the field of processing science are so vast and impactful that it is the Structural Materials Division’s great pleasure to honor their lifetime of achievements at TMS 2020.

Paying homage to the honorees lifelong commitment to developing and validating process models, this symposium will remain alloy-agnostic and instead keep central themes of processing, process simulation, and modeling the evolution of microstructure/texture/defects during processing. Hence, this symposium seeks papers on any metallic material system in the following areas: (1) wrought processing, (2) powder production, (3) powder processing, (4) melting and casting, (5) solid-state joining operations, (6) additive manufacturing, (7) machining operations, and (8) application of numerical methods in processing. Preference will be given to papers that combine experiment with modeling for greater insight into material behavior and also those that span more than one of the above topic areas. Invited speakers from academia and government labs will highlight the honoree’s technical breadth and depth while those from industry will highlight the impact of their work in a production environment.
Abstracts Due 07/15/2019
Proceedings Plan Planned: Supplemental Proceedings volume
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Road Map of Four Decade Journey to Modeling Thermo-mechanical Processes and Microstructure Evolution.
An (incomplete) ICME framework for modeling additive manufacturing
Annealing Twins in Wrought Polycrystalline Superalloys
Application of the CALPHAD Method in the Framework of ICME
Architecture of Hybrid Materials Produced by Severe Plastic Deformation for Industrial Applications
Austenite Transformation Behavior During Tensile Deformation of Quenched and Partitioned Steels
Development and calibration of numerical meso-scale models of microstructure evolution for concurrent recovery, recrystallization, and grain growth with Zener pinning
DIRECTIONAL RECRYSTALLIZATION PROCESSING
Engineered Residual Stress Optimization and Utilization
Enhancing the properties of Ni-based superalloys via mesoscale engineering
Genomic Materials Design: Science-Based Engineering
Heterogeneous deformation during forming of pure niobium and its influence on superconducting radio frequency cavity performance
Hierarchical multiscale modeling of microtextured regions in Ti-6242 during alpha/beta processing
Integrated Approaches to Alloy Industrialization Using Numerical Simulation and Physical Modeling
Integrated Materials Development and Manufacturing Process Simulation for Aerospace Applications
Manipulation of microstructure and microstructural gradients through dehydrogenation of hydrogen-sintered Ti-6Al-4V
Martensitic Variant Selection Under Applied Stress: A New Approach Applied to the β→𝛼 Transformation in Titanium
Mesoscale Simulations of Processing-Microstructure Linkages during Additive Manufacturing
Metallic Alloy Microstructure Selection during Rapid Solidification and Additive Manufacturing
Microstructure control of nickel based superalloy forgings: a focus onto post-dynamic evolutions
Microstructure Underpins Processing
Modeling Beta Phase Texture Evolution during Alpha+Beta Forging To Understand Precursors to Coarse (“Abnormal”) Grain Formation
Modeling of the Solidification Structure Evolution of Ti-6Al-4V Processed via Electron Beam Powder Bed Fusion
Modeling Pore Closure in Titanium Alloys
On the Mechanical Behavior of Keyhole-Free Friction Stir Welded Copper-Aluminum Spot Joints
Optimizing Metals Additive Manufacturing
Processing heterostructured materials for superior mechanical properties
Pushing the performance limits of metallic alloys through severe plastic deformation processing
Regulating plastic deformation by structural phase transformations and vice versa for unprecedented mechanical properties
Role of thermo-mechanical-chemical transients: Relevance to Welding and Additive Manufacturing of structural metals
The Challenges of Refractory Alloy Processing: A Case Study in a Refractory Complex Concentrated Alloy
The effects of alpha-beta interaction on the texture development of Zr and Ti alloys during hot working
The Evolution of Abnormal Grains structures during Beta Annealing of Ti-64 Wrought Products
THE OPERATION OF A RESEARCH SCALE ESR FURNACE AND ITS IMPACT ON TRACE ELEMENTS
The role of plastic rotation in the breakdown of colony microstructures in two phase titanium alloys
Towards Rapid Throughout Measurement of Grain Boundary Properties
Transformations, Recrystallization, Microtexture and Plasticity in Titanium Alloys
Zoning Thermomechanical Process History Data Using Unsupervised Machine Learning


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