Additive Manufacturing: On Alloy Design Webinar Series

Live Events: January 19, 21, and 26, 2021, from 1 p.m.–2 p.m. ET

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This three-part webinar series delves deeper into alloy design, a topic explored in an earlier roundtable discussion. TMS is reconvening experts to discuss the design of new alloys suited for metal additive manufacturing (AM), with a focus on computational approaches and experimental approaches. The topics will cover important aspects of processing and postprocessing in alloy design. In particular, the presenters will discuss ICME-based alloy design and optimization for AM and special features of Laser AM processes that can be exploited to design new alloys.

This webinar series consists of three sessions, from January 19 to January 26, 2021. Register once for access to all three webinars live or on demand.

What You Will Experience

  • Gain an understanding of the approaches being used to design alloys for AM
  • Know the metallurgical considerations for phase formation/microstructural evolution during AM in the design of new alloys
  • Learn why a need exists for new alloys in AM

Registration

  Cost
Member Free
Nonmember $100

If you require a certificate of participation for attending this webinar, please send a request by e-mail.

Session 1 - Tuesday, January 19, 2021, 1-2 pm ET

"Alloy Design for Additive Manufacturing: Goals and Experimental Tools"

Moderator

Allison M. Beese

Allison M. Beese
Associate Professor, Materials Science and Engineering and Mechanical Engineering, Pennsylvania State University

Bio
Allison Beese received her B.S. in Mechanical Engineering from Penn State. She then worked at Knolls Atomic Power Laboratory before entering graduate school. She earned her M.S. and Ph.D. degrees in Mechanical Engineering from MIT. She spent two years as a postdoctoral fellow at Northwestern University, and joined the Materials Science and Engineering Department at Penn State in 2013. Her group focuses on identifying processing-structure-property links in metals through experimental characterization of microstructures and mechanical properties. In turn, they work to develop appropriate computational models, that consider microstructure, to describe the large deformation and fracture behavior of these materials. She has received an NSF CAREER award, the 2018 TMS AIME Robert Lansing Hardy Award, the 2017 International Outstanding Young Researcher in Freeform and Additive Manufacturing award, and a 3M non-tenured faculty award.

Presenter

Eric Jägle

Eric Jägle
Professor, University of the Bundeswehr Munich

In this presentation, I will talk on the one hand about some special features of Laser AM processes that can be exploited to design new alloys. This includes the unique time-temperature profiles experienced by the material and in-situ metal-gas reactions. On the other hand, I will talk about our approaches to rapidly test different alloy compositions in Laser-AM-processing, including laser re-melting and powder mixtures.

Bio
Eric Jägle studied materials science at the University of Stuttgart, Germany, receiving a Dipl.-Ing. degree with distinction in 2006. In 2006-2007, he spent one year at the University of Cambridge. In the M.Phil. course in Materials Modelling, he worked with H.K.D.H. Bhadeshia on simulating the origin of banding in hot-rolled steel. Afterwards, he returned to Stuttgart for his Ph.D. at the Max-Planck-Institut für Metallforschung (MPI for Metals Research) under the supervision of Prof. E. J. Mittemeijer. His work focused on the mesoscopic simulation of microstructure development during phase transformations, in particular during recrystallization. After receiving his Ph.D. in 2011 with distinction, he moved to the Max-Planck-Institut für Eisenforschung (MPI for Iron Research) in Düsseldorf, Germany. There, he worked as post-doctoral researcher in the department of Prof. D. Raabe on Atom Probe Tomography analysis of electrical steels, precipitation transformations and mechanical alloying. In 2015 he became leader of a newly-formed group in the same department working on alloys for Additive Manufacturing. The group focuses on various aspects of alloys used in AM such as particle reinforcement, in-process strengthening reactions, hot cracking behaviour, residual stress and in-process metal-gas reactions. The investigated materials include steels, Ni- and Al- based alloys and composites. In 2020, he moved to the Institute of Materials Science of the Bundeswehr University Munich as full professor, continuing his work on materials for additive manufacturing.

Session 2 - Thursday, January 21, 2021, 1-2 pm ET

"ICME-based Alloy Design and Optimization for Additive Manufacturing"

Moderator

Allison M. Beese

Allison M. Beese
Associate Professor, Materials Science and Engineering and Mechanical Engineering, Pennsylvania State University

Presenter

Dana Frankel

Dana Frankel
Manager of Design and Product Development, QuesTek Innovations LLC

Traditional wrought and cast alloys are not optimized for processing via AM, and in some cases may not be printable at all. Furthermore, while there is a growing interest in use of AM parts as-built or with minimal post processing, design of optimized AM post-processing (heat treatment, HIP, surface finish, etc.) is often critical to achieving performance goals. By applying Integrated Computational Materials Engineering (ICME)-based models to capture process-structure-property relationships specific to AM, QuesTek has designed alloys that are not just optimized for AM processing, but in fact harness unique AM process conditions to in some cases exceed performance of wrought material. This talk will cover a summary of key challenges associated with designing alloys for AM, an overview of relevant ICME models, and examples of successful AM alloy designs.

Bio
Dana Frankel received her Sc.B. in Materials Science from Brown University before spending time as a materials engineer at Intel, working in quality and reliability, failure analysis, and electron microscopy groups. She went on to earn her Ph.D. in Materials Science from Northwestern University where her research focused on the design of high strength, fatigue resistant low-Ni and Ni-free shape memory alloys for biomedical applications. After graduating, she joined QuesTek Innovations LLC as a Materials Design Engineer. At QuesTek she has worked across a wide variety of alloy systems including lightweight cast alloys, refractory alloys, high entropy alloys, cast iron and steels for structural and tool applications, and design and optimization of alloys tailored for additive manufacturing. As the manager of design and product development at QuesTek, she leads the Design group, oversees alloy design activities, and coordinates strategic development of QuesTek’s IP portfolio.

Session 3 – Tuesday, January 26, 2021, 1-2 pm ET

"Rapid Development of AM Alloys for Aerospace Applications"

Moderator

Allison M. Beese

Allison M. Beese
Associate Professor, Materials Science and Engineering and Mechanical Engineering, Pennsylvania State University

Presenter

Joseph Vinciquerra

Laura C. Dial
Technology Manager of Metallurgy, GE Research

With the proliferation of metal AM, there continues to be a demand for the conversion of traditional casting alloys to various modalities of AM, and in some cases, a need to design new alloys that approximate the performance of their cast counterparts, but are optimized upfront to be more compatible with AM processes. To do this by traditional means is often considered both time- and cost-prohibitive. Through the use of probabilistic machine learning methods, intelligent designs of experiments, and sensor-informed process feedback, GE Research has explored methods to substantially accelerate the alloy design and parameter optimization process for AM alloys intended for aerospace applications. This talk will cover the foundations of the methods utilized, an overview of how the techniques have been applied to successful means, and a study on the development of high-performance superalloys using the approaches described herein.

Bio
Dr. Laura C. Dial is a recognized metallurgy expert whose patents and innovations span numerous manufacturing routes including powder metallurgy, high energy milling, additive manufacturing, and traditional thermo-mechanical processing. As Technology Manager of Metallurgy at GE Research and in her prior role as a Principal Scientist, she has been deeply involved in the design, development, and modification of steel, titanium, cobalt, and nickel-based alloys for power generation and aircraft engine applications. Most recently, Dr. Dial has led groundbreaking work in accelerating the cycle time of alloy and process development for additive manufacturing, resulting in a new family of superalloys designed for direct metal laser melting (DMLM). Dr. Dial continues to foster strong, multi-disciplinary collaborations, spanning advanced design to artificial intelligence, toward the development of new materials.

Co-Author

Joseph Vinciquerra

Joseph Vinciquerra
Technology Director, GE Research

Bio
Joseph Vinciquerra is an aerospace engineer with more than twenty years of advanced technology development experience. As materials technology director at GE Research, he leads a world-class team of technologists working to shape and deliver the future of materials and processing techniques for GE and its customers. His 200+ member team of research scientists and engineers work across metals, ceramics, composites, coatings, and compounds and bring exceptional depth in material behavior, characterization, inspection, and physical modeling.

Vinciquerra’s technical background is rooted in experimental fracture mechanics of composite materials, to which he has gone on to innovate in composite structures design, coatings development, and life methods. Prior to his current role, he led GE’s research and development efforts associated with additive manufacturing for aerospace applications. His current areas of interest reside at the intersection of material science and digital manufacturing, such as in additive manufacturing, and the use of artificial intelligence and machine learning to accelerate material discovery and process optimization.

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