Past Event

Digital Transformation in Metals Processing (Repeat)

November 10, 2020
Digital Transformation in Metals Processing (Repeat)
Webinar

Location

Zoom Webinar

Co-Sponsored by:

 

 

 

 

 

 

 

 

 

 

 


Overview

The design, testing, and processing of metals is becoming increasingly driven by computation and automation—for instance, gaps in physical models are addressed by machine learning, and additive manufacturing is crossing from prototyping to production. These developments foreshadow a digital transformation in the manufacturing of metal components and structures, optimizing performance across scales, from atoms to meters.

Repeats on November 10 at 7:30pm EST adhering to the same agenda topics with recorded and captioned videos, live discussions and breakout sessions
 

Advanced Materials Research for Breakthrough Technologies

Advances in materials science and engineering are key components of the innovation process.  In this four-part series we highlight areas of materials research driving breakthroughs in technology.

  • After Moore's Law (Oct. 14 at 10:30am EST and again on Oct. 21 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)
  • Spintronics: Putting a spin on electronics for low-power computing (Oct. 21 at 10:30am EST and again on Oct. 27 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)
  • Microphotonics Everywhere (Oct. 28 at 10:30am EST and again on Nov. 3 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)
  • Digital Transformation in Metals Processing (Nov. 4 at 10:30am EST and again on Nov. 10 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)

Each 2-hour webinar will feature two faculty speakers who will provide complementary perspectives on technology challenges and opportunities and provide an overview of related research activities at MIT.  Ten students will also give short presentations on their recent research results, followed by parallel break-out sessions for detailed discussions.

 

  • Overview

    The design, testing, and processing of metals is becoming increasingly driven by computation and automation—for instance, gaps in physical models are addressed by machine learning, and additive manufacturing is crossing from prototyping to production. These developments foreshadow a digital transformation in the manufacturing of metal components and structures, optimizing performance across scales, from atoms to meters.

    Repeats on November 10 at 7:30pm EST adhering to the same agenda topics with recorded and captioned videos, live discussions and breakout sessions
     

    Advanced Materials Research for Breakthrough Technologies

    Advances in materials science and engineering are key components of the innovation process.  In this four-part series we highlight areas of materials research driving breakthroughs in technology.

    • After Moore's Law (Oct. 14 at 10:30am EST and again on Oct. 21 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)
    • Spintronics: Putting a spin on electronics for low-power computing (Oct. 21 at 10:30am EST and again on Oct. 27 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)
    • Microphotonics Everywhere (Oct. 28 at 10:30am EST and again on Nov. 3 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)
    • Digital Transformation in Metals Processing (Nov. 4 at 10:30am EST and again on Nov. 10 at 7:30pm EST adhering to the same agenda topics, recorded and captioned videos, live discussions and breakout sessions)

    Each 2-hour webinar will feature two faculty speakers who will provide complementary perspectives on technology challenges and opportunities and provide an overview of related research activities at MIT.  Ten students will also give short presentations on their recent research results, followed by parallel break-out sessions for detailed discussions.

     


Agenda

7:30pm
Director, MIT Corporate Relations/Industrial Liaison Program
Jewan Bae
Director, MIT Corporate Relations/Industrial Liaison Program

Jewan John Bae comes to MIT Corporate Relations with more than 20 years of experience in the specialty chemicals and construction industries. He facilitates fruitful relationships between MIT and the industry, engaging with executive level managers to understand their business challenges and match them with resources within the MIT innovation ecosystem to help meet their business objectives.

Bae’s areas of expertise include new product commercialization stage gate process, portfolio management & resource planning, and strategic planning. He has held various business leadership positions at W.R. Grace & Co., the manufacturer of high-performance specialty chemicals and materials, including Director of Strategic Planning & Process, Director of Sales in the Americas, and Global Strategic Marketing Director. Bae is a recipient of the US Army Commendation Medal in 1986.

Director, Materials Research Laboratory (MRL)
Carl Thompson
Carl V. Thompson
Director, Materials Research Laboratory (MRL)

Professor Thompson joined the MIT faculty in 1983. He is Director of MIT’s Materials Research Laboratory and co-Director of the Skoltech Center for Electrochemical Energy Storage. His research interests include processing of thin films and nanostructures for applications in microelectronic, microelectromechanical, and electrochemical systems. Current activities focus on development of thin film batteries for autonomous microsystems, IC interconnect and GaN-based device reliability, and morphological stability of thin films and nano-scale structures. Thompson holds an SB in materials science and engineering from MIT and a PhD in applied physics from Harvard University.

7:35pm
Danae and Vasilis Salapatas Professor of Metallurgy
Christopher Schuh
Christopher Schuh
Danae and Vasilis Salapatas Professor of Metallurgy

Christopher A. Schuh is the Danae and Vasilis Salapatas Professor of Metallurgy in the Department of Materials Science and Engineering at MIT. 

Schuh’s academic training in Materials Science and Engineering focused on metals, including their processing, microstructure, and mechanics. He earned his B.S. from the University of Illinois at Urbana-Champaign in 1997, and his Ph.D. from Northwestern University in 2001. He held the Ernest O. Lawrence postdoctoral fellowship at Lawrence Livermore National Laboratory 2001-2002, prior to joining the MIT faculty in 2002.

Prof. Schuh’s research is focused on structural metallurgy and seeks to control disorder in metallic microstructures for the purpose of optimizing mechanical properties; much of his work is on the design and control of grain boundary structure and chemistry. Prof. Schuh has published more than 250 papers and dozens of patents, and has received a variety of awards acknowledging his research accomplishments. 

Prof. Schuh has co-founded a number of metallurgical companies. His first MIT spin-out company, Xtalic Corporation, commercialized a process from Schuh’s MIT laboratory to control the internal structure in metal electroplated coatings down to the nanometer scale, producing exceptional mechanical and functional properties. These nanocrystalline coatings have been deployed in applications ranging from machine components, to automotive parts, to electronics, and are in wide and growing usage around the globe. Prof. Schuh also cofounded Desktop Metal, a metal additive manufacturing company producing 3D metal printers that address markets ranging from prototyping, to shop-scale, to production scale.

In 2011 Prof. Schuh was appointed Head of the Department of Materials Science and Engineering at MIT, a position he filled until the end of 2019. During his tenure as Head the department saw a significant expansion of the faculty ranks, a major reconfiguration of their physical spaces at the heart of the MIT campus, and the roll-out of online materials science courses that have expanded the exposure of MIT’s Materials Science and Engineering program to learners from all over the globe. He also currently serves as the Coordinating Editor of the Acta Materialia family of journals, including Acta Materialia, Scripta Materialia, Acta Biomaterialia, and Materialia, the last of which he launched in 2018. Among his various awards and honors are his appointment as a MacVicar Fellow of MIT, acknowledging his contributions to engineering education, and his election as member of the National Academy of Inventors and the National Academy of Engineering.

(Christopher Schuh video starts at time stamp: 7:10)

Over the past several decades the iterative trial-and-error approach to alloy design has become dramatically ‘digitally enhanced’.  Physically-motivated computational models that incorporate thermodynamics, kinetics, and processing pathways can substantially narrow the search for optimum alloy compositions and configurations, while high-throughput experimental methods accelerate iteration. In advanced research areas where the controlling physics are not always known, computation can be augmented with data science and machine learning methods to span vast compositional spaces where few experiments exist. This talk will highlight projects of MIT faculty contributing to the digital transformation of the innovative ‘front-end’ of the metals industry—the design and reduction-to-practice of new alloys.

8:05pm
Director, Center for Additive and Digital Advanced Production Technologies (APT)
Director, Laboratory for Manufacturing and Productivity
Professor of Mechanical Engineering, MIT Department of Mechanical Engineering
John Hart
Director, Center for Additive and Digital Advanced Production Technologies (APT)
Director, Laboratory for Manufacturing and Productivity
Professor of Mechanical Engineering

John Hart is Professor of Mechanical Engineering, Director of the Center for Additive and Digital Advanced Production Technologies, and Director of the Laboratory for Manufacturing and Productivity, at MIT. John’s research group at MIT, the Mechanosynthesis Group, aims to accelerate the science and technology of production via advancements in additive manufacturing, nanostructured materials, and precision machine design. In 2017 and 2018, respectively, he received the MIT Ruth and Joel Spira Award for Distinguished Teaching in Mechanical Engineering and the MIT Keenan Award for Innovation in Undergraduate Education. He is a co-founder of Desktop Metal and VulcanForms, and a Board Member of Carpenter Technology Corporation.

Manufacturing of metal components is essential to every major industry, consumes significant natural resources, and involves complex supply chains. The promise of a digital thread from alloy formulation to scaled production and potential re-use therefore has inspired new experimental approaches and manufacturing techniques that go hand-in-hand with computational methods. This talk will highlight MIT research in the “hands-on” side of metals processing—including high-throughput laboratory techniques, in situ characterization of deformation and microstructure, new additive manufacturing processes, and resource-efficient extraction. An outlook will be framed in terms of the value chains of key industries, pathways for commercialization, and business models enabled by digital transformation.

8:35pm

Invited Presentation Lightning Talks – Invited MIT Students and Postdocs

As part of the program for this webinar, we are offering breakout discussions with our presenting graduate students and postdocs. In order to participate in these breakout rooms, you will need the latest version of Zoom (version 5.3.2). (If you need help determining your version of Zoom, please follow the instructions here.) 

If you do not already have this version, please update your Zoom client/application before joining the discussionFollow the instructions here to update Zoom.

8:55pm

Parallel Lightning Presentation Break-out Discussions

Room #

Invited Presenter

Position

Title and Abstract

Supervisor

1

Austin Ward

Graduate Student

Ultrasonic additive manufacturing of nanostructured composites
Nanostructured materials have attractive functional and mechanical properties and can be produced economically as powders or foils. However, consolidating nanostructured materials into bulk forms is challenging because their nanoscale structures tend to coarsen during high-temperature densification processes. To overcome this challenge, we have used a low-temperature net-shaping process, termed ultrasonic additive manufacturing, to fabricate bulk laminar composites from nanostructured feedstock. Through modeling and specialized experiments, we determined processing conditions that preserve the nanostructure of the feedstock while giving strong metallurgical bonding. This talk highlights the processing, properties, and potential applications of bulk nanostructured materials fabricated via ultrasonic consolidation.

Prof. Zachary Cordero

2

Clay Houser

Graduate Student

TRIP Steels for Additive Manufacturing
Metal additive manufacturing (AM) for structural components is limited by low durability properties (toughness, fatigue strength) from microscale porosity inherent to the AM deposition process. TRIP steels have the potential to greatly enhance toughness while maintaining high strength and tolerate the porosity from AM. A parametric design approach utilizing CALPHAD methods and process-structure-property modeling was employed to computationally optimize composition and processing. The TRIP steel design presents high strength, optimal austenite stability, and solidification characteristics like known printable steels. The design approach represents a method that can be applied to a wide variety of material systems.

Prof. Gregory Olson

3

Malik Wagih

Graduate Student

Machine Learning for Design of Grain Boundaries in Metals
In metals, the segregation of solute atoms at grain boundaries can strongly affect the macroscopic material properties, including mechanical, electrochemical, electrical, and magnetic properties. As such, controlling grain boundary segregation is an essential tool for many engineering applications. However, due to the complex disordered nature of grain boundary structures, there is generally a very limited understanding of the nature of solute segregation in polycrystalline alloys, and a lack of databases of segregation information relevant to them. In this talk, we discuss our combined high-throughput atomistic and machine learning approach to understand and predict solute segregation at grain boundaries in metal alloys.

Prof. Chris Schuh

4

Sam McAlpine

Graduate Student

Predicting Phase Stability in Refractory High Entropy Alloys with the Vacancy Exchange Potential
High entropy alloys (HEAs) are potential next-generation structural materials, yet accurate prediction of phase stability remains a challenge. Refractory HEAs (RHEAs) in particular can push the limits of high-temperature strength. We perform first-principles calculations to determine the vacancy exchange potential in several RHEAs. Results and experimental confirmation indicate that a zero/negative vacancy exchange potential indicates phase instability, while elemental trends of the vacancy exchange potential predict segregation in single-phase refractory HEAs. If these results hold across a broad range of systems, the vacancy exchange potential can serve as a rapid predictor of phase stability within the vast HEA compositional domain.

Prof. Michael Short

5

Kang Pyo So

Research Scientist

High-performance nanoengineered composites at the ton-scale
One-dimensional (1D) dispersion-strengthened composites possess superior mechanical, thermal, and radiation resistance properties when compared to alloys. Furthermore, these benefits are achieved without reducing ductility, thermal conductivity, and electrical conductivity. We advance 1D composites in four directions: i) Engineering intra-granular dispersions by nanoscale cold welding; ii) Optimizing aluminum-carbon nanotube composites with the benefits aforementioned; iii) Utilizing scientific machine learning to identify and characterize microstructure defects in 1D composites; iv) Designing and further demonstrating the infrastructure and processes to achieve ton-scale production of various 1D composite material systems, potentially pioneering the widespread adoption of these composites in the energy and transportation sectors.

Prof. Michael Short

6

Zach Jensen

Graduate Student

Linking Literature Data Extraction with Domain Specific Materials Informatics
This project’s goal is to advance materials synthesis using data and computational models. Most synthesis information is found in literature requiring natural language processing to extract and format. We developed a computational pipeline that automatically downloads millions of materials science articles and extracts synthesis data such as the precursors, types of operations, target materials, and synthesis conditions. In addition, we developed techniques to extract from other important sources of data including tables, figures, and patents. Combining these methods in domain-informed ways provides data useful for machine learning and data mining informing the synthesis of novel materials and improving existing synthesis.

Prof. Elsa Olivetti

7

Kaihao Zhang

Post Doc

Dropwise digital printing of metals
Metal additive manufacturing (AM) has the potential to impact an immense range of applications across industries. However, many established metal AM processes do not easily permit multi-material printing or printing directly onto existing objects. We present a high resolution direct metal printing method that potentially addresses these limitations. Individual metal microparticles are electrohydrodynamically ejected on-demand from a meniscus and laser-melted in-flight before landing and solidifying on a substrate. We demonstrate printing of solder and platinum particles (ø ~ 30-150 μm) and explore the process parameter space as limited by the ejection conditions and the kinetics of melting, impact, and solidification.

A. John Hart

8

Gianluca Roscioli

Graduate Student

How physical insights transform processing of cutting tools
The growing field of data analytics and machine learning has created an emphasis on data quantity in recent years. This short talk, instead, aims to highlight the importance of data quality and physical insights to improve efficiency in materials discovery. By designing in-situ scanning electron microscopy experiments and corresponding computational simulations, we addressed one of the oldest materials challenges: failure of cutting tools. Running parametric simulations, we were able to show that sharp edges ultimately fail due to the heterogeneity present in the material, thus defining new material design guidelines for improved cutting tools.

Prof. Cem Tasan

9

Shaolou Wei

Graduate Student

Expediting alloy design by using natural mixing characteristics
Application-worthy metallic alloys with multi-principal alloying elements are often challenging to identify and design because of the large number of compositional combinations. Starting with a nine-element mixture and assessing the phase-separated zones that arose from natural mixing, we will showcase the development of Ti-V-Nb-Hf refractory high-entropy alloys (RHEAs) with attractive mechanical and physicochemical properties. We will reveal that this strategy provides a method for screening and isolating complex element compositions that may have outstanding performances.

Prof. Cem Tasan

10

Eddie Pang

Graduate Student

A digital revolution in electron backscatter diffraction
Electron backscatter diffraction (EBSD) is a popular materials characterization tool used in process development, quality control, and materials discovery. In the past few years, advanced computational capabilities have given rise to a new family of techniques to analyze EBSD patterns: intensity-based indexing. These emerging methods extract more information than ever before from the data-rich patterns and allow us for the first time to distinguish similar phases (e.g. austenite and martensite in steels) and study highly deformed materials, among other applications. Intensity-based methods are rapidly gaining in popularity and are revolutionizing the way we process EBSD data.

Prof. Chris Schuh

11

Mary Elizabeth Wagner

Graduate Student

New Methods for Thermodynamic Predictions in the Realm of Limited Data
Development of novel materials is hindered by lack of information about thermodynamic solution properties, which must be determined through experiments or modeling. Current models, however, are unable to accurately predict the behavior of complex multicomponent liquids, and gathering sufficient experimental data for a full analysis is lengthy and expensive. Herein, a new method for thermodynamic modeling of high temperature liquids is presented. Requiring only a fraction of the experimental data needed for the CALPHAD method and far less calculations than first-principles models, this method is a fast, efficient, and accurate way to quickly probe new materials systems.

Prof. Antoine Allanore

9:30pm

Wrap-up
  • Agenda
    7:30pm
    Director, MIT Corporate Relations/Industrial Liaison Program
    Jewan Bae
    Director, MIT Corporate Relations/Industrial Liaison Program

    Jewan John Bae comes to MIT Corporate Relations with more than 20 years of experience in the specialty chemicals and construction industries. He facilitates fruitful relationships between MIT and the industry, engaging with executive level managers to understand their business challenges and match them with resources within the MIT innovation ecosystem to help meet their business objectives.

    Bae’s areas of expertise include new product commercialization stage gate process, portfolio management & resource planning, and strategic planning. He has held various business leadership positions at W.R. Grace & Co., the manufacturer of high-performance specialty chemicals and materials, including Director of Strategic Planning & Process, Director of Sales in the Americas, and Global Strategic Marketing Director. Bae is a recipient of the US Army Commendation Medal in 1986.

    Director, Materials Research Laboratory (MRL)
    Carl Thompson
    Carl V. Thompson
    Director, Materials Research Laboratory (MRL)

    Professor Thompson joined the MIT faculty in 1983. He is Director of MIT’s Materials Research Laboratory and co-Director of the Skoltech Center for Electrochemical Energy Storage. His research interests include processing of thin films and nanostructures for applications in microelectronic, microelectromechanical, and electrochemical systems. Current activities focus on development of thin film batteries for autonomous microsystems, IC interconnect and GaN-based device reliability, and morphological stability of thin films and nano-scale structures. Thompson holds an SB in materials science and engineering from MIT and a PhD in applied physics from Harvard University.

    7:35pm
    Danae and Vasilis Salapatas Professor of Metallurgy
    Christopher Schuh
    Christopher Schuh
    Danae and Vasilis Salapatas Professor of Metallurgy

    Christopher A. Schuh is the Danae and Vasilis Salapatas Professor of Metallurgy in the Department of Materials Science and Engineering at MIT. 

    Schuh’s academic training in Materials Science and Engineering focused on metals, including their processing, microstructure, and mechanics. He earned his B.S. from the University of Illinois at Urbana-Champaign in 1997, and his Ph.D. from Northwestern University in 2001. He held the Ernest O. Lawrence postdoctoral fellowship at Lawrence Livermore National Laboratory 2001-2002, prior to joining the MIT faculty in 2002.

    Prof. Schuh’s research is focused on structural metallurgy and seeks to control disorder in metallic microstructures for the purpose of optimizing mechanical properties; much of his work is on the design and control of grain boundary structure and chemistry. Prof. Schuh has published more than 250 papers and dozens of patents, and has received a variety of awards acknowledging his research accomplishments. 

    Prof. Schuh has co-founded a number of metallurgical companies. His first MIT spin-out company, Xtalic Corporation, commercialized a process from Schuh’s MIT laboratory to control the internal structure in metal electroplated coatings down to the nanometer scale, producing exceptional mechanical and functional properties. These nanocrystalline coatings have been deployed in applications ranging from machine components, to automotive parts, to electronics, and are in wide and growing usage around the globe. Prof. Schuh also cofounded Desktop Metal, a metal additive manufacturing company producing 3D metal printers that address markets ranging from prototyping, to shop-scale, to production scale.

    In 2011 Prof. Schuh was appointed Head of the Department of Materials Science and Engineering at MIT, a position he filled until the end of 2019. During his tenure as Head the department saw a significant expansion of the faculty ranks, a major reconfiguration of their physical spaces at the heart of the MIT campus, and the roll-out of online materials science courses that have expanded the exposure of MIT’s Materials Science and Engineering program to learners from all over the globe. He also currently serves as the Coordinating Editor of the Acta Materialia family of journals, including Acta Materialia, Scripta Materialia, Acta Biomaterialia, and Materialia, the last of which he launched in 2018. Among his various awards and honors are his appointment as a MacVicar Fellow of MIT, acknowledging his contributions to engineering education, and his election as member of the National Academy of Inventors and the National Academy of Engineering.

    (Christopher Schuh video starts at time stamp: 7:10)

    Over the past several decades the iterative trial-and-error approach to alloy design has become dramatically ‘digitally enhanced’.  Physically-motivated computational models that incorporate thermodynamics, kinetics, and processing pathways can substantially narrow the search for optimum alloy compositions and configurations, while high-throughput experimental methods accelerate iteration. In advanced research areas where the controlling physics are not always known, computation can be augmented with data science and machine learning methods to span vast compositional spaces where few experiments exist. This talk will highlight projects of MIT faculty contributing to the digital transformation of the innovative ‘front-end’ of the metals industry—the design and reduction-to-practice of new alloys.

    8:05pm
    Director, Center for Additive and Digital Advanced Production Technologies (APT)
    Director, Laboratory for Manufacturing and Productivity
    Professor of Mechanical Engineering, MIT Department of Mechanical Engineering
    John Hart
    Director, Center for Additive and Digital Advanced Production Technologies (APT)
    Director, Laboratory for Manufacturing and Productivity
    Professor of Mechanical Engineering

    John Hart is Professor of Mechanical Engineering, Director of the Center for Additive and Digital Advanced Production Technologies, and Director of the Laboratory for Manufacturing and Productivity, at MIT. John’s research group at MIT, the Mechanosynthesis Group, aims to accelerate the science and technology of production via advancements in additive manufacturing, nanostructured materials, and precision machine design. In 2017 and 2018, respectively, he received the MIT Ruth and Joel Spira Award for Distinguished Teaching in Mechanical Engineering and the MIT Keenan Award for Innovation in Undergraduate Education. He is a co-founder of Desktop Metal and VulcanForms, and a Board Member of Carpenter Technology Corporation.

    Manufacturing of metal components is essential to every major industry, consumes significant natural resources, and involves complex supply chains. The promise of a digital thread from alloy formulation to scaled production and potential re-use therefore has inspired new experimental approaches and manufacturing techniques that go hand-in-hand with computational methods. This talk will highlight MIT research in the “hands-on” side of metals processing—including high-throughput laboratory techniques, in situ characterization of deformation and microstructure, new additive manufacturing processes, and resource-efficient extraction. An outlook will be framed in terms of the value chains of key industries, pathways for commercialization, and business models enabled by digital transformation.

    8:35pm

    Invited Presentation Lightning Talks – Invited MIT Students and Postdocs

    As part of the program for this webinar, we are offering breakout discussions with our presenting graduate students and postdocs. In order to participate in these breakout rooms, you will need the latest version of Zoom (version 5.3.2). (If you need help determining your version of Zoom, please follow the instructions here.) 

    If you do not already have this version, please update your Zoom client/application before joining the discussionFollow the instructions here to update Zoom.

    8:55pm

    Parallel Lightning Presentation Break-out Discussions

    Room #

    Invited Presenter

    Position

    Title and Abstract

    Supervisor

    1

    Austin Ward

    Graduate Student

    Ultrasonic additive manufacturing of nanostructured composites
    Nanostructured materials have attractive functional and mechanical properties and can be produced economically as powders or foils. However, consolidating nanostructured materials into bulk forms is challenging because their nanoscale structures tend to coarsen during high-temperature densification processes. To overcome this challenge, we have used a low-temperature net-shaping process, termed ultrasonic additive manufacturing, to fabricate bulk laminar composites from nanostructured feedstock. Through modeling and specialized experiments, we determined processing conditions that preserve the nanostructure of the feedstock while giving strong metallurgical bonding. This talk highlights the processing, properties, and potential applications of bulk nanostructured materials fabricated via ultrasonic consolidation.

    Prof. Zachary Cordero

    2

    Clay Houser

    Graduate Student

    TRIP Steels for Additive Manufacturing
    Metal additive manufacturing (AM) for structural components is limited by low durability properties (toughness, fatigue strength) from microscale porosity inherent to the AM deposition process. TRIP steels have the potential to greatly enhance toughness while maintaining high strength and tolerate the porosity from AM. A parametric design approach utilizing CALPHAD methods and process-structure-property modeling was employed to computationally optimize composition and processing. The TRIP steel design presents high strength, optimal austenite stability, and solidification characteristics like known printable steels. The design approach represents a method that can be applied to a wide variety of material systems.

    Prof. Gregory Olson

    3

    Malik Wagih

    Graduate Student

    Machine Learning for Design of Grain Boundaries in Metals
    In metals, the segregation of solute atoms at grain boundaries can strongly affect the macroscopic material properties, including mechanical, electrochemical, electrical, and magnetic properties. As such, controlling grain boundary segregation is an essential tool for many engineering applications. However, due to the complex disordered nature of grain boundary structures, there is generally a very limited understanding of the nature of solute segregation in polycrystalline alloys, and a lack of databases of segregation information relevant to them. In this talk, we discuss our combined high-throughput atomistic and machine learning approach to understand and predict solute segregation at grain boundaries in metal alloys.

    Prof. Chris Schuh

    4

    Sam McAlpine

    Graduate Student

    Predicting Phase Stability in Refractory High Entropy Alloys with the Vacancy Exchange Potential
    High entropy alloys (HEAs) are potential next-generation structural materials, yet accurate prediction of phase stability remains a challenge. Refractory HEAs (RHEAs) in particular can push the limits of high-temperature strength. We perform first-principles calculations to determine the vacancy exchange potential in several RHEAs. Results and experimental confirmation indicate that a zero/negative vacancy exchange potential indicates phase instability, while elemental trends of the vacancy exchange potential predict segregation in single-phase refractory HEAs. If these results hold across a broad range of systems, the vacancy exchange potential can serve as a rapid predictor of phase stability within the vast HEA compositional domain.

    Prof. Michael Short

    5

    Kang Pyo So

    Research Scientist

    High-performance nanoengineered composites at the ton-scale
    One-dimensional (1D) dispersion-strengthened composites possess superior mechanical, thermal, and radiation resistance properties when compared to alloys. Furthermore, these benefits are achieved without reducing ductility, thermal conductivity, and electrical conductivity. We advance 1D composites in four directions: i) Engineering intra-granular dispersions by nanoscale cold welding; ii) Optimizing aluminum-carbon nanotube composites with the benefits aforementioned; iii) Utilizing scientific machine learning to identify and characterize microstructure defects in 1D composites; iv) Designing and further demonstrating the infrastructure and processes to achieve ton-scale production of various 1D composite material systems, potentially pioneering the widespread adoption of these composites in the energy and transportation sectors.

    Prof. Michael Short

    6

    Zach Jensen

    Graduate Student

    Linking Literature Data Extraction with Domain Specific Materials Informatics
    This project’s goal is to advance materials synthesis using data and computational models. Most synthesis information is found in literature requiring natural language processing to extract and format. We developed a computational pipeline that automatically downloads millions of materials science articles and extracts synthesis data such as the precursors, types of operations, target materials, and synthesis conditions. In addition, we developed techniques to extract from other important sources of data including tables, figures, and patents. Combining these methods in domain-informed ways provides data useful for machine learning and data mining informing the synthesis of novel materials and improving existing synthesis.

    Prof. Elsa Olivetti

    7

    Kaihao Zhang

    Post Doc

    Dropwise digital printing of metals
    Metal additive manufacturing (AM) has the potential to impact an immense range of applications across industries. However, many established metal AM processes do not easily permit multi-material printing or printing directly onto existing objects. We present a high resolution direct metal printing method that potentially addresses these limitations. Individual metal microparticles are electrohydrodynamically ejected on-demand from a meniscus and laser-melted in-flight before landing and solidifying on a substrate. We demonstrate printing of solder and platinum particles (ø ~ 30-150 μm) and explore the process parameter space as limited by the ejection conditions and the kinetics of melting, impact, and solidification.

    A. John Hart

    8

    Gianluca Roscioli

    Graduate Student

    How physical insights transform processing of cutting tools
    The growing field of data analytics and machine learning has created an emphasis on data quantity in recent years. This short talk, instead, aims to highlight the importance of data quality and physical insights to improve efficiency in materials discovery. By designing in-situ scanning electron microscopy experiments and corresponding computational simulations, we addressed one of the oldest materials challenges: failure of cutting tools. Running parametric simulations, we were able to show that sharp edges ultimately fail due to the heterogeneity present in the material, thus defining new material design guidelines for improved cutting tools.

    Prof. Cem Tasan

    9

    Shaolou Wei

    Graduate Student

    Expediting alloy design by using natural mixing characteristics
    Application-worthy metallic alloys with multi-principal alloying elements are often challenging to identify and design because of the large number of compositional combinations. Starting with a nine-element mixture and assessing the phase-separated zones that arose from natural mixing, we will showcase the development of Ti-V-Nb-Hf refractory high-entropy alloys (RHEAs) with attractive mechanical and physicochemical properties. We will reveal that this strategy provides a method for screening and isolating complex element compositions that may have outstanding performances.

    Prof. Cem Tasan

    10

    Eddie Pang

    Graduate Student

    A digital revolution in electron backscatter diffraction
    Electron backscatter diffraction (EBSD) is a popular materials characterization tool used in process development, quality control, and materials discovery. In the past few years, advanced computational capabilities have given rise to a new family of techniques to analyze EBSD patterns: intensity-based indexing. These emerging methods extract more information than ever before from the data-rich patterns and allow us for the first time to distinguish similar phases (e.g. austenite and martensite in steels) and study highly deformed materials, among other applications. Intensity-based methods are rapidly gaining in popularity and are revolutionizing the way we process EBSD data.

    Prof. Chris Schuh

    11

    Mary Elizabeth Wagner

    Graduate Student

    New Methods for Thermodynamic Predictions in the Realm of Limited Data
    Development of novel materials is hindered by lack of information about thermodynamic solution properties, which must be determined through experiments or modeling. Current models, however, are unable to accurately predict the behavior of complex multicomponent liquids, and gathering sufficient experimental data for a full analysis is lengthy and expensive. Herein, a new method for thermodynamic modeling of high temperature liquids is presented. Requiring only a fraction of the experimental data needed for the CALPHAD method and far less calculations than first-principles models, this method is a fast, efficient, and accurate way to quickly probe new materials systems.

    Prof. Antoine Allanore

    9:30pm

    Wrap-up