Digital Transformation in Metals Processing (Repeat)

_{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}