Mechanical and Materials Engineering Speakers

Location:
Baldwin Hall 544/644

Time: Fridays, 11:15 – 12:10 p.m. 

Abstract: Many of the world’s healthcare systems are amassing large amounts of patient observational data, including electronic health records, labs, insurance claims, and other sources. Moreover, the information technology infrastructure needed to connect the data has developed rapidly. However, despite the abundance of data, there are many pitfalls associated with using observational data that can lead to poor model performance if not carefully addressed. This presentation will describe opportunities for using a fusion of analytics and operations research approaches to improve medical decisions, drawing on case studies of chronic diseases such as cancer, diabetes, and heart disease. The case studies will illustrate using machine learning, Markov decision processes, and stochastic programming to address decision-making for chronic disease prevention and treatment. Finally, the presentation will cover future opportunities for research and practice related to medicine. 

Bio: Brian Denton is the Stephen M. Pollock Professor of Industrial and Operations Engineering and the Chair of the Department of Industrial and Operations Engineering at the University of Michigan. His research interests are data analytics and data-driven optimization under uncertainty with applications to medicine, public health, and healthcare delivery. He is a Professor in the Department of Urology (by courtesy) at Michigan Medicine and a member of the Institute for Healthcare Policy and Innovation and the Cancer Center at the University of Michigan. His research has been funded by the National Science Foundation, the Agency for Healthcare Research and Quality, the National Institutes of Health, the U.S. Department of Veterans Affairs, and industry research contracts. He is past President of the Institute for Operations Research and the Management Sciences (INFORMS) and is an elected Fellow of INFORMS. 

Information to come. 

Abstract: Advanced processing and manufacturing technologies for aerospace applications require new materials with improved processing and performance characteristics. This presentation will cover challenges and opportunities in materials design and characterization for Air Force relevant polymer matrix composites and review recent changes in the approach to materials discovery and novel chemistries to adapt to a fast-moving field. Specifically, emerging methods and tools to develop and evaluate novel chemistries for high temperature applications, multifunctional composites, agile processing technologies and disruptive tools, such as in-silico materials discovery and autonomous synthesis via artificial intelligence and machine learning will be discussed. The presentation will conclude with some recommendations and future directions for further research and development.

Bio: Dr. Hilmar Koerner is a Research Team Lead in the Composite, Ceramic, Metallic, & Materials Performance Division, Materials and Manufacturing Directorate, Air Force Research Laboratory, Air Force Materiel Command, Wright-Patterson Air Force Base, Ohio. In this position, he directs research on novel high temperature thermosetting polymers, processing science and modeling for advanced composite manufacturing routes.  Scientifically, Hilmar received his PhD at the Technical University of Clausthal in Polymer Science, followed by a Postdoc at Cornell. He is active in numerous technical communities and his research interests center on the physics and chemistry of high temperature polymer thermosets, molecular hybrids, nanocomposites and methods for light weighting and advanced manufacturing applications. He has co-authored more than 175 refereed papers, 70 proceedings, two edited books, and >10 patents. He has been a contributor on ~200 technical presentations with numerous invited/plenary talks. He is active in MRS, ACS (ACS Fellow), APS and SAMPE.

Abstract is forthcoming.

Bio: James E. Cashman III, ME '76, '79, MBA '82, has 45 years' experience in technical, financial, operations and sales management, which have been key to the success of numerous computer software, product data management, transaction processing and computer-aided engineering companies. Most recently Jim was Executive Chairman of ANSYS, Inc., a developer and global marketer of engineering simulation software and technologies that are widely used by engineers and designers across a broad range of industries. He had previously been CEO of ANSYS from 2000 through 2016 when the organization annual revenues from $50 million to over $1 billion and ranked in the Top 5 of public technology companies for total returns. Jim championed Simulation Driven Product Development, which enables organizations to build and validate complete virtual prototypes, resulting in reduced physical prototype testing, faster time to market, and improved market acceptance of innovative new products. Before joining ANSYS, Jim was vice president for international operations, marketing and software development at PAR Technology Corporation; earlier, he was a founder of Metaphase Technology as well as an early-stage member of Structural Dynamics Research Corporation (SDRC), a computer-aided engineering company where he held management positions in international sales, major accounts and market development, and product management. 

Abstract: Artificial Intelligence (AI) is revolutionizing industries worldwide, and its widespread implementation in manufacturing will secure a significant competitive edge for the United States. Because advanced manufacturing operations depend on experience and knowhow, the potential to use AI to enhance production discovery and apply implicit knowledge is high. Dr. Bian will address the current state of advanced manufacturing and AI, identify the main obstacles to AI integration in manufacturing, and underscore the necessity of AI in manufacturing endeavors. By taking additive manufacturing as a case study, Dr. Bian will discuss the previous work on the application of AI on advanced manufacturing, such as understanding the parameter-process-structure relationship, monitoring processes in real-time, and characterizing parts. This talk will also discuss the complex questions that arise when incorporating AI into manufacturing research and education, as well as opportunities for manufacturing engineers.

Bio: Dr. Linkan Bian is a program director in the Advanced Manufacturing (AM) cluster of Civil, Mechanical and Manufacturing Innovation division of National Science Foundation (NSF). At his home institute Mississippi State University, Dr. Bian is the Thomas B. & Terri L. Nusz Endowed Professor in Industrial and Systems Engineering Department. Dr. Bian received his Ph.D. in Industrial and Systems Engineering from Georgia Institute of Technology, and B.S. in Applied Mathematics from Beijing University. The major themes of Dr. Bian’s research focus on understanding the process-structure-property relationships of additive manufacturing, as well as the investigation of how AI/ML can transform the modeling and experimental approaches. Dr. Bian was the president for IISE Quality Control and Reliability Engineering division and is currently serving on the editorial board of multiple ASME and IISE journals.

Abstract: Two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides, known as MXenes, have evolved from a newly discovered material in the past decade into a large family of 2D materials. To date, more than fifty MXene compositions have been synthesized, including Ti2CTx, Mo2CTx, Nb2CTx, Ti3C2Tx, Mo2TiC2Tx, and Mo2Ti2C3Tx. MXenes have a wide array of material properties, including solution-processability and hydrophilicity (surfactant-free nanoinks), high electrical conductivity, high 2D stiffness, functionalized surfaces, and chemical and structural tunability. MXenes have been extensively investigated for applications such as energy storage, catalysis, sensing, environmental, biomedical, electromagnetic interference shielding, and wireless communication.  

In this talk, I will focus on the latest developments in the MXene family and present some of the work that we have started in the past few years, including the first report on high-entropy MXenes, the expansion of ordered double-metal MXenes, the introduction of tungsten-titanium MXene and their unique electrocatalysis behavior, specifically for hydrogen evolution reaction. I will also discuss our recent findings on MXenes defect engineering to control their properties and their high-temperature phase stability and transformation for extreme environment applications. 

Bio: Dr. Babak Anasori is the Reilly Rising Star Associate Professor at Purdue University, with joint appointments at the Schools of Materials Engineering and Mechanical Engineering. He also serves as the Editor-in-Chief of the Graphene and 2D Materials, a Springer-Nature journal. Dr. Babak Anasori received his PhD from Drexel University in 2014 in the Materials Science and Engineering Department, the birthplace of MXenes. Dr. Anasori has authored more than 180 refereed publications on MXenes and their precursors, and he has been recognized as a Web of Science Highly Cited Researcher from 2019 to 2023. He ranked 4th on the 2023 and 2024 lists of Rising Stars of Science in the USA by Research.com. Additionally, ScholarGPS identified him as the number #1 in Mechanical Engineering among all scholars in the USA in the past five years. He has received several international awards, including the 2016 Materials Research Society (MRS) Postdoctoral Award, the 2021 Drexel University 40-under-40, the 2021 Waterloo Institute for Nanotechnology (WIN) Rising Star Award in Nanoscience and Nanotechnology, and the 2024 Abraham Max Distinguished Professor Award at Purdue School of Engineering. Dr. Anasori’s research lab focuses on developing novel 2D carbide and carbonitride MXenes for various applications, including energy generation, electromagnetic interference shielding, and ultra-high temperature ceramics. 

Abstract: Manufacturing has undergone significant changes over the past five-ten years thanks to technological advancements that have been leveraged to meet a diverse set of customer requirements driven by global and societal needs. Conventional manufacturing control strategies were typically designed for robustness and speed within a controlled and well-regulated environment. However, recent demands for customization and agility coupled with big data investments have provided an opportunity for more learning-based methods to be introduced. Data driven strategies have long provided a means of harnessing information to enhance the performance of these complex systems. In this talk, motivated by real-world interest from industry, we will demonstrate how an improved understanding of how to combine data-based learning and experiential knowledge to make intelligent decisions can save time, money, and resources in advanced manufacturing systems.

Bio: Kira Barton is a Professor in the Robotics and Mechanical Engineering Departments at the University of Michigan. She received her B.Sc. in Mechanical Engineering from the University of Colorado at Boulder in 2001, and her M.Sc. and Ph.D. in Mechanical Engineering from the University of Illinois at Urbana-Champaign in 2006 and 2010. She is also serving as the Associate Director for the Automotive Research Center, a University-based U.S. Army Center of Excellence for modeling and simulation of military and civilian ground systems. She was a Miller Faculty Scholar for the University of Michigan from 2017 – 2020. Prof. Barton’s research specializes in advancements in modeling, sensing, and control for applications in smart manufacturing and robotics, with a specialization in learning and multi-agent systems.  Kira is the recipient of an NSF CAREER Award in 2014, 2015 SME Outstanding Young Manufacturing Engineer Award, the 2015 University of Illinois, Department of Mechanical Science and Engineering Outstanding Young Alumni Award, the 2016 University of Michigan, Department of Mechanical Engineering Department Achievement Award, and the 2017 ASME Dynamic Systems and Control Young Investigator Award. Kira was named 1 of 25 leaders transforming manufacturing by SME in 2022, and was selected as one of the 2022 winners of the Manufacturing Leadership Award from the Manufacturing Leadership Council. She became an ASME fellow in 2024.

Abstract: Remanufacturing supports sustainability goals by restoring used products in terms of quality and functionality. Attitudes towards buying remanufactured products and using remanufactured parts in manufacturing are varied across industries, inherently impacting business models. However, the increasing adoption of ESG principles has renewed the positive energy behind remanufacturing. 

With support from the REMADE Institute and in collaboration with industrial companies (Danfoss, John Deere, Volvo, and more) and academic partners, Dr. Kremer led several applied projects in remanufacturing. In this talk, she will present the significant engineering and non-engineering challenges she has experienced and observed across these sponsored projects.

Bio: Gül E. Kremer earned a doctorate in engineering management from the Missouri University of Science and Technology (formerly University of Missouri-Rolla); master's and bachelor's degrees in industrial engineering from Yildiz Technical University in Istanbul, Turkey, and a master’s in business, specializing in production management, from Istanbul University. She has been a National Research Council-US AFRL Summer Faculty Fellow in the Human Effectiveness Directorate from 2002 to 2004, and a Fulbright Scholar (2010-2011). She served as a Program Director in the National Science Foundation’s Division of Undergraduate Education between 2013 and 2016. Dr. Kremer’s research interests include applied decision analysis to improve complex products and systems, and engineering education. The results of her research efforts have been presented in various publications including 3 books and more than 300 refereed publications. Several of her papers have been recognized with Best Paper awards. She is a Fellow of the American Society for Mechanical Engineers (ASME), and served as the Design for Manufacturing and Lifecycle Technical and Chair of Design Education Committees of the Design Engineering Division of ASME.

Abstract:  This talk will combine a number of topics in what is hoped is a lively discussion. Dr. Schmid's laboratories at the University of Notre Dame and University of North Carolina at Charlotte have a long history of collaborating with orthopedic implant manufacturers in research; a number of new implant systems have been developed as a result. These commonly use new strategies in minimally invasive implantation, such as with alternative placement strategies or new materials. Examples of new systems include phase change polymers used in fracture fixation; spot repair of cartilage with hybrid welded-woven implants; medicine-delivering and otherwise active scaffolds; fatigue performance improvement of acrylics used in orthopedics; dynamic fracture repair involving novel, well, guns; novel reamer designs and additive manufactured alternatives to conventional ceramics. 

Bio: Steven R. Schmid was a professor for over 25 years at the University of Notre Dame, and is currently the Belk-Woodward Distinguished Professor at the University of North Carolina at Charlotte.  Of his textbooks, Manufacturing Engineering and Technology (with S. Kalpakjian) is the world's most popular manufacturing textbook. Manufacturing Processes for Engineering Materials, Schey’s Tribology in Metalworking, Fundamentals of Machine Elements and Fundamentals of Fluid Film Lubrication are some of his other books.  He has won numerous best paper and teaching awards. In 2012-2013, Dr. Schmid was the first Faculty Fellow at the Advanced Manufacturing National Program Office, and served as the Associate Director for Research Partnerships. He played a key role in the design of the National Network for Manufacturing Innovation (Manufacturing USA) program. From 2016-2019 he served as Program Director for the Advanced Manufacturing Program at the National Science Foundation, where he started the SME Blue Skies program and reorganized the manufacturing programs into the current over-arching Advanced Manufacturing program.   Dr. Schmid has served as an International Delegate for SME (equivalent to Board member), and as President of the North American Manufacturing Research Institute. He is a Fellow of ASME and SME. He is the Director of the Center for Additive Manufacturing of Advanced Ceramics (CAMAC) at UNC Charlotte. He was awarded the SME Gold Medal in 2019, the Award of Merit (a lifetime achievement award) by the Manufacturing Engineering Society of Spain in 2020, and the David Dornfeld Innovation Award in 2023.

Abstract: Optimal control in large-scale systems, such as energy distribution networks, automated manufacturing, and traffic management, presents significant opportunities to enhance energy efficiency, sustainability, and system resilience. However, these opportunities are accompanied by substantial challenges due to the size and complexity of the problems. Traditional methods, including model order reduction and partitioning techniques, often face issues such as model instability, loss of critical features (e.g., accurate capture of congested traffic flow), and problems with suboptimality and infeasibilities.

In certain cases, such as traffic systems, modeling these systems as partial differential equations (PDEs)—which are continuous in both space and time—offers a more natural representation of interactions between vehicles. This contrasts with traditional multi-agent frameworks that require explicit modeling of interactions, often complicating the optimization process with additional constraints.

Directly operating on the PDEs governing these systems is presented as a compelling alternative. However, this emerging field of PDE optimal control faces challenges such as the infinite dimensionality of the system and the absence of established energy metrics in contexts like traffic management.

In this seminar, the novel concept of macroscopic energy equations will be examined, and the application of Linear Quadratic Regulator (LQR) control for managing traffic flow, particularly in automated and connected vehicles, will be explored. The seminar will conclude by discussing how this novel control framework enables energy minimization through distributed barrier functions and how these principles can be generalized and applied to broader energy systems.

Bio: Dr. Stephanie Stockar received her PhD in Mechanical Engineering from The Ohio State University in 2013 and her MS Degree from The Ohio State University in 2011. She received her BS and MS in Mechanical Engineering from ETH Zurich, Switzerland in 2007 and 2010, respectively. She is currently a tenure-track Assistant Professor in the Department of Mechanical and Aerospace Engineering (MAE) at The Ohio State University, and Affiliated Faculty with both, the Center for Automotive Research (CAR) and Sustainability Institute (SI).  Before joining the MAE Department at OSU in 2019, she was an Assistant Professor in the Department of Mechanical Engineering at Penn State University from 2016 to 2019.

Dr. Stockar conducts research is in the areas on constrained optimal control for multi-scale, large-scale, and distributed parameter systems, playing a pivotal role in energy distribution and conversion processes in transportation and buildings. Her research approach is profoundly interdisciplinary, drawing from the theory of system dynamics and control, data-driven and artificial intelligence method, and intersecting with thermal and fluid sciences. Stockar’s work has been funded by Ford Motor Company, Fiat Chrysler Automobiles, the National Science Foundation, the US Department of Energy and ARPA-E. Dr. Stockar is a 2021 NSF CAREER Award recipient, she earned the SAE Ralph Teetor Educational Award (2021), and the Ralph E. Powe Junior Faculty Enhancement Award in Engineering and Applied Science (2020). 

Abstract: Micron-scale on-chip cooling, heat management, and precise temperature control are crucial as we continue to advance toward nanoscale transistors, compact electronics, wearables, and portable thermostats. Mechanical refrigerators with moving parts are unsuitable because they cannot be scaled down. Instead, solid-state solutions that rely on passive and active heat transfer, and which have no moving parts, are needed. In this talk, I will review our work on designing solid-state coolers and thermal switches, with a focus on controlling heat using electric and magnetic fields. In the second part of the talk, I will discuss the material design criteria for solid-state thermal devices. Finally, I will present some of our recent material characterizations and electron-phonon transport measurements, emphasizing 2D layered van der Waals materials and their relevance to heat management.

Bio: Mona Zebarjadi is an Associate Professor of Electrical and Computer Engineering and Materials Science and Engineering Departments at the University of Virginia, where she is leading the Energy Science, Nanotechnology, and Imagination Lab (ESNAIL). Prior to her current appointment, she was an assistant professor in the Mechanical Engineering Department at Rutgers University. Her research interests are in electron and phonon transport modeling; materials and device design, fabrication, and characterization; with emphasis on energy conversion systems such as thermoelectric, thermionic, and thermomagnetic power generators, and heat management in high-power electronics and optoelectronic devices. She received her bachelor’s and master's degrees in physics from Sharif University and her Ph.D. in EE from UCSC in 2009, after which she spent 3 years at MIT as a postdoctoral fellow working jointly with electrical and mechanical engineering departments. She is the recipient of the 2017 NSF Career Award, 2014 AFOSR Young Investigator Award, 2015 A.W. Tyson Assistant Professorship Award, MRS Graduate Student Gold Medal, and SWE Electronics for imaging scholarship. She co-authored more than 100 publications (book chapters and peer-reviewed journal papers) and has delivered more than 70 invited talks.

Abstract: A Manufacturing Industrial Internet (MII) connects manufacturing equipment and systems via various sensors, actuators, and computing units. With the data collected and processed automatically in MII, Artificial Intelligence (AI) greatly advances data-driven decision making to provide critical computation services. However, the data with poor quality will significantly affect the performance of AI models at its training and deployment phases.  Existing research efforts focusing on bigger datasets with more complex AI models may result in unsustainable AI learning performance and unexplainable behaviors. In this talk, I will introduce my research and the future plan from the following aspects: (1) Data Quality Assurance for AI modeling in MII. The high-speed, large-volume data streams pose significant challenges to the online deployment of AI models. Existing active learning criteria fail to adapt different online annotation scenarios. I will introduce the proposed Ensemble Active Learning by Contextual Bandits (CBEAL) framework that guides the human experts to selectively annotate streaming-in samples to reduce human labelling efforts and enable continuous improvement of AI models, with theoretical justification to quantify the exploration and exploitation of the input variable space. Its application and follow-up work in advanced manufacturing systems (i.e., quality modeling in semiconducting, additive manufacturing) will be discussed. (2) Interpretable Neural Network (INN) for AI Incubation. We propose to encode human knowledge in a network architecture to enhance human-AI mutual transparency optimize the suboptimal knowledge in a data-driven manner. The application in the quality modeling of a semiconductor manufacturing process will also be discussed.  (3) Data valuation and Sharing for Scaling Trustworthy and Interpretable AI. Understanding the value of a dataset is critical to enable the exchange and effective usage of data for the efficient development of AI models across connected manufacturing systems. Future research direction on data valuation and data exchange in MII will be briefly introduced. 

Bio: Yingyan Zeng is an assistant professor in the Department of Mechanical and Materials Engineering at the University of Cincinnati. She received the Ph.D. degree in the Grado Department of Industrial and Systems Engineering at Virginia Tech and the M.S. degree in Computer Engineering at Virginis Tech. She received her B.S. in Industrial and Systems Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2019. Her research interest lies in the interface between data quality and machine learning in MII, with an emphasis on the quality-assured multimodality data generation, informative data acquisition, integrated Design of Experiments and observational data collection, privacy-preserving data valuation and exchange methods to enhance the AI modeling performance in advanced manufacturing systems. She also works on Infrastructure design and performance modeling to enhance the resilience of computation services in Cyber-Physical Systems. She was the finalist of the QSR Best Paper Competition in INFROMS Annual Meeting. Her research outcomes have been published in leading journals, including ACM Transactions, Biosensors and Bioelectronics, and top machine learning conferences.

Information to come. 

Past Speakers

Headshot of Wei Chen

Abstract

Designing advanced material systems poses challenges in integrating knowledge and representation from multiple disciplines and domains such as materials, manufacturing, structural mechanics, and design optimization. Data-driven machine learning and computational design methods provide a seamless integration of predictive materials modeling, manufacturing, and design optimization, enabling the accelerated design and deployment of advanced material systems. In this talk, we will introduce stateof-the-art data-driven methods for designing heterogeneous nano- and microstructural materials and 

complex multiscale programmable metamaterial systems. We will discuss research developments in design representation, design evaluation, and design synthesis, along with novel design methods that integrate machine learning, mixed-variable Gaussian process modeling, Bayesian optimization, topology optimization, and the concept of digital twins. Furthermore, we will address the challenges and opportunities involved in designing engineered material systems with physical intelligence.

Bio

Dr. Wei Chen is the Wilson-Cook Professor in Engineering Design and Chair of Department of Mechanical Engineering at Northwestern University. Directing the Integrated DEsign Automation Laboratory, her current research involves the use of statistical inference, machine learning, and uncertainty quantification techniques for design of emerging materials systems including microstructural materials, metamaterials and programmable materials. She serves as the Design Thrust lead for the newly funded NSF Engineering Research Center (ERC) on Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (HAMMER), where she works on digital twin systems for concurrent materials and manufacturing process design. Dr. Chen is an elected member of the National Academy of Engineering (NAE). She served as the Editor-in-chief of the ASME Journal of Mechanical Design, the Chair of the ASME Design Engineering Division (DED), and the President of the International Society of Structural and Multidisciplinary Optimization (ISSMO). Dr. Chen is the recipient of the 2022 Engineering Science Medal from the Society of Engineering Science (SES), ASME Pi Tau Sigma Charles Russ Richards Memorial Award (2021), ASME Design Automation Award (2015), Intelligent Optimal Design Prize (2005), ASME Pi Tau Sigma Gold Medal achievement award (1998), and the NSF Faculty Career Award (1996). She received her Ph.D. from the Georgia Institute of Technology in 1995.

Headshot of Laura Blumenschein

Abstract

Soft robotics and material compliance have in recent years given promise to adaptable and robust artificial systems which can better interact with unpredictable environments and situations. However, examples of successful soft robot designs often remain highlyspecialized to a situation and difficult to generalize on. To build tools that allow for adaptability in the face of uncertainty, we must better understand the interplay between the material compliance, morphology, resulting behavior, and any human agents involved. In this presentation, I will talk about three projects in my research group that examine these questions. In the first, I will discuss a class of soft robot that moves through growth and show how geometric modeling can allow the existing morphology and behavior of these robots to be used for a new purpose, as a tool for localization and mapping in unknown environments. Then, I will discuss how geometric parameterization of soft actuator morphology can allow improved control of the actuator stiffness, force, and deformation. Finally, I will shift focus and highlight how we are attempting to integrate human actors into control and teaching of our systems through haptic communication of uncertainty.

Bio

Dr. Laura Blumenschein is an Assistant Professor of Mechanical Engineering at Purdue University. She received her Masters of Science in Mechanical Engineering at RiceUniversity under Marcia O’Malley and her PhD in Mechanical Engineering from Stanford in 2019 under the supervision of Professor Allison Okamura. Her research focuses on creating more robust and adaptable soft robots: this includes soft robots inspired by plants and soft haptic devices which allow for more seamless human-robot interaction. Laura has been recognized by MIT Technology Review as a 35 Under 35 Innovator and received an NSF graduate research fellowship during her graduate career. Her work on plant-inspired growing robots has been featured in The Wall Street Journal, Popular Science, Wired, and on CBS's Innovation Nation. 

Headshot of Minami Yoda

Abstract

Measurements in micro- and minichannel flows using nonintrusive optical diagnostics are often difficult due to spatial resolution limits and lack of optical access. This talk describes two superresolution imaging techniques, structured illumination (SI) and total internal reflection fluorescence (TIRF) microscopy, which both have spatial resolution below classic diffraction limits and exploit new illumination, vs. imaging, approaches. SI microscopy reconstructs a thin slice of the bulk flow illuminated over its entire volume by spatially modulated illumination from multiple images. TIRF microscopy uses evanescent-wave illumination at a refractive index (e.g. fluid-wall) interface to visualize fluorescent tracers in near-wall flows. This talk will illustrate the unique capabilities of SI and TIRF particle velocimetry. Optical diagnostics in multiphase liquid-vapor flows poses additional challenges due to the scattering and distortion of light due to the refractive-index difference between the phases. We are currently evaluating SI for visualizing flow boiling of dielectric fluids because it can reduce multiple light scattering, as demonstrated in recent structured laser illumination planar imaging (SLIPI) of sprays [DOI: 10.1007/s00348-017-2396-9]. This talk will also present visualizations of vapor bubbles in flow boiling of fluorescently dyed HFE7200 in a microgap.

Bio

Minami Yoda did her undergraduate studies at Caltech, and her graduate studies at Stanford University. Her research interests in experimental fluid mechanics and optical diagnostics currently include flow boiling, colloidal assembly, super-resolution microscopy, and the thermal-fluids performance of plasma-facing components for magnetic fusion energy. Currently Chair of Mechanical Engineering at Michigan State University, she was Ring Family Professor at Georgia Tech, a postdoctoral researcher at the Technical University of Berlin, Germany and a visiting researcher at the Delft University of Technology, the Netherlands. Dr. Yoda is former Chair of the American Physical Society (APS) Division of Fluid Dynamics and the American Nuclear Society Fusion Energy Division. She is an editor of Fluid Dynamics Research, was an Associate Editor of Experiments in Fluids, and Fellow of APS and ASME.

Headshot of Aeriel Leonard

Abstract

Microstructurally and compositionally complex alloys (MCCA) such as Nickel-Aluminum-Bronze (NAB) are important to Navy and maritime applications due to their high strength, toughness, and fatigue resistance, as well as excellent corrosion resistance. NAB’s are widely used in many naval applications including ship propellers, underwater fasteners, pumps, and valves. Traditional sand cast NAB alloys tend to have a large amount of waste material, and reduced complexity in component geometry due to the limitations of the casting processing. As a result, NAB alloys are emerging as a viable alloy for additive manufacturing (AM) and therefore provides a new space to establish fundamental relationships between AM processing, structure and properties. Of the additive processes, wire arc additive manufacturing (WAAM) is an evolving technology for fabricating large-scale, near net shape NAB components. It is understood that the high cooling rates achieved in WAAM prevent the precipitation of coarse rosette-like KI phase which usually form during the latter stages of solidification during the casting process. In this work, Dislocation-precipitate interactions of a WAAM NAB alloy subjected to uniaxial cyclic and monotonic loading were investigated. A unique multi-scale methodology involving scanning transmission electron microscopy (S/TEM), electron back scatter diffraction (EBSD), and electron channeling contrast imaging (ECCI) was designed to uncover individual interactions with several κ-phase particles. A strong accumulation of dislocations along globular κII and lamellar κIII particles were observed during loading. Shearing of small, coherent κIV particles by dislocations was also observed within the matrix. These interactions provide insights into the processing parameters needed to enhance strength and the ductility of these complex microstructures. 

Bio 

Dr. Aeriel D.M. Leonard is an Assistant Professor in the Materials Science and Engineering Department at The Ohio State University. She was awarded the National Science Foundation CAREER Award in 2023, Department of Energy Early Career Award in 2022, and the Office of Naval Research Young Investigator Award in 2021. She earned her Bachelor’s Degree in Metallurgical and Materials Engineering from the University of Alabama in 2012. In 2013, she began her PhD journey at the University of Michigan in Materials Science and Engineering where she earned her PhD in 2018. Dr. Leonard’s PhD work investigated real-time microstructural and deformation evolution in magnesium alloys using advanced characterization techniques such high energy diffraction microscopy and electron back scatter diffraction. During her time at Michigan she led and worked on many teams aimed at increasing the number of underrepresented minorities in engineering including developing and implementing a leadership camp for female engineering students in Monrovia, Liberia. Dr. Leonard was awarded an NRC Postdoctoral Fellowship at the US Naval Research Laboratory in Washington DC where she worked for two years. During this time, she used advanced characterization techniques such as x-ray computed tomography and high energy diffraction microscopy to understand damage and texture evolution during in-situ loading in additive manufactured materials. She also runs a lifestyle blog titled AerielViews aimed at young graduate and professional students.

Headshot of Giorgio Rizzoni

Abstract

Mobility is undergoing dramatic transformations that will radically change the way we move and access work and leisure time. This presentation focuses on how increasingly connected and automated vehicles can achieve unprecedented fuel economy gains. The presentation is based on the results of the first phase of the ARPA-E NEXTCAR project, and on partial results of the second phase, and introduces a hierarchical control approach that exploits vehicle connectivity and automated driving capabilities to enhance the fuel economy capability of light-duty passenger vehicles. The use of cloud-based route optimization, coupled with adaptation to local traffic conditions via machine learning algorithms, and with the use of increasing levels of automation to shape the expected short-term vehicle load, permits the optimization of powertrain and vehicle longitudinal velocity control achieve near-optimal fuel economy thanks to the ability to predict the near-term future. The ability to realize such capabilities in production vehicles, demonstrated in this project, is around the corner, and will play a key role in shaping the future of personal and commercial mobility. In the first phase of the project, we demonstrated achievements based on CAV technologies applied to a production mild HEV capable of SAE Level 1 automation; in the second phase, these methods are extended to a SAE Level 4 PHEV. The results show that by leveraging different degrees of automation as well as vehicle-to-vehicle and vehicle-to infrastructure communications it is possible to significantly improve the energy efficiency of vehicles in realistic driving settings.

Bio

Giorgio Rizzoni, Ford Motor Company Chair in Electromechanical Systems and Professor of MAE and ECE, joined The Ohio State University in 1990. He has served in his current role as Director of the Center for Automotive Research, an interdisciplinary university research center in OSU’s College of Engineering, since 1999. Rizzoni’s research activities are related to modeling, control and diagnosis of advanced propulsion systems, vehicle fault diagnosis and prognosis, electrified powertrains and energy storage systems, vehicle safety and intelligence, and sustainable mobility. He has contributed to the development of graduate curricula in these areas and advised more than 50 PhD and 110 MS students in his career. He was recognized by the MAE Department with the Distinguished Graduate Faculty award in April of 2016. Dr. Rizzoni has led inter alia three Department of Education (DOE) Graduate Automotive Technology Education Centers of Excellence, the participation of Ohio State in two DOE US-China Clean Energy Research Centers and multiple Ohio Third Frontier programs. He is currently leading the ARPA-E NEXTCAR program, with the aim of advancing energy efficiency in connected and automated vehicles, and OSU’s Advanced Air Mobility efforts. During his career at Ohio State, Prof. Rizzoni has directed externally sponsored research projects funded by major government agencies and by the automotive industry in approximately equal proportion. Professor Rizzoni is a Fellow of IEEE (2004), SAE (2005) and ASME (2022), and a recipient of the 1991 NSF Presidential Young Investigator Award, in addition to many other technical and teaching awards. He received his BS, MS and PhD in Electrical and Computer Engineering from the University of Michigan.

Headshot of Karma Sawyer

Abstract

Electrification of space and water heating is now widely discussed in the clean energy community as a critical solution to decarbonizing the US building stock. There is little research available around the potential positive and negative impacts of electrification, especially on marginalized groups. On one hand, building electrification strategies provides opportunities to reduce exposure to indoor pollutants, which are often 2 to 5 times higher than typical outdoor concentrations and have disproportionate impacts on people who are often most susceptible to the adverse effects of pollution. Most heat pump demonstrations have not been done in underserved communities in which people may live in more dense conditions, changing the performance metrics needed to meet basic energy services. Many buildings, especially in underserved communities, are not insulated properly, limiting the cost and energy savings from heat pump technologies. Decarbonization through electrification is a relatively new strategy that has grown in popularity only since the recent rapid increase of solar and wind generation. Underserved communities that would benefit from building electrification may also be affected by an unreliable and expensive energy system. The current body of research on equity and environmental justice in the power grid is underdeveloped and not broadly adopted. Moreover, the voices of minority, lowincome, and protected populations are often not present in conversations regarding grid planning and resource allocation. This presentation will describe some of the gaps in research and recommendation for analyses and case studies that will be necessary for building electrification to be an impactful decarbonization tool for everyone.

Bio

Dr. Karma Sawyer is the Director of the Electricity Infrastructure and Buildings (EI&B) Division at Pacific Northwest National Laboratory (PNNL). She is responsible for vision and strategy to tackle the nation’s most important energy efficiency, clean energy, and electricity infrastructure challenges. The Division consists of 400+ staff members in six technical groups in electrical, mechanical, and systems engineering, data and computer sciences, cybersecurity, policy and economics by building diverse, multidisciplinary teams to provide innovative and actionable solutions to Department of Energy and Department of Defense clients. Prior to joining PNNL, Dr. Sawyer served as the Program Manager for Emerging Technologies in the U.S. Department of Energy’s (DOE) Building Technologies Office. Under her leadership, the Program’s activities are projected to avoid an estimated 315 metric tonnes of CO2 emissions and cut building energy costs by some $94 billion through 2035. From 2010-2013, she served as Assistant Program Director and Fellow at the Advanced Research Projects AgencyEnergy (ARPA-E), focusing on carbon capture and thermal storage technologies. Dr. Sawyer earned a Ph.D. in Chemistry from the University of California, Berkeley in 2008 and a B.S. in Chemistry from Syracuse University. She is a member of the 2024-26 New Voices cohort at the National Academies of Sciences, Engineering, and Medicine and was named a Distinguished Gilbreth Speaker by the National Academy of Engineering in 2023. She lives in Washington DC with her husband and two children and is a proud advocate for disability rights.

Headshot of Chirag Kharangate

Abstract

Developments in many modern applications are encountering rapid escalation in heat dissipation, coupled with a need to decrease the size of thermal management hardware. These developments have spurred unprecedented interest in replacing single-phase hardware with other more efficient configurations including two-phase boiling and condensation counterparts. However, a lack of a very clear understanding of the physical mechanisms impacting performance parameters in two-phase configurations limits their widespread implementation. In today’s talk, we will showcase fundamental research being conducted to gain clarity on thermal transport in flow boiling and flow condensation configurations. For both flow boiling and flow condensation, a combination of experimental, theoretical, computational, and data sciences driven approaches will be covered. In the experimental parts of the study, high-speed video imaging is used to identify dominant interfacial characteristics during phase-change. In the theoretical part, a new physics-based model is developed to predict critical heat flux (CHF), an important design parameter in flow boiling systems. In the computational parts of the study, a CFD model is constructed for annular flow condensation in the vertical upward flow configuration of a heat exchanger. In the data sciences parts, machine learning vision to capture statistical information during flow boiling and regression-based machine learning modeling to predict flow condensation heat transfer will be covered. With an added discussion on applications of two-phase configurations to devices and systems, this research effort aims to increase the implementation of boiling and condensation across systems and devices to meet their future heat dissipation needs.

Bio

Chirag Kharangate is leading the Two-Phase Flow and Thermal Management Laboratory in Mechanical and Aerospace Engineering at Case Western Reserve University (CWRU). Dr. Kharangate received his Ph.D. in Mechanical Engineering from Purdue University in 2016 and has multiple years of research and industry experience working on projects dealing with thermal management technologies utilizing single-phase and two-phase flows for automotive, computer, and aerospace applications. As a postdoctoral scholar in the Nanoheat Laboratory at Stanford University, he worked on the design and optimization of two-phase embedded microchannel cooling in Si and SiC substrates. At CWRU, Dr. Kharangate is the recipient of the Case School of Engineering Research Award, ASME K-16 Outstanding Early Faculty Career in Thermal Management Award, and the Office of Naval Research Young Investigator Program Award. He has extensive expertise in testing and modeling flow boiling, flow condensation, and evaporation phase change schemes. He complements his experimental and theoretical work with the development of computational fluid dynamics (CFD) as well as novel machine learning tools for predicting phase change phenomena.

Led by Dr. Eric Nauman, a professor of biomedical engineering in UC’s College of Engineering and Applied Science, University of Cincinnati engineers put popular football helmets made by leading brands through impact testing and found that no single design demonstrated superior reduction of potential concussion incidence or consistent energy absorption at every part of the helmet. Research assistants: Sean Bucherl (ball cap), Christopher Boles (UC sweatshirt), and Shengming Hu (UC polo).

Abstract

The central premise of the HIRRT lab is that understanding the mechanism of tissue damage – whether it results from blast waves, blunt force trauma, non-contact sports injuries, or more long-term degeneration such as tumor invasion or glaucomatous progression – will foster the development of effective interventions. Our collaborative methods focus on modeling the injury while simultaneously designing methods for its prevention and treatment. There are three foundational concepts that provide an underpinning for this work: (i) continuum mixture theory, (ii) sensitivity analysis, and (iii) the integration of biomedical imaging with computational methods to create subject-specific models. The goal of this talk is to describe the outcomes of our work in traumatic brain injury and to discuss the future of Mechanical Engineering from an educational and research perspective. 

Bio

Dr. Nauman received his bachelor’s degree from the University of Delaware and his master’s and Ph.D. from the University of California, Berkeley. He is a professor of Biomedical Engineering and Mechanical Engineering at the University of Cincinnati. For almost 20 years, he has studied the effects of trauma on the central nervous system. Early work in his laboratory examined the effects of impacts on spinal cord physiology and function in conjunction with Purdue’s Center for Paralysis Research. The relatively simple geometry of the spinal cord white matter was a useful foundation for the development of multiscale models linking cellular and tissue level damage. He then developed a long-lasting collaboration with Professors Talavage and Leverenz, extending these investigations to include traumatic brain injury. This work led to the discovery that a substantial portion of football and soccer players exhibit neurophysiological trauma without readily identifiable symptoms. More recently, they established that damage accumulates due to repeated head impacts and the amount of damage is related to the number, location, and magnitude of those blows. Consequently, protecting athletes from head injury requires better protective, diagnostic, and therapeutic interventions, all areas where Dr. Nauman and his collaborators are currently developing new technologies.

Headshot of Michael Sumption

Abstract

High Performance APC Nb3Sn conductors can be made using an internal oxidation process to create ZrO2 or HfO2 nanoparticles during reactive-diffusional growth of Nb3Sn (A15). These nanoparticles both refine A15 grains (increasing GB flux pinning) and directly act as flux pinning centers themselves. The relative solubility of, e.g., Zr and oxygen in the A15 and Nb-alloy drives nucleation at the moving Nb-A15 boundary leading to a distribution of nano-oxides in the final A15. SEM, TEM, and Atom probe results are shown. We present simple nucleation and growth analysis which is consistent with the observed trends in nanoparticle size with Nb-alloying element and the trend in particle size as we move through the A15 layer. This latter effect is seen to be due to the growth during the heat treatment time via diffusion of the solutes through the Nb3Sn. The size and size distribution of the nanoparticles has been characterized through image analysis of TEM, and a phase field model has been created which uses known thermodynamic and kinetic properties of the component materials to model the nucleation and growth process. Transport results are presented for ZrO2 and HfO2 APC variants, and the pinning is analyzed in terms of the size and density of the particles (and GB density). The change in pinning is observed as the mean particle size changes from much larger than the fluxon size to below it, and correlated to a modified model of pinning.

Bio

Mike Sumption is a Professor in the Materials Science Department at the Ohio State University. His group studies superconducting, electronic, and magnetic materials, looking at both fundamentals and applications. Research interests include: (i) Nb3Sn A15 materials; Phase formation, APC growth and properties, (ii) ReBCO coated conductors and cables, current sharing and quench, (iii) AC loss in superconducting composites, (iv) MgB2 upper critical fields and transport properties, (v) CNT metal and CNT polymer composites, (vi) hyperconductors, (vii) high power density propulsion motors and cables, (viii) energy related electronic materials and applications, (ix) system propulsion and thermal trades in aircraft, (x) Magnet development for particle steering (particle accelerators, medical accelerators, wigglers and undulators), and (xi) MRI systems based on MgB2 and Nb3Sn magnets. The work is funded by DOE, NASA, ARPA-E, and NIH.

Headshot of Xingbo Liu

Abstract

Solid Oxide Cells (SOCs) are promising electrochemical devices in clean energy conversion and storage. Key to improve the efficiency and reaction kinetics of SoCs is to control the defect chemistry in mixed ionic-electronic conducting (MIEC) electrodes. We developed a multi-domain physical model incorporating multi-step charge transfer to examine the competitive behaviors between the paralleled triple phase boundary (3PB) and two-phase boundary (2PB) kinetic pathways. Analyses identified the limitation of surface oxygen ion diffusion as the mechanism for 3PB-to-2PB transition. The model also proved surface reactions are driven predominantly by electrochemical forces at the 3PB, while being controlled by oxygen vacancy concentration variation at regions away from 3PB. The multi-physics model is simultaneously calibrated with experimental polarization curves and impedance behavior for various air/fuel supply conditions. The calibrated simulations are utilized to simulate a 2D half-cell constructed with measured microstructural data and random heterogeneity. The long-term performance degradation of the half-cell is predicted by the calibrated multi-physics model coupled with structural coarsening trends simulated using a phase field-based coarsening model. Degradation of both polarization curves and impedance behavior is investigated. Thorough analyses, including changes of contributions from different pathways, the resistance components, and overall reaction order, are performed to provide more insights into cathode performance degradation due to grain coarsening phenomena.

Bio

Dr. Xingbo Liu received his Ph.D. in Materials Science from University of Science and Technology Beijing in 1999, and he subsequently went to West Virginia University as a postdoc. Currently, he is the Associate Dean of Research and Statler Endowed Chair Professor in Statler College of Engineering and Mineral Resources at WVU. Dr, Liu’s research program on materials for next generation energy conversion and storage, with the focus on high temperature materials such as solid oxide fuel cells & high temperature Ni-Superalloys. Dr. Liu has received numerous awards, including Hydrogen Technology Award by U.S. DOE (2023), TMS Brimacombe Medal (2016), State of West Virginia Innovator of the Year (2013), R&D 100 Award (2011), TMS Early Career Faculty Fellow Award (2010), WVU CEMR Researcher of the Year, Outstanding Researcher Awards, and several others. He is the Fellow of ASM International and American Ceramics Society.

Headshot of Robert Gao

Abstract

As the fundamental building blocks of Industry 4.0, sensing and artificial intelligence play a critical role in advancing the science base for manufacturing. The ability in acquiring data in-situ and extracting clues from the data to guide the action of assistive infrastructure such as robots is essential to enhancing process control and production planning. This seminar highlights research on the design, modeling, and experimental evaluation of miniaturized sensors for manufacturing process monitoring, using a multiphysics-sensor with acoustic wireless data transmission as an example. The sensor enables the online quantification of four process parameters within a plastic injection mold, by using only one sensor package. The second part of the seminar illustrates AI/ML-enable robot learning of parts and human motion trajectories for coordinated, safe human-robot collaboration in assembly. The seminar illustrates the potential of convergent research that integrates physical science with data science to push the envelope of smart manufacturing, with potential impacts on data collection and visualization across supply chains, predictive maintenance, digital performance management, and intelligent process planning.

Bio

Robert Gao is the Cady Staley Professor of Engineering and Department Chair of Mechanical and Aerospace Engineering at Case Western Reserve University in Cleveland, Ohio. Since receiving his Ph.D. from the Technical University of Berlin, Germany in 1991, he has been working on physics-based signal transduction mechanisms, multi-resolution signal processing, stochastic modeling, and AI/machine learning for improving the observability of cyber-physical systems such as manufacturing processes, with the goal to improve process and product quality control. The outcome of his research has been reflected in more than 400 refereed technical papers, including 200 journal articles, three books, and 13 patents. Professor Gao is a Fellow of the ASME, SME, IEEE, and CIRP, and a Distinguished Fellow of the International Institute of Acoustics and Vibration (IIAV). He has received several professional awards, including the ASME Milton C. Shaw Manufacturing Research Medal (2023), ASME Blackall Machine Tool and Gage Award (2018), SME Eli Whitney Productivity Award (2019), IEEE Instrumentation and Measurement Society Technical Award (2013), IEEE Best Application in Instrumental and Measurement Award (2019), Hideo Hanafusa Outstanding Investigator Award (2018), and several Best Paper awards. He serves as the Chair of the Scientific Committee of the North American Manufacturing Research Institute of the Society of Manufacturing Engineers (NAMRI/SME) and Chair of the Collaborative Working Group on AI in Manufacturing (CWG-AI) of CIRP. He also served as an Associate Editor for several journals, and is currently a Senior Editor for the IEEE/ASME Transactions on Mechatronics.

Headshot of Farhang Pourboghrat

Abstract

Manufacturing processes, such as stamping, forging, and rolling rely heavily upon process simulation and in-situ correction to produce high volume of complex parts. Incremental sheet forming and polymer additive manufacturing (AM) processes are used to make small volume of specialty products. AM processes rely heavily on CAD models for process design and closed loop control for production quality. In comparison, a hybrid autonomous manufacturing system designed for on-site manufacturing of custom parts will require considerable automation based on incremental deformation, utilization of sensors, feedback control, and integration of highfidelity forming and material modeling tools with ML, DL, and AI, to optimize forming processes, and production paths. To the best of our knowledge, a fully autonomous manufacturing system does not currently exist. However, the author and several co-investigators from multiple universities and national labs are working on projects with the goal to develop the framework for hybrid autonomous manufacturing systems for metals and polymer composites. In this seminar, I will present some preliminary results from physics-based models developed to predict the microstructural evolution of metals deformed in thermo-mechanical manufacturing processes. The results from these models are then used to develop surrogate deep learning (DL) models to predict the evolution of mechanical properties of materials accurately and efficiently.

Bio

BSME and MSME - University of Iowa (1981 and 1983, respectively)

PhD in Mechanical Engineering - University of Minnesota (1990). 

1990 to 1998 - Staff Scientist at the Alcoa Technical Center. 

1998 to 2015 - Faculty in the ME Department at Michigan State University. 

January to June 2005 – sabbatical leave at Rice University.

2015 to 2017 - Professor in the Integrated Systems Engineering Department at The 

Ohio State University. Also holds a joint appointment in MAE Department at OSU. 

2017 to present - Professor and Chair of the Integrated Systems Engineering at OSU.

Research Interests - Multiscale characterization of materials and microstructure-based modeling of forming processes, including additive manufacturing, sheet metal forming, tube hydroforming, incremental sheet forming, and thermoforming of fiber-reinforced thermoplastic composites. Currently, applying machine learning (ML) to corollate microstructural properties of metals and fiber-reinforced polymer composites with their anisotropic properties.

Headshot of Ming Tang

Abstract

This seminar will show case some research projects conducted by the Extend Reality lab (XR-Lab) at College of DAAP and UC Digital Future. The topics include (1) Implementation of digital technology to create a digital twin (DT) in a smart environment system. (2) Implementation of sensing technology to fuse the DT with Extended Reality (XR). (3) Using sensing technology to evaluate human performance in XR by capturing motion and biometrics.

Bio

Ming Tang, Registered Architect, RA, NCARB, LEED AP (BD+C), is a Professor at the School of Architecture and Interior Design, College of Design, Architecture, Art, and Planning, University of Cincinnati. He is the Director of the UC Extended Reality Lab located at the UC Digital Future. He is also the founding partner of TYA Design and served on the committee of the UC Institute for Research in Sensing (IRiS). His research includes Virtual Reality & Augmented Reality, Digital Twins, Game-based learning, Computational design, Digital fabrication, Generative and performance-driven design, AI, Human behavior analysis and simulation. His recent projects focus on using VR and gaming technology for job training, health education, and public safety funded by the Office of Criminal Justice Service, Ohio Dept. of Transportation, Cincinnati Insurance Company, and Council on Aging.

Headshot of Michael Susner

Abstract

Correlated two-dimensional (2D) materials offer a new avenue for the development of next-generation electronic devices. Since the discovery of Dirac physics in graphene, research in 2D materials has grown exponentially with two main aims: 1) the discovery of new (and preferably functional) 2D materials, 2) developing new and innovative techniques to harness and tune their optical, magnetic, and electronic properties. Though most research on 2D materials has focused on graphene, boron nitride, and transition metal chalcogenides (TMCs), new 2D materials classes are coming into the forefront, including metal thiophosphates which, in many ways, are the 2D equivalent of complex oxides as changes in composition, stacking, or pressure in turn lead to large changes in bandgap, magnetic ordering temperature and type, ferroelectric ordering temperature, quadruple potential wells for neuromorphic computing and even the appearance of superconductivity. I shall present the materials characterization of CuInP2S6 and related self-assembled CuInP2S6/In4/3P2S6 heterostructures as a case study for this materials class in particular and 2D materials in general to show how the underlying physics is affected by chemical and structural modifications. I will also discuss recent efforts in materials characterization where our team determined that the heterostructured phase evinces a tunable quadruple potential well for the ferroelectric phase which has possible implications as a route information processing and storage. Finally, I will discuss recent experimental efforts on magnetic MTPs and their rich physics which offer an opportunity for potential for possible terahertz optoelectronic devices.

Bio

Michael A. Susner earned his B.S. in Chemistry (2005) from Michigan State University and his M.S. (2009) and PhD. (2012) in Materials Science and Engineering from The Ohio State University. From 2014-2016 he was a Postdoctoral Research Fellow in the Correlated Electron Materials Research Group at Oak Ridge National Laboratory. He joined the Air Force Research Laboratory in 2017 as a NRC Fellow and worked in the Soft Matter Materials Branch in the Materials and Manufacturing Directorate as a UES Research Scientist from 2019 to 2020. He became a staff scientist for AFRL in the Photonic Materials Branch in 2020 in order to establish a crystal growth center at AFRL. He is interested in establishing structure-property correlations in functional materials, i.e. those evidencing magnetic, ferroelectric, and superconducting behaviors. His current research focuses on the development of materials for second harmonic generation for laser conversion and for quantum information materials. 

Headshot of Raj Singh

Abstract

Diamond is a fascinating material due to its wide band gap, optical transparency, and high thermal conductivity rendering it an ideal wide band gap semiconductor for quantum electronics and optical devices useful under ambient and extreme conditions of high temperatures and intense radiation. Specifically, diamond has nitrogenvacancy (N-V) defect centers with unusual characteristics making it attractive for these unique applications. One can control by microwave, optical signal, electric and magnetic fields the qubit states in N-V centers for quantum network, quantum memory and quantum sensing. Some of these applications require small nanometer or micrometer size diamond crystals containing preferably only one type of N-V defects for greatest sensitivity, individual addressability, and applicability. We are addressing these challenges by developing processing approaches to synthesize diamond single crystal arrays containing only one type of N-V defect centers by microwave plasma enhanced chemical vapor deposition. These results along with a brief overview on the promise of diamond for quantum applications and our current research activities will be presented and discussed.

Bio

Dr. Raj N. Singh is a Regents Professor and served as a founding Head of School of Materials Science and Engineering, Williams Companies Distinguished Chair Professor, Director Energy Technologies Programs at the Oklahoma State University (OSU). He received his Sc.D. degree from Massachusetts Institute of Technology and B.S. from IIT Kanpur in Materials Science and Engineering. He worked at Argonne National Laboratory, GE-R&D Center and University of Cincinnati before joining OSU in 2012. Dr. Singh has been recognized for his engineering leadership through his scholarly activities (260 journal articles, 95 referred reports, and 270 presentations), pioneering inventions of MI composite processing technology leading to commercialization (27 granted patents), for graduating 36 students with MS and PhD degrees and through numerous professional awards in recognition of his engineering leadership such as Rishi Raj Medal for Innovation and Commercialization from American Ceramic Society (2023), National Academy of Inventors Fellow (2015); Albert Sauveur Achievement Award of ASM International (2016); Regents Professor (OSU 2015); Fellow of the ASM International (1996); Fellow of the American Ceramic Society (1992); Fellow of Graduate School (UC 2007); Whitney Gallery of Technical Achievers GECR&D (1990); Publication Awards GE-CR&D (1984, 1988); Patent Awards GE-CR&D: Bronze, Silver, and Gold Patent Medallions (1983, 1987, 1988). He also serves as member of editorial boards of 5 international journals.

Headshot of Yuehwern Yih

Abstract

Technologies and innovations are advancing rapidly to meet human needs. However, in complex environments, such as healthcare and humanitarian aids, the interactions between human, technologies, and innovations could present unintended consequences that lead to suboptimal outcomes. In this lecture, Dr. Yih will use examples from her research in healthcare and humanitarian relief operations to illustrate the gaps between (1) the “work imagined” embedded in the original design (intended use) of technologies and innovations and (2) the “work done” in the field, especially when there are multiple stakeholders/users, each holds different role,responsibilities, and priorities. For example, in a study on the therapeutic drug management (TDM) of a popular antibiotic, Vancomycin, for pediatric patients, we illuminate the complexity of TDM processes where nurses, physicians, pharmacists, and laboratory technicians interact with “smart” device, information system, and population-based model to manage its dosing. The misalignment of IT design and user’s workflow may create discrepancies that impact patient outcomes. Similarly, in the case of managing supply chains for emergency response or in a low-resource setting, the use of IT applications without comprehensive considerations limits the data quality and its usefulness for effective decision making (by human or AI).

Bio

Dr. Yuehwern Yih is Professor of Industrial Engineering and currently serves as the Director of LASER PULSE ($70 million 10-year program funded by US Agency for International Development (USAID)). Prior to LASER PULSE, she served as the Associate Director of the Regenstrief Center for Healthcare Engineering. Dr. Yih’s core research focuses on understanding system dynamics and improving the outcomes of complex systems under volatile environments including manufacturing systems, supply network, humanitarian assistance, health care delivery, and international development. In addition to her scholarly achievements, Dr. Yih is recognized by her translational research, receiving the highest honor at Purdue, the inaugural Faculty Engagement Fellow, the Most Impactful Faculty Inventors, and the Outstanding Leadership in Globalization Award. Dr. Yih also received the National Science Foundation Young Investigator Award, the Dell K. Allen Outstanding Young Manufacturing Engineer Award, the Melinda and Bill Gates Grand Challenge Award, multiple Best Paper awards, and multiple Outstanding Teaching Awards. Dr. Yih earned her Ph.D. degree from the University of WisconsinMadison. She is a GE Faculty Fellow, NEC Faculty Fellow, Institute of Industrial and Systems Engineers (IISE) Fellow, and Executive Leadership in Academic Technology and Engineering (ELATE) Fellow.

Headshot of Dieter Vanderelst

Abstract

Popular and scientific literature often refer to bat echolocation as `seeing with sound.' Bats are assumed to infer objects' 3D position and identity from the echoes they receive. This view originates from experimental findings showing that bats can accurately locate single targets. However, unlike the artificial targets used in experiments, most natural objects, including vegetation and human-made objects, typically return many overlapping echoes. Theoretical limitations and recent evidence suggest that it is highly questionable whether bats can interpret echoes from these complex natural objects in terms of a 3D model or acoustic image. In our lab, with Occam's razor in mind, we take a bottom-up approach to construct simple models for the extraordinary capabilities of echolocating bats to navigate and forage in complete darkness. We use simulations and robots to model bats' behavior, seeking robust acoustic cues and sensorimotor loops supporting foraging and navigation tasks. This talk will give an overview of our recent work on modeling prey capture, navigation, and foraging in nectarivorous bats.

Bio

Dr. Vanderelst obtained an MSc in Theoretical Psychology (Ghent University, Belgium, 2005) and an MSc in Artificial Intelligence (Leuven University, Belgium, 2006). He received his Ph.D. in 2012 (University Antwerp, Belgium) in Biology. Before joining UC in 2016, he worked as a Marie-Curie Fellow at the University of Bristol and a postdoctoral fellow at the Bristol Robotics Lab. He is generally interested in bio-inspired artificial intelligence and models of cognitive functions in humans and animals. In particular, he models echolocation-based navigation, flight control & foraging in bats. In his research, he uses simulation methods, artificial sonar systems, and robots to study the sensorimotor loops underlying bat biosonar.

Headshot of Soumya Nag

Abstract

Additive manufacturing (AM) provides a tremendous opportunity to synergistically couple materials, design, and manufacturing strategies. This seminar would focus on the fundamentals, current state of art and future of one such AM modality – the blown powder Directed Energy Deposition (DED) technique. Much of the content would be a scientific deep dive on novel alloy development strategies using the unique attributes of DED-driven manufacturing. Specifically, examples for development of Titanium alloys as well as high temperature Ni and Nb alloys would be discussed. The first example employs build parameter DOEs of Ti64 alloy to determine defect density and microstructural descriptors, which in turn may be mapped with the material property values. Armed with this information, physics based predictive models were generated to develop response surfaces. The next topic explores phase transformations and deformation mechanisms in additively manufactured Ni-base superalloys (IN718/IN625) graded to Nb-based refractory alloys (C103). Insights would be provided on connectingin-situ sensor and modeling tools to understand the phase/stress evolution of additive builds in a spatio-temporal manner. Some of the concepts would be material-agnostic and can be beneficial for fabricating next generation components for a wide range of applications in aerospace, marine, and energy sectors.

Bio

Soumya Nag is a Senior R&D Staff Scientist at Oak Ridge National Laboratory. He is also an adjunct faculty at Clarkson University, Capital Region Campus and has a joint faculty appointment with University of Tennessee at Knoxville. His background is in phase transformation, physical metallurgy and nanoscale characterization of metallic and hybrid materials. His research interest is understanding processing (additive and conventional) - structure (phase transformation across different length and time scales)- property (mechanical and environmental property) relationships of light weight and high temperature structural alloys. Currently he has more than 70 peer reviewed publications and is a key reader/reviewer of various technical journals. He has given more than 100 technical presentations in national and international conferences. He also has a Six Sigma Green Belt (DFSS) Certification.

Headshot of Aja Bettencourt-McCarthy

Abstract

The transition to graduate studies at the University of Cincinnati presents new opportunities for scholarship, research, and teaching and the UC Libraries is here to support you. This talk will provide an overview of the library resources available to you at UC as well as tips for approaching research projects, managing citations, and exploring publishing options.

Bio

Aja Bettencourt-McCarthy is the Science & Engineering Global Services librarian at the University of Cincinnati Libraries. Based in the CEAS Library, she supports the Mechanical & Materials and Electrical & Computer Engineering departments. Prior to joining the faculty at UC, Aja was a librarian at the University of Kentucky and the Oregon Institute of Technology. Aja’s research interests include information behavior, fostering inquiry and entrepreneurial mindsets in STEM, and instruction best practices.