Speakers

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.

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. 

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 

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.

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.

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.

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.

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.