Topological Elastic Metamaterials: An introduction and application to the analog Quantum Spin Hall Effect

Date: April 25, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Inspired by recent discoveries of topological phases of matter in quantum physics, there has been a rapidly growing research effort to uncover analog mechanisms in classical wave physics, including acoustics and elastodynamics. By properly acting on time reversal, chiral, and particle-hole symmetries, material systems obeying the laws of classical mechanics can deliver dispersion properties reminiscent of selected quantum mechanical systems. Among the many remarkable characteristics of these materials, their ability to support unidirectional propagating waves is particularly significant and it could serve as a foundational property to achieve waveguides that are robust even in presence of disorder and defects. This talk will discuss the general concept of a topological elastic metamaterial, the foundational role played by the geometric phase, and finally present a selected example of a topological elastic metamaterial from recent works conducted in Prof. Semperlotti’s group. More specifically, a topological elastic waveguide inspired by a mechanism analog to the quantum Spin Hall effect will be discussed. A combination of theoretical, numerical, and experimental results will be presented to illustrate how unidirectional propagating guided modes can be achieved at the interface between elastic material phases having different topological order. These so-called edge states are topologically protected against backscattering, hence allowing efficient elastic energy transmission even in presence of defects and disorder. Such unique propagation properties could have a profound impact on the development of many real-world applications and on the performance of practical devices whose operating mechanism is rooted in the physics of acoustic and elastic waves.

Biographical Sketch: Dr. Fabio Semperlotti is a Professor in the School of Mechanical Engineering and the Perry Academic Excellence Scholar at Purdue University; he also holds a courtesy appointment in the School of Aeronautics and Astronautics Engineering. He directs the Structural Health Monitoring and Dynamics laboratory (SHMD) where he conducts, together with his group, research on several aspects of structures and materials design including structural dynamics and wave propagation, elastic metamaterials, structural health monitoring, and computational and experimental mechanics. His research has received financial support from a variety of sources including the National Science Foundation, the Department of Defense, the Department of Energy, and industrial sponsors. Dr. Semperlotti was the recipient of the National Science Foundation CAREER award (2015), the Air Force Office of Scientific Research Young Investigator Program (YIP) (2015), the DARPA Young Faculty Award (YFA) 2019, and the ASME C.D. Mote Jr. Early Career Award 2019. Dr. Semperlotti received a M.S. in Aerospace Engineering (2000), and a M.S. in Astronautic Engineering (2002) both from the University of Rome “La Sapienza” (Italy), and a Ph.D. in Aerospace engineering (2009) from the Pennsylvania State University (USA). In 2010, he was a postdoctoral research associate in the Mechanical Engineering department at the University of Michigan. Prior to joining Penn State, Dr. Semperlotti served as a structural engineer for a few European aerospace industries, including the French Space Agency (CNES), working on the structural design of space launch systems and satellite platforms.

PM_2.5 in Health and Atmospheric Science

Date: April 11, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Air pollution has been a problem as long as there have been cities.  Major air pollution disasters in the mid-20^th century prompted efforts to understand and control air quality.  Episodes in Europe and the eastern USA were linked to primary emissions from coal combustion and heavy industries, while those in the western USA, especially the infamous Los Angeles smog involved photochemical generation of secondary pollutants, both gases like ozone and the smog that degraded visibility as the day progressed.   A study of the smog aerosol revealed characteristics that, when combined with efforts to understand the health impacts of inhaled particles, formed the foundation for the first regulations regulate airborne particulate matter concentrations, ultimately leading to PM_2.5 as the primary particulate air quality standard.  PM_2.5, the mass concentration of particles smaller than about 2.5 µm, describes the fine particles that can penetrate to the lower airways, including the alveolar region, when inhaled.  This seminar will explore this history and examples of its impact, including the recent southern California fires.  Since PM_2.5 first appeared in the scientific literature, PM_2.5 has been referenced in more than 50,000 papers.  With this prominence, many studies limit their focus to PM_2.5, or to chemical composition, black carbon, and other properties of that size fraction.  Health studies report exacerbations of respiratory and cardiovascular health effects close to roadways, and some suggest that a common suspect, black carbon, is not the culprit, but that ultrafine particles (< 100 nm diameter) may be responsible, but the mass concentration of such particles is usually too small to be detectable within PM_2.5.  The exclusion of these small particles, and of larger ones, also means that the growing PM_2.5 bias in atmospheric particle measurements misses parts particles that govern atmospheric aerosol dynamics, confounding efforts to understand even PM_2.5.  We shall, therefore, also examine some of the limitations of PM_2.5, and highlight research opportunities that this bias creates.

Biographical Sketch: Richard Flagan is the Irma and Ross McCollum/William H Corcoran Professor of Chemical Engineering and Environmental Science and Engineering at the California Institute of Technology.  He received his BS in Mechanical Engineering from the University of Michigan, and his SM and PhD from MIT, also in Mechanical Engineering.  He joined the Environmental Engineering Science department of Caltech after a couple of years as a postdoc and Lecturer at MIT.  Although his PhD research with John Appleton in Mechanical Engineering, and postdoctoral studies were with Adel Sarofim and John Heywood in Chemical Engineering, Flagan shifted his focus to aerosols upon joining the Caltech Faculty.  Primarily an experimentalist, his contributions include numerous advances in aerosol measurements, particularly in the nanoparticle regime with the first low pressure impactor, the scanning mobility particle sizer (SMPS), and others.  He pioneered chamber studies that determined aerosol yields of a wide range of anthropogenic and biogenic hydrocarbons, and the SMPS to measure size distributions of nanoparticles in airborne measurements.   Flagan has published over 420 papers, and holds 28 patents.  Though out of print, his textbook has been downloaded over 350,000 times.  Flagan has received numerous awards, including the Fuchs Memorial Award of the International Aerosol Research Assembly, the highest award in the field of aerosol science, and the Haagen-Smit Clean Air Award from the California Air Resources Board.  He is a member of the National Academy of Engineering, and has received two honorary doctorates.  He has served as Chair of the Faculty at Caltech, and is a member of the Board of Directors of the California Council on Science and Technology.

Developing Intelligent Automation for Smart and Sustainable Manufacturing Systems

Date: April 4, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: The current manufacturing paradigm is shifting toward the development of production systems that require greater flexibility and adaptability. To achieve this objective, new system-level control strategies must be developed to control and coordinate different components on the shop floor. This talk will focus on our recent approaches to improving the flexibility and adaptability of manufacturing systems across different levels of automation. First, I will introduce some of our recent work in leveraging artificial intelligence technology to enhance automation-operator interactions on the shop floor. Then, we will generalize these results to the system level and discuss how models and controllers can be developed to improve manufacturing system cooperation, coordination, and performance. Case studies from both simulations and real-world environments will be provided to showcase the exciting possibilities for the future of manufacturing systems.

Biographical Sketch: Ilya Kovalenko is currently an Assistant Professor in the Department of Mechanical Engineering and the Department of Industrial & Manufacturing Engineering at Penn State University. He received both his PhD in Mechanical Engineering (2020) and his MS degree in Mechanical Engineering from the University of Michigan (2018), and his BS degree in Mechanical Engineering from the Georgia Institute of Technology (2015). He was awarded the NSF Graduate Research Fellowship in 2016, the University of Michigan’s College of Engineering Distinguished Leadership Award in 2020, and the NSF CAREER in 2025. His current research interests lie in the areas of control theory, artificial intelligence, and smart manufacturing, with a focus on cooperative control, cyber-physical systems, and robotics.

 

 Applicable and Generalizable Machine Learning for Intelligent Welding, from Quality Prediction to Robotic Automation

Date: March 28, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: In the last decade, the manufacturing sector has adopted Industry 4.0 innovations, including edge and cloud computing, Artificial Intelligence (AI), and Machine Learning (ML), enhancing production visibility, quality, automation, productivity, and safety. This presentation highlights novel ML applications in welding processes, through case studies in Resistance Spot Welding (RSW), laser welding, and arc welding.

The case study of RSW focuses on process sensing and modeling for quality prediction and defect detection. This study not only employs data-driven modeling but also utilizes ML to uncover physical insights into the RSW process, enhancing feature extraction and developing a more generalizable model for predicting quality and defects. It also introduces a new ML approach to create virtual signals for force and displacement using dynamic resistance measurements, addressing the lack of novel process sensing in facilities due to high costs. The case study of laser welding tackles feature engineering, i.e., from sensing data characterization to feature selection, to improve the model generalizability and decision-making efficiency in a plant production scenario. Transfer learning is also investigated to enable the ML models to adapt to dynamically changing welding conditions. The third case study targets the robotic automation of arc welding. To enable robotic operational adaptivity, a hybrid ML-based process characterization, and online adaptive control framework are developed for robotic arc welding to automatically and efficiently achieve the desired weld pool condition, given any initial conditions.  These case studies showcase significant potential for advancing welding processes to new levels of efficiency and effectiveness.

Biographical Sketch: Dr. Peng (Edward) Wang is currently an Associate Professor in the Department of Mechanical and Aerospace Engineering at Case Western Reserve University (CWRU). Dr. Wang has extensive experience in developing novel ML methodologies for machine condition monitoring and diagnosis, process modeling and quality prediction, and collaborative robots. Dr. Wang is the recipient of the CAREER award from the US National Science Foundation in 2023, Young Investigator Award from the International Symposium of Flexible Automation in 2024, Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME) in 2022, the Best Paper Award from the 2023 Manufacturing Science and Research Conference (MSEC), Outstanding Technical Paper Award from the SME North American Manufacturing Research Conference (NAMRC) in 2017, 2020, and 2021, and other best paper awards. Dr. Wang is an Associate Editor of the IEEE Sensors Journal and Journal of Intelligent Manufacturing.

Vetting Scaling Laws in Turbulent Reacting Flows: The Case of Damköhler’s Second Postulation

Date: March 14, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Damköhler’s second postulation has been the foundation of the development of scaling laws for turbulent premixed flames that led to the establishment of regime diagrams and has been used as the principal argument for explaining experimental and computed observables. Damköhler’s arguments are challenged based on direct numerical simulations of vortex-flame interactions and fully turbulent premixed flames under high Karlovitz number conditions. Specifically, the simulations could not prove that sub-flame thickness Kolmogorov eddies can enter the flame due to the high dissipation rate. Local analyses of both configurations showed that frequently used correlations based on the laminar flame structure could not be used to explain, among others, the reported thickening of turbulent flames under extreme turbulence levels. Additionally, laminar flame scales derived using detailed simulations resulted in a wide range of Karlovitz number values of the boundary separating the so-called thin reaction zone and broken reaction zone regimes and are not in agreement with established values in the literature, which have been derived from relatively simple theoretical arguments. Finally, the present results could not support even the existence of the thin reaction zone and broken reaction zone regimes, which have been hypothesized by Borghi and Peters and adopted in numerous computational and experimental studies.

Biographical Sketch: Fokion N. Egolfopoulos is a William E. Leonhard Professor of Engineering in the Department of Aerospace and Mechanical Engineering at the University of Southern California. He obtained his Diploma Degree in 1981 from the National Technical University of Athens, and his PhD in 1990 from the University of California at Davis after spending the last two years of his doctoral research at Princeton University. He is a recipient of the Silver Medal of the Combustion Institute at the Twenty-Second International Combustion Symposium, a Fellow of the Combustion Institute, a Fellow of the American Society of Mechanical Engineers (ASME), and an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA). He has authored and co-authored one hundred and fifty-six (156) archival journal publications, eleven (11) editorial comments, three (3) book chapters, one hundred and sixty-two (162) conference proceedings and reports, and has given one hundred and seventy-two (172) invited and contributed scholarly addresses. Since 2009 he has been the Editor in Chief of Combustion and Flame, after serving as an Associate Editor of the journal from 2003 until 2008.

Exploring Modularity for Advancing Space Exploration and Supporting Crew Health

Date: March 7, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: In the quest for exploring new frontiers in space, the design of modular systems has emerged as a potential solution that not only could enhance exploration capabilities but also support crew health and performance. A system is considered modular when its components are designed to function independently, each capable of performing its specific role without reliance on the entire system. At the same time, these components can seamlessly integrate to work collectively, forming a unified whole. This dual capability allows for flexibility, scalability, and adaptability, enabling the system to be customized, expanded, or reconfigured as needed to meet evolving requirements and adapt to different situations. Within a modular system, modules are designed to connect, interact, and exchange resources—either physically or virtually—through standardized interfaces or mechanisms. From modular robotic systems for exploration in unknown environments to modular systems for spacecraft habitats that can support crew health and activities.

Biographical Sketch: He is an Associate Professor in the Department of Engineering at Texas A&M University-Corpus Christi (TAMU-CC), USA. His research interests include the development and integration of Modular Robots and Modular mechatronic systems across different domains such as in Unmanned Autonomous Systems, Space, Agriculture, Industry, HealthCare, and Education. Dr. Baca has worked in the Autonomous Systems and Modular Robotics fields for over a decade and his work has led to multiple publications in leading conferences and journals, as well as organized and co-chaired international conferences and workshops. He has been involved in projects funded by federal agencies such as DoD, NSF, NASA, ED, and USDA-NIFA. He is co-founder of CORAL (Collaborative Robots and Agents Lab), and Faculty member of the NSF CREST-GEIMS (Center for Geospatial and Environmental Informatics, Modeling and Simulation) and the IUCRC (Industry-University Cooperative Research Center) Center for Growing Ocean Energy Technologies and the Blue Economy (GO Blue) at TAMU-CC.