https://s.uconn.edu/meseminar10/15/21

Abstract: Since Additive Manufacturing (AM) processes can fabricate complex part shapes and material compositions, it released significant amount of freedom for designers to design innovative products. In general, parts that are good candidates for AM tend to have complex geometries, low production volumes, special combinations of properties or characteristics. Most of existing design methods and approaches are well established for conventional manufacturing processes which tend to limit the complexity and potential multi-functionalities of products considerably. Given the unique characteristics of AM, Prof. Zhao and her team have proposed a new definition for the term — Design for Additive Manufacturing (also known as DfAM or DFAM) — as “a general type of design methods or tools whereby functional performance and/or other key product life-cycle considerations such as manufacturability, reliability, and cost can be optimized subjected to the capabilities of additive manufacturing technologies”. Most research in DFAM field only focuses on specific topics without considering AM process specific characteristics. AM technology connects design, material properties, process settings, end-product quality, and potential post-process operations intimately. When DFAM is applied, AM process-specific capabilities and constraints must be considered at early design stage. Thus, rooted from the proposed definition, this talk will report Prof. Zhao and her team’s recent work on developing novel design strategies and geometric modeling techniques to support multi-functional design concept generation and multi-scale highly complex CAD model realization with manufacturability analysis applied at early design stage.

Biographical Sketch: Dr. Yaoyao Fiona Zhao is an Associate Professor and William Dawson Scholar at the Department of Mechanical Engineering in McGill University, in Montreal, Canada. Since Dr. Zhao joined McGill University in 2012, she has established the Additive Design and Manufacturing Laboratory (ADML) which is one of the leading research laboratories in additive manufacturing field. Her research expertise lies in the general field of design and manufacturing including the exploration of new design methods, developing efficient numerical simulation method for additive manufacturing processes, manufacturing informatics, application of machine learning in design and manufacturing, sustainable product development and intelligent manufacturing. Her team is leading the research in Design for Additive Manufacturing with the development of new design methods to achieve multi-functionalities, less part count, better functional and sustainability performance. Her team is also leading the efforts on developing methods and guidelines for manufacturing industry to adopt machine learning and AI as an effective tool for global competition.

http://s.uconn.edu/meseminar10/8/21

Abstract: The COVID-19 pandemic has accelerated the development and manufacturing of vaccines at an unprecedented speed. This has been enabled by the emergence of biotechnology platforms such as mRNA and Viral Vectors. In this seminar, I will outline the engineering aspects of such platforms and the modern biology behind their evolution.

Biographical Sketch: Dr. Vijay Srinivasan is a Senior Advisor in the Engineering Laboratory at the National Institute of Standards and Technology, Gaithersburg, Maryland. He joined NIST in 2009, after 26 years at IBM Research during which he was also an Adjunct Professor at the Columbia University, New York. Dr. Srinivasan has published widely and is a Fellow of ASME and AAAS.

http://s.uconn.edu/meseminar10/1/21

Abstract: The development of aerospace products suffers from chronic cost and schedule overruns and derivative designs, while innovative products are needed on time and on budget.  We’ll examine the primary failure modes of conventional system-architecting practice that have led to these symptoms and how to avoid them.  We’ll consider how to answer the 5 key questions with which product-development teams struggle – how to determine the best performance achievable given a set of technologies, how to find a small diverse set of high-value architectures, how to identify the technology options that are critical for early investment before the final architecture is selected, how to set technology-development targets, and how to know what properties a new technology would need to be worth its development.  People are actually poorly suited to certain architecting tasks; we’ll describe how people’s skills tell us to which architecting tasks they’re really well suited to contribute.  Finally, we’ll describe a methodical approach to system architecture that researchers at Raytheon Technologies Research Center have been developing and successfully applying for product architecting across RTX.

Biographical Sketch: Dr. Zeidner leads the development and application of RTX’s DISCOVER ecosystem of advanced methods and tools for the conceptual design of RTX products, from the component level of product engineering, all the way up to the campaign level of multi-product operations. Dr. Zeidner has 25 years of experience in the development of system architectural-design methods and collaborative processes and tools for engineering innovation and decision-making.  His areas of expertise include design-space exploration, system modeling, risk analysis and software architecture. While at RTX, he has developed the Concept Generation and Selection (CGS) process, which has been successfully applied to over 200 projects across RTX’s business units, enabling large, non-collocated teams to brainstorm productively. He holds 4 patents and has earned fifteen internal RTRC awards for technical achievement. Prior to joining RTRC, Dr. Zeidner taught and conducted research as an Assistant Professor of Manufacturing Engineering at Boston University on the topics of design-to-manufacture and advanced software-development methods.  Dr. Zeidner obtained his PhD in Civil Engineering in 1983 from Princeton University.

http://s.uconn.edu/meseminar9/24

Password: 1234

Abstract: Lithium-ion batteries have been used extensively in the past decade in a variety of applications from portable devices to airplanes and electric vehicles. Battery packages used in electric vehicles experience dynamic loadings, shocks, and large deformations during normal operation as well as in a crash scenario. It is of paramount importance to battery manufacturers and the automotive industry to better understand how the cells deform under such loadings and what conditions might damage a cell and lead to failure. This talk will focus on the experimental methods used to characterize material properties of lithium-ion batteries under large mechanical loading. Then deployments of these material models for simulating crash response of batteries will be discussed. The models that will be discussed are capable of predicting the profile of deformation and the onset of short circuit in batteries in the cases of mechanical abusive loads.

Biographical Sketch: Elham Sahraei is an Assistant Professor and Director of Electric Vehicle Safety Lab at Temple University. She was the co-director of the MIT Battery Modeling Consortium, a multi-sponsor industrial program supported by major automotive and battery manufacturers from 2011 till 2019. Her research is focused on computational modeling of lithium-ion batteries for electric vehicles. Dr. Sahraei earned her Ph.D. from the George Washington University in 2011, and completed two years of post-doctoral training at Massachusetts Institute of Technology in 2013, where she became a Research Scientist afterwards. She is currently a principal investigator on safety of lithium-ion batteries under combined mechanical-electrical loading for Office of Naval Research. She has also been an investigator on several Ford-MIT alliance projects, and she is the inventor of “Collision Safety Structure,” a structure for controlled buckling of driver seats that reduces perils of frontal crashes.

http://s.uconn.edu/meseminar9/17/21

Abstract: Developing polymers with desirable properties has historically relied on trial-and-error, which can take long time, and there is no guarantee of success. Machine learning has become an integral part of materials design, and it can potentially impact polymer development in a positive way. However, the lack of open-source data has impeded the development of machine learning-guided polymer development, i.e., polymer informatics. In this talk, I will discuss our effort in generating polymer structures using machine learning models trained on existing polymer database. I will describe the process we used to generate the PI1M database (PI1M refers to 1 million polymers for Polymer Informatics) . I will also talk about the challenges ahead of the continued research in this direction. In addition, I will introduce our work in generating polymer properties using molecular dynamics simulations and discuss the challenges to be addressed. Machine learning models for several polymer properties (e.g., thermal properties, gas separation performance) will be presented. I will also highlight the need of a greater community effort to advance the polymer informatics field.

 

Biographical Sketch: Dr. Tengfei Luo is a Professor in the Department of Aerospace and Mechanical Engineering (AME) with a concurrent appointment in the Department of Chemical and Biomolecular Engineering (CBE) at the University of Notre Dame (UND). Before joining UND, he was a postdoctoral associate at MIT (2009-2011) after obtaining his PhD from Michigan State University (2009). Dr. Luo’s research focuses on exploring the chemistry-conformation-property relationships of polymers using molecular simulations, machine learning and experiments. He is an ASME Fellow (2019), JSPS Invitational Fellow (2019), DuPont Young Professor Awardee (2016), DARPA Young Faculty Awardee (2015), and Air Force Summer Faculty Fellow (2015). 

http://s.uconn.edu/meseminar9/10/21

Abstract: It is commonly believed that understanding, building, and controlling complex engineering systems require data-driven methods beyond first principles. Often these methods are boasted as “AI”. Yet data-driven methods are known to lack risk certification, i.e., we don’t have principled knowledge about when they will fail or how badly if they do. This challenge has created a recent surge of efforts in “baking” first principles into data-driven methods, leading to new learning theories, algorithms, and applications. In this talk, I will go through three studies across the spectrum where first principles play a significant role in mitigating the risks induced by data-driven models. The first study investigates an optimal solution generator governed by the Karush-Kuhn-Tucker conditions, with applications to accelerating topology optimization. The second discusses the approximation of open-loop solutions to the Hamilton-Jacobi-Isaacs equations for incomplete-information differential games, with applications to safer human-robot interactions. The last investigates certifiable attribution of generative models, with applications to DeepFake regulation.

 

Biographical Sketch: Dr. Yi Ren is an Assistant Professor with the Department of Aerospace and Mechanical Engineering at Arizona State University. He got his BEng in Automotive Engineering from Tsinghua University in 2007 and his PhD in Mechanical Engineering in 2012 from the University of Michigan, Ann Arbor, where he also worked as a Postdoc before moving to ASU in 2015. His research focuses on developing robust machine learning methods for risk-sensitive engineering systems, with applications to structure/materials design and autonomous driving. He has won multiple NSF grants from system engineering, materials science, robotics, and cybersecurity programs, the Amazon Machine Learning Award (2019), and the Best Paper Award at the ASME International Design and Engineering Technical Conferences (2015). He leads the planning of an ASU NSF AI institute for 4D materials design.